U.S. patent application number 15/343057 was filed with the patent office on 2017-05-04 for methods for nucleic acid size detection of repeat sequences.
This patent application is currently assigned to Asuragen, Inc.. The applicant listed for this patent is Asuragen, Inc.. Invention is credited to Eran Bram, Andrew Hadd, Blake Printy, Raghav Shroff.
Application Number | 20170121763 15/343057 |
Document ID | / |
Family ID | 58635460 |
Filed Date | 2017-05-04 |
United States Patent
Application |
20170121763 |
Kind Code |
A1 |
Bram; Eran ; et al. |
May 4, 2017 |
METHODS FOR NUCLEIC ACID SIZE DETECTION OF REPEAT SEQUENCES
Abstract
Disclosed herein are data processing and calculating annotation
systems and devices, and corresponding methods, for nucleic acid
analysis. In particular, disclosed herein are methods for sizing a
repeat region of a nucleic acid sample. For example, the methods
disclosed herein use a ladder of amplification products to
determine nucleic acid size.
Inventors: |
Bram; Eran; (Cedar Park,
TX) ; Shroff; Raghav; (Austin, TX) ; Hadd;
Andrew; (Austin, TX) ; Printy; Blake; (Austin,
TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Asuragen, Inc. |
Austin |
TX |
US |
|
|
Assignee: |
Asuragen, Inc.
Austin
TX
|
Family ID: |
58635460 |
Appl. No.: |
15/343057 |
Filed: |
November 3, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62250476 |
Nov 3, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/6858 20130101;
C12Q 2525/151 20130101; C12Q 1/6858 20130101 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Claims
1-90. (canceled)
91. A method of generating an internal sizing standard, comprising:
identifying a repeat profile in a channel of a ladder of
amplification products; iteratively generating a set of repeat peak
locations, an iteration comprising: determining a predicted peak
location in the repeat profile using a previous peak location and
an interval value, identifying a peak location within a first
window including the predicted peak location, and adding the
identified peak location to the set of repeat peak locations when a
signal intensity at the identified peak location satisfies an
amplitude criterion; and estimating a linear relationship between
repeat peak location and repeat fragment size using the set of
repeat peak locations and a set of corresponding fragment
sizes.
92. The method of claim 91, further comprising repeatedly updating
the amplitude criterion using signal intensities of one or more of
the set of repeat peak locations while iteratively generating the
set of repeat peak locations.
93. The method of claim 91, further comprising repeatedly updating
the interval value using peak locations of one or more of the set
of repeat peak locations while iteratively generating the set of
repeat peak locations.
94. The method of claim 91, further comprising initializing the
amplitude criterion using a statistical measure of a first portion
of the repeat profile before iteratively generating the set of
repeat peak locations.
95. The method of claim 91, further comprising initializing the
interval value using the periodicity of a second portion of the
repeat profile and iteratively generating the set of repeat peak
locations.
96. The method of claim 91, further comprising using an
interpolated peak location with the set of repeat peak locations
when a signal intensity at the identified peak location does not
satisfy the amplitude criterion.
97. A method of generating an external sizing standard, comprising:
identifying peaks in a channel of a ladder of amplification
products, the peaks exceeding a local noise threshold; iteratively
re-estimating, using identified peaks in a first region of the
channel, a linear relationship between peak location and fragment
size using a first set of fragment sizes and a first set of
corresponding peak locations, the iterative re-estimation
comprising: including progressively smaller fragment sizes into the
first set of fragment sizes, and including peak locations
corresponding to the progressively smaller fragment sizes into the
first set of corresponding peak locations; estimating, using
identified peaks in a second region of the channel, a non-linear
relationship between peak location and fragment size for a second
region of the channel using a second set of fragment sizes and a
second set of corresponding peak locations; generating an external
sizing standard for sizing a repeat region of a nucleic acid sample
by combining the linear relationship and the nonlinear
relationship.
98. The method of claim 97, wherein an iteration of the iterative
re-estimation further comprises: determining a predicted peak
location using one of the progressively smaller fragment sizes and
a re-estimated linear relationship between peak location and
fragment size; determining an actual peak location in the channel
within a window including the predicted peak location; and
including the actual peak location into the first set of
corresponding peak locations.
99. The method of claim 97, further comprising: generating an
internal sizing standard by identifying a repeat profile in a
channel of a ladder of amplification products, iteratively
generating a set of repeat peak locations, and estimating a linear
relationship between repeat peak location and repeat fragment size
using the set of repeat peak locations and a set of corresponding
fragment sizes; and generating a mobility corrected sizing standard
using the internal sizing standard and the external sizing
standard.
100. The method of claim 99, wherein generating a mobility
corrected sizing standard comprises: generating an affine
transformation using the internal sizing standard and the external
sizing standard; and applying the affine transformation to the
external sizing standard to obtain the mobility corrected sizing
standard.
101. A method for genotype peak sizing, comprising: generating a
dynamic threshold from a background model, wherein the generation
comprises piecewise scaling the background model to obtain the
dynamic threshold; determining gene-specific product peak locations
using the dynamic threshold; associating repeat sizes with the
gene-specific product peak locations using a sizing standard; and
outputting an indication of the gene-specific product sizes.
102. The method of claim 101, further comprising: generating the
sizing standard by identifying a repeat profile in a channel of a
ladder of amplification products, iteratively generating a set of
repeat peak locations, and estimating a linear relationship between
repeat peak location and repeat fragment size using the set of
repeat peak locations and a set of corresponding fragment
sizes.
103. The method of claim 101, wherein piecewise scaling the
background model to obtain the dynamic threshold comprises:
determining a first region of the background model corresponding to
amplification products with sizes above a first fragment size and
below a second fragment size; determining a second region of the
background model corresponding to amplification products with sizes
above the second fragment size; and multiplying the first region of
the background model by a first scaling factor that varies from an
initial scaling factor to a second scaling factor less than the
initial scaling factor; and multiplying the second region of the
background model by the second scaling factor.
104. The method of claim 101, wherein determining gene-specific
product peak locations in the repeat profile using the dynamic
threshold comprises: identifying a first peak in the repeat profile
at a first location; and determining a first value of the repeat
profile at the first location exceeds a first value of the dynamic
threshold at the first location.
105. The method of claim 101, wherein determining gene-specific
product peak locations in the repeat profile using the dynamic
threshold further comprises: identifying a second peak in the
repeat profile at a second location, the second peak adjacent to
the first peak, determining a second value of the repeat profile at
the second location satisfies an amplitude criterion, the amplitude
criterion based on the first value, and determining the second
value of the repeat profile at the second location exceeds a second
value of the dynamic threshold at the second location.
106. A method of genotype peak sizing comprising: i. amplifying a
repeat region of a nucleic acid sample; ii. performing high
resolution fragment analysis; iii. obtaining a ladder of
amplification products; iv. identifying at least one gene-specific
peak; and v. sizing at least one of (a) the repeat region and (b)
the at least one gene specific peak using the ladder of
amplification products.
107. The method of claim 106, wherein sizing at least one of (a)
the repeat region and (b) the at least one gene-specific product
peak using the ladder of amplification products comprises sizing
using an internal sizing standard.
108. The method of claim 107, wherein sizing at least one of (a)
the repeat region and (b) the at least one gene-specific peak using
an internal sizing standard comprises: generating the internal
sizing standard by estimating a linear relationship between repeat
peak location and repeat fragment size in a repeat profile of a
first channel; generating an external sizing standard by estimating
a linear relationship between peak location and fragment size for a
first region of a second channel, estimating a non-linear
relationship between peak location and fragment size for a second
region of the second channel, and generating the external sizing
standard by combining the linear relationship and the nonlinear
relationship; generating a mobility corrected sizing standard by
combining the internal sizing standard and the external sizing
standard; and sizing the at least one of the repeat region and the
at least one gene-specific peak using the mobility corrected sizing
standard.
109. The method of claim 106, further comprising at least a first
and a second primer for amplification of the repeat region.
110. The method of claim 109, wherein the first primer comprises a
portion of the repeat region.
111. The method of claim 109, wherein the second primer anneals to
a position outside of the repeat region.
112. The method of claim 109, further comprising a third primer and
an optional fourth primer.
113. The method of claim 106, further comprising correcting a
channel for signal artifacts.
114. The method of claim 106, further comprising: identifying a
window in a channel including a potential air bubble location;
determining a correlation between signal intensities for channels
within the window; and replacing, based on the determined
correlation, the signal intensities for the channels within the
window with simulated noise.
115. The method of claim 106, further comprising extending the
dynamic range of a first Channel, the first channel configured to
detect a first electromagnetically detectable moiety, wherein
extending the dynamic range comprises: identifying a window of the
first channel including a saturated region; determining combined
signal intensities using the signal intensities for the first
channel within the window and the signal intensities for a second
channel configured to detect a another electromagnetically
detectable moiety within the window; and replacing the signal
intensities for the first channel.
116. The method of claim 115, wherein determining the combined
signal intensities comprises extrapolating the shape of a
calculated peak in the first channel.
117. The method of claim 106, further comprising: identifying a
bleed-over location based on signal intensities for a channel;
determining a window including the bleed-over location based on
signal intensities for another channel; and replacing the signal
intensities for the other channel within the window with simulated
noise.
118. The method of claim 106, further comprising determining
satisfaction of quality control criteria, the quality control
criteria comprising at least one of sizing model criteria, a repeat
profile signal-to-noise criterion, a repeat profile contamination
criterion, and a minor allele sensitivity criterion.
119. The method of claim 118, wherein the sizing model criteria
comprise at least one of an internal sizing model goodness-of-fit
criterion, an external sizing model goodness-of-fit criterion, and
a consistency criterion that compares the internal sizing model to
the external sizing model.
120. The method of claim 118, wherein the repeat profile
contamination criterion is failed when a repeat size associated
with a location of a gene-specific peak is less than zero.
121. The method of claim 118, wherein the minor allele sensitivity
criterion is satisfied when a ratio of a noise value of a repeat
profile to a maximum of the values of the repeat profile at a
location of a gene-specific peak exceeds a threshold value.
122. The method of claim 106, further comprising dynamically
determining a threshold for calling peaks in a repeat profile.
123. The method of claim 106, further comprising using a sliding
window to call peaks in a repeat profile.
124. The method of claim 106, further comprising interpolating
peaks below an amplitude threshold in a repeat profile.
125. The method of claim 106, further comprising generating a
calibration curve that maps from sampling units to base pair units
using estimated peak locations in a repeat profile.
Description
[0001] This application claims the benefit of priority to
Provisional Application No. 62/250,476, filed Nov. 3, 2015, which
is hereby incorporated by reference in its entirety.
[0002] The present disclosure relates to data processing and
calculating annotation systems and devices, and corresponding
methods, for nucleic acid analysis. In particular, the disclosure
relates to methods of sizing a repeat region of a nucleic acid.
[0003] Genetic loci that comprise regions of nucleotide repeats
(e.g., homopolymer regions, dinucleotide repeats, trinucleotide
repeats, hexanucleotide repeats etc.) are common in the human or
animal genome. Genetic loci that have enriched GC
(guanine-cytosine) content are also common, while loci having AT
(adenine-thymine) rich content have been reported and studied. In
some circumstances, the expansion of GC or A/T-rich regions, or the
expansion of nucleotide repeats, can be associated with various
disease states. For example, the expansion of CGG repeats in the 5'
untranslated region (UTR) of the Fragile X Mental Retardation-1
gene (FMR1), located on the X chromosome, is associated with
Fragile X Syndrome (FXS) and a variety of disorders and phenotypes.
In most people, the trinucleotide CGG is repeated approximately
5-44 times in the 5' untranslated region (UTR) of the FMR1 gene
("CGG repeat region"). Expansions in this region to greater than
about 45 CGG repeats, and particularly to greater than about 200
CGG repeats, have been associated with FXS. FXS phenotypes may
include mental retardation, autism, anxiety, and other cognitive or
behavioral conditions. (J. Mol. Diag. 10(6): 496-501 (2008)).
Likewise, expansion of the CCG trinucleotide repeat region ("CCG
repeat region") in the 5' UTR of the FMR2 gene is associated with
X-linked intellectual disabilities, and particularly with Fragile X
syndrome E (FRAXE). FRAXE is a common form of X-linked mental
retardation. In other instances, repeat length polymorphisms have
been associated with disease states. For example, intron 6 of the
TOMM40 gene contains a poly-T repeat region and has been reported
to exhibit a repeat length polymorphism in the population (rs
10524523). The TOMM40 poly-T size has been reported as being
associated with late-onset Alzheimer's Disease and with cognitive
performance in the elderly (See The Pharmacogenetics Journal
10:375-3840 (2010); and Alzheimer's and Dementia 9:132-136 (2013)).
Furthermore, an intronic (G.sub.4C.sub.2).sub.n hexanucleotide
repeat expansion in the C9ORF72 gene has been observed in the
general population with a frequency of approximately 1/600 and is
present in approximately 10% of all amyotrophic lateral sclerosis
(ALS) and frontotemporal dementia (FTD) cases. Fewer than 30
repeats are considered normal, whereas pathogenic C9ORF72
expansions may include hundreds to thousands of repeats. Methods
for accurately measuring, sizing, and reconstructing patient
genotypes can therefore be beneficial for the diagnosis and
treatment of these and other diseases.
[0004] Methods for evaluating sequences comprising nucleotide
repeats, such as the CGG and CCG repeats in FMR1 and FMR2, include
restriction enzyme digestion and polymerase chain reaction (PCR)
strategies. Restriction digest analysis can provide a crude measure
of the size of a repeat region. However, restriction digest
analysis can be limited in resolution, does not easily detect short
interruptions (such as AGG interruptions within a CGG repeat
region), and cannot determine methylation status.
[0005] PCR strategies may provide greater accuracy in sizing the
repeat regions and reconstructing various genotypes. However,
limitations exist in the amplification and sequencing of genetic
loci that comprise long repeat sequences or those containing GC or
A/T-rich sequences that hinder the ability to reconstruct genotypes
for these loci. Efforts to optimize PCR procedures for the analysis
of the CGG repeats in FMR1, for example, have been attempted, and
include modifications to conventional PCR assays. (See Genome Res.
6(7): 633-8, (1996); J. Mol. Diag. 8: 544-550, (2006); and Am. J.
Med. Genet. 51(4): 527-34, (1994)). More recently, PCR techniques
have been developed that permit more reliable amplification of
genomic loci having over 200 CGG or CCG repeats.
[0006] Current workflow strategies often employ PCR with size
resolution techniques, for example, capillary electrophoresis.
Capillary electrophoresis improves the quantitative capabilities of
PCR-based assays to allow for accurate resolution of DNA products
down to the single base resolution. To facilitate sizing of DNA
products, external standard calibrators are typically employed, for
example, using pooled dye-labeled DNA fragments of known sizes,
which span the size range of interest for a specific capillary
electrophoresis application. Nonetheless, despite the enabling
capabilities achieved by applying these standards, this approach
has several shortcomings which include but are not limited to: i.
the high cost of commercially available dye labeled DNA ladders;
ii. the reduced capillary electrophoresis multiplexing bandwidth
caused by the use of a dedicated dye channel for the standard
ladder iii. the sizing inaccuracy resulting from skewed
electrophoretic mobility due to base composition or sequence
differences between the PCR amplified analyte and the standard DNA,
requiring the use of custom DNA ladders, which can be laborious or
inefficient to use in sizing, specifically for repeat disorder PCR
products. Furthermore, fragment size analysis of FMR1 PCR and CE
products is conducted manually by trained operators, which is
laborious for large sample sets, and can introduce both ambiguity
and subjectivity into an otherwise streamlined workflow.
[0007] Accordingly, there is a need in the art for improved methods
of sizing repeat regions and reconstructing genotypes associated
with those repeat regions, which may also be GC or A/T-rich. The
methods disclosed herein relate to the generation and use of an
internal standard, alone or in combination with an external
standard, in an amplification based method for sizing one or more
nucleotide repeat regions and reconstructing a genotype
therefrom.
BRIEF DESCRIPTION OF DRAWINGS
[0008] The drawings are not necessarily to scale or exhaustive.
Instead, emphasis is generally placed upon illustrating the
principles of the inventions described herein. The accompanying
drawings, which are incorporated in and constitute a part of this
specification, illustrate several embodiments consistent with the
disclosure and, together with the description, serve to explain the
principles of the disclosure. In the drawings:
[0009] FIG. 1 depicts an exemplary high-level representation of a
system for genotype peak sizing.
[0010] FIG. 2 illustrates an exemplary process for automatically
analyzing genomic repeat regions.
[0011] FIG. 3A depicts an exemplary external sizing ladder for use
in generating an external sizing standard.
[0012] FIG. 3B depicts an exemplary external sizing standard.
[0013] FIG. 4A depicts an exemplary channel of a ladder of
amplification products.
[0014] FIG. 4B depicts generation of an internal sizing standard
using a repeat profile.
[0015] FIG. 5 depicts an exemplary repeat profile annotated with a
background model and dynamic threshold.
[0016] FIG. 6A depicts an exemplary repeat region for a
heterozygous female showing an (n, n+1) genotype.
[0017] FIG. 6B depicts an exemplary expanded repeat region.
[0018] FIGS. 7A-7C depict samples with different quality control
issues. FIG. 7A. depicts a sample with a poor ROX ladder. FIG. 7B.
depicts a sample with a poor PCR amplification. FIG. 7C depicts a
sample with a contamination peak.
[0019] FIG. 8 depicts an exemplary computing system for genotype
peak sizing.
[0020] FIG. 9 depicts a schematic representation of the
experimental design according to certain embodiments described in
Example 1.
[0021] FIG. 10 depicts a graphic representation of the process by
which repeat peak localization is utilized for sizing gene-specific
products. The expected location of each repeat may be used to
generate a calibration curve for sizing, either as a stand-alone
sizing ladder or as a fragment mobility correction to an external
sizing standard.
[0022] FIG. 11 depicts the distribution of FMR1 genotypes in the
general populations in light grey compared to the distribution of
genotypes in the 1106-sample validation dataset in dark grey.
[0023] FIG. 12 depicts the concordance between repeat region
lengths determined using an internally-derived sizing ladder and a
sizing ladder derived from an external sizing standard called ROX
ladder (ROX 100 Size Ladder, Asuragen P/N: 145194).
[0024] FIG. 13 depicts a comparison between manual and automated
sizing of major alleles evaluated in a clinical set.
[0025] FIGS. 14A and 14B depict diagrams detailing the analytical
sensitivity of algorithm to gene-specific products. Arrows indicate
calls made by automated sizing. FIG. 14A depicts detection of
additional gene-specific products. FIG. 14B depicts detection of a
low-abundance expanded allele.
[0026] FIGS. 15A and 15B depict diagrams detailing analytical
sensitivity of the assay across FMR1 genotype ranges. Arrows
indicate calls made by automated sizing. FIG. 15A depicts a normal
sample. FIG. 15B depicts a premutation sample with a minor
allele.
[0027] FIG. 16 shows a diagram of exemplary assay and software
components.
[0028] FIG. 17 depicts an external ladder, ROX profile depicting
certain assays.
[0029] FIGS. 18A and 18B depict the results of testing the
automatic sizing analysis of a large (n=1106) set of clinical
samples.
[0030] FIGS. 19A and 19B depict the results of multi-instrument
RUSH input amount testing.
[0031] FIGS. 20A and 20B depict the results of artificial minor
allele input titration testing.
[0032] FIGS. 21A and 21B depict the results of RUSH sample
titration testing.
[0033] FIGS. 22A and 22B depict the exemplary results of automatic
sizing analysis for samples with normal genotypes.
[0034] FIGS. 22C and 22D depict the exemplary results of automatic
sizing analysis for samples with premutation genotypes.
[0035] FIGS. 23A and 23B depict the exemplary results of automatic
sizing analysis for expanded samples.
[0036] FIGS. 23C and 23D depict exemplary results of low-level
minor allele identification and sizing.
[0037] FIG. 24 depicts exemplary results of automatic sizing
analysis for a control sample with a mixture of genotypes
throughout the normal, permutation, and expanded genotype
ranges.
[0038] FIGS. 25A and 25B depicts exemplary results of automatic
sizing analysis for a control sample including a mixture of 5% full
mutation sample in a background of 95% permutation sample. FIG. 25A
depicts the full sample including all called genotypes, while FIG.
25B depicts a zoomed-in version showing the full mutation call.
DETAILED DESCRIPTION
[0039] Reference will now be made in detail to certain exemplary
embodiments according to the present disclosure, certain examples
of which are illustrated in the accompanying drawings.
[0040] The section headings used herein are for organizational
purposes only and are not to be construed as limiting the subject
matter described. All documents, or portions of documents, cited in
this application, including but not limited to patents, patent
applications, articles, books, and treatises, are hereby expressly
incorporated by reference in their entirety for any purpose. To the
extent publications and patents or patent applications incorporated
by reference contradict the invention contained in the
specification, the specification will supersede any contradictory
material.
[0041] To assist in understanding the present invention, certain
terms are first defined. Additional definitions are provided
throughout the application.
[0042] The use of the word "a", "an", or "the" when used in
conjunction with the term "comprising" in the claims and/or the
specification can mean "one", but it is also consistent with the
meaning of "one or more", "at least one", and "one or more than
one".
[0043] In this application, the use of the singular includes the
plural unless specifically stated otherwise. Also in this
application, the use of "or" means "and/or" unless stated
otherwise. Furthermore, the use of the term "including", as well as
other forms, such as "includes" and "included", are not limiting.
Any range described herein will be understood to include the
endpoints and all values between the endpoints.
[0044] The term "A/T-rich", "A/T-richness", and "repeating A/T-rich
segment" as used herein refers to a homopolymeric segment, defined
below, or a segment comprising (T.sub.nA).sub.m, (AT.sub.n).sub.m,
(TA.sub.n).sub.m, or (A.sub.nT).sub.m, wherein n is 2 or greater
and m is such that the length of the repeating A/T-rich segment is
10 or more residues. The value of n need not be constant throughout
the segment. Thus, examples of repeating A/T-rich segments include
AATAATAATAAT, AATAAATAAT, AAATAAAAAT, AATAAAAAAT, etc. With respect
to a segment comprising (T.sub.nA).sub.m, (AT.sub.n).sub.m,
(TA.sub.n).sub.m, or (A.sub.nT).sub.m, in some embodiments, n is a
value ranging from 2 to 10. In some embodiments, n is a value
ranging from 3 to 10. In some embodiments, n is a value ranging
from 4 to 10. In some embodiments, n is a value ranging from 2 to
8. In some embodiments, n is a value ranging from 3 to 8. In some
embodiments, n is a value ranging from 4 to 8. In some embodiments,
n is a value ranging from 2 to 6. In some embodiments, n is a value
ranging from 3 to 6. In some embodiments, m is a value ranging from
2 to 20. In some embodiments, m is a value ranging from 3 to 20. In
some embodiments, m is a value ranging from 4 to 20. In some
embodiments, m is a value ranging from 2 to 15. In some
embodiments, m is a value ranging from 3 to 15. In some
embodiments, m is a value ranging from 4 to 15. In some
embodiments, m is a value ranging from 2 to 10. In some
embodiments, m is a value ranging from 3 to 10. In some
embodiments, m is a value ranging from 4 to 10. In some
embodiments, m is a value ranging from 2 to 8. In some embodiments,
m is a value ranging from 3 to 8. In some embodiments, m is a value
ranging from 4 to 8. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 10 to about
60 residues. In some embodiments, the length of the repeating
A/T-rich segment is in the range from about 10 to about 40
consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 15 to about
40 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 20 to about
40 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 5 to about 50
consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 10 to about
50 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 15 to about
50 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 20 to about
50 consecutive residues. In some embodiments, the length of the
repeating AT-rich segment is in the range from about 5 to about 60
consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 10 to about
60 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 15 to about
60 consecutive residues. In some embodiments, the length of the
repeating A/T-rich segment is in the range from about 20 to about
60 consecutive residues. Unless otherwise indicated, a repeating
A/T-rich segment can comprise an interruption as explained in the
following paragraph. In some embodiments, a repeating A/T-rich
segment does not comprise an interruption.
[0045] As used herein, "bleed-over" occurs when
fluorescently-labelled PCR products fluoresce with sufficient
intensity produce significant signal in an overlapping fluorescent
frequency emission band logically assigned to a
differently-labelled PCR product. This bleed-over may arise when
fluorescence detectors for the different channels have overlapping
spectral sensitivities. This bleed-over may convolute the process
of detecting products native to a particular channel in a multiplex
reaction. For example, PCR products in the HEX channel may
fluoresce with sufficient intensity to affect the recorded signal
intensities in the ROX channel.
[0046] As used herein, "GC-rich", "GC-richness", and "repeating
GC-rich segment" refer to a homopolymeric segment, defined below
comprising G or C nucleotides, or to a segment comprising repeating
patterns of G and C nucleotides. CGG repeats, CCG repeats, GGGGCC
repeats, and optional interspersed AGG interruptions are included.
The fraction or percentage of total nucleobase residues in a
nucleic acid or a fragment of that nucleic acid that are guanine
residues, cytosine residues, or analogs thereof defines the
richness. For example, a 100 nucleotide sequence that contains
exactly 30 cytosines, exactly 30 guanines, exactly one cytosine
analog, and exactly one guanine analog has a GC-richness of 62%. In
some embodiments, a "GC-rich" nucleic acid or region of a nucleic
acid is one that contains more than about 50% guanine residues,
cytosine residues, or analogs thereof (e.g., more than about 50,
51, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99, or 99.5%
guanine residues, cytosine residues, or analogs thereof, or any
percentage in between).
[0047] The term "homopolymeric segment" as used herein refers to
segments of nucleic acid which comprise a nucleotide such as G, C,
A, T, or U repeated in series.
[0048] Unless otherwise indicated, a homopolymeric segment, a
GC-rich repeat, or A/T-rich repeat can comprise an interruption in
an otherwise consecutive or repeating series of nucleotides. The
interruption can be any number of nucleotides differing from the
other nucleotides making up the series. In some embodiments, the
interruption is a single nucleotide. An example of a homopolymeric
segment comprising an interruption is a first number of T residues,
then one C residue, and then a second number of T residues.
[0049] An example of a homopolymeric segment comprising an
interruption is a first number of U residues, then one C residue,
and then a second number of U residues. Another example of a
homopolymeric segment comprising an interruption is a first number
of A residues, then one G residue, and then a second number of A
residues. The first and second numbers of A, T, or U residues in
the foregoing examples can be, e.g., in the range of 5 to 10. In
some embodiments, the first and second numbers of A, T, or U
residues in the foregoing examples are in the range of 6 to 10. In
some embodiments, the first and second numbers of A, T, or U
residues in the foregoing examples are in the range of 7 to 10. In
some embodiments, the first and second numbers of A, T, or U
residues in the foregoing examples are in the range of 8 to 10. In
some embodiments, the first and second numbers of A, T, or U
residues in the foregoing examples are in the range of 9 to 10.
Alternatively, a homopolymeric segment can comprise a consecutive
series of nucleotides (which is not interrupted).
[0050] As used herein, a "nucleic acid" is any contiguous
nucleobase residues or analogs that have been isolated from a
subject and/or for which sizing of a repeat region is sought. A
nucleic acid can comprise a gene, gene fragment, or genomic region
isolated from a subject. As used herein, a "genotype" is or
comprises the nucleobase sequence of a nucleic acid.
[0051] As used herein, a "peak location" may be an index of a
signal where the slope changes sign from positive to negative.
Other definitions of peak location may be used without departing
from the envisioned systems and methods. For example:
f(x,c)=.DELTA..sub.1 sgn(.DELTA..sub.1s(x,c))
P(c)={x|x,f(x,c)=-2s(x,c)>50}
[0052] Here s(x, c) represents the signal intensity at index x for
instrument channel c, f(x, c) is the derivative of the sign of the
first-order derivative of the signal intensity, and P(c) represents
the set of all indices in channel c for which f(x, c) equals
-2.
[0053] As used herein a "peak shoulder" may be the point nearest a
peak location at which the signal intensity exceeds an amplitude
threshold. This amplitude threshold may be two standard deviations
above a mean value. In some embodiments, the standard deviations
and mean values may be calculated over an interval of the signal
including the peak shoulder. The left peak shoulder may be the peak
shoulder with a lower index than the peak location, and the right
peak shoulder may be the peak shoulder with a higher index than the
peak location. In other embodiments, peak shoulders can be
identified by fitting a statistical distribution to the peak, where
shoulders are assigned to locations at the tail ends (defined by
percentile cutoff) of the distribution. In yet other embodiments,
peak shoulders can be identified using the first order derivative
of the peak region, where shoulders are assigned to locations where
the absolute value of the first order derivative is below a
threshold.
[0054] As used herein, a "repeat region" or "nucleotide repeat
region" refers to a nucleic acid or a region of a nucleic acid
comprising a repeating sequence of 1-20 nucleobase residues in
length (e.g., a homopolymer, a dinucleotide, trinucleotide,
tetranucleotide, pentanucleotide, hexanucleotide sequence, etc.)
wherein the short sequence is repeated 2 or more times (e.g., 2, 3,
4, 5, 10, 15, 20, 50, 100, 200, 500, or more repeats). For example,
a nucleotide repeat would encompass a region of a nucleic acid in
which a short sequence such as CGG, CCG, GGGGCC is repeated two or
more times. A repeat region may be a homopolymer, e.g. a run of A
or T nucleotides, and a repeat region may include interruptions or
repeat variants. A nucleic acid or a region of a nucleic acid can
(but does not need to be) be both a repeat and a GC rich region, or
a repeat and AT rich region. For example, the nucleic acid or
region of a nucleic acid can comprise di-, tri-, tetra-, penta-, or
hexa-nucleotide repeats of guanine residues, cytosine residues, or
analogs thereof.
[0055] A nucleic acid can comprise one or more nucleotide repeat
regions, A/T-rich regions, or GC-rich regions that contain one or
more interruptions. As used herein, an "interruption" in a nucleic
acid refers to the presence of one or more nucleobase residues or
analogs in the nucleic acid that are inconsistent with the repeat
pattern or, in a GC-rich region, comprises a nucleobase other than
G or C (or analogs thereof). For example, a GC-rich, nucleotide
repeat region could encompass a sequence comprising 40 CGG
trinucleotide repeats with two AGG sequences interspersed within
the 40 CGG repeats.
[0056] As used herein "signal intensities" are expressed herein in
terms of "relative fluorescence units" or RFUs, but other measures
of fluorescence may be used without departing from the envisioned
systems and methods.
[0057] As used herein, the term "template" refers to a nucleic acid
that interacts with a primer for extension in a nucleic acid
synthesis reaction.
I. Sizing a Repeat Region and Reconstructing a Genotype
[0058] FIG. 1 depicts an exemplary high-level representation of a
system 100 for genotype peak sizing. System 100 may comprise PCR
device 110, capillary electrophoresis (CE) device 120, annotation
device 130, and laboratory information management system 140. PCR
device 110 may comprise a PCR instrument familiar to those skilled
in the art. For instance, the PCR device may comprise a thermal
cycle. As a non-limiting example, an ABI model 9700 thermal cycler
may be used. PCR device 110 may be configured to amplify a repeat
region of a nucleic acid sample.
[0059] CE device 120 may comprise a CE instrument familiar to those
skilled in the art. For instance, ABI model 3100, 3130, 3730, or
3500 CE devices may be used. CE device 120 may be configured to
perform high resolution fragment analysis. In some embodiments, the
CE device may be used to separate amplification fragments by size.
In some embodiments, the CE device may be used to generate a ladder
of amplification products. In other embodiments, the CE device may
be used to generate a ladder of amplification products by
separating amplification fragments by size. In certain embodiments,
the CE device is used to obtain repeat region sizing information.
CE device 120 may be configured to provide an output indicative of
this ladder of amplification products. In some embodiments, this
output may comprise one or more channels in a file. The file may be
an .FSA file, or a similar file known to one of skill in the
art.
[0060] Annotation device 130 may comprise a purpose-built computing
device, a desktop, workstation, all-in-one system, computer
cluster, terminal, mainframe, mobile computing device, or other
computing device. Annotation device 130 may be standalone; or may
be part of a subsystem, which may be part of a larger system. For
example, Annotation device 130 may comprise distributed servers
that are remotely located and communicate over a public network or
a dedicated private network. In some embodiments, annotation device
130 may be implemented at least in part as a virtual system on a
cloud-computing infrastructure. Consistent with disclosed
embodiments, annotation device 130 may include or communicate with
one or more storage devices configured to store data and/or
software instructions. The stored data and/or software instructions
may include one or more software programs. For example, the stored
data and/or software instructions may include analysis software.
Annotation device 130 may execute this analysis software to perform
one or more methods consistent with the disclosed embodiments. In
certain aspects, annotation device 130 may execute this analysis
software remotely from annotation device 130. For example,
annotation device 130 may access one or more remote devices to
execute the stored analysis software. In certain embodiments,
annotation device 130 may be configured as a particular apparatus
or system based on the storage, execution, and/or implementation of
the analysis software. Annotation device 130 may be configured to
communicate with other components of system 100, such as CE device
120 and Laboratory Information Management System 140. Annotation
device 130 may communicate with these components of system 100
using Ethernet, FireWire, USB, RS-232, SCSI, WLAN, Bluetooth, or a
similar interface.
[0061] Annotation device 130 may be configured to size repeating
genomic regions. This sizing may be performed automatically. For
example, annotation device 130 may be configured to generate FMR1
sizing results. Annotation device 130 may employ a combination of
signal-processing, statistical, and machine learning techniques to
size repeating genomic regions, and/or identify gene product
locations. Annotation device 130 may be configured to output
results of this analysis, and/or intermediate step of this
analysis. Annotation device 130 may be configured to output these
indications to laboratory information management system 140, or to
a display, printer, storage device, or another system.
[0062] Laboratory information management system 140 may comprise
purpose-built computing device, a desktop, workstation, all-in-one
system, computer cluster, terminal, mainframe, mobile computing
device, or other computing device. Laboratory information
management system 140 may be stand alone; or may be part of a
subsystem, which may be part of a larger system. For example,
laboratory information management system 140 may comprise
distributed servers that are remotely located and communicate over
a public network or a dedicated private network. In some
embodiments, laboratory information management system 140 may be
implemented at least in part as a virtual system on a
cloud-computing infrastructure. Consistent with disclosed
embodiments, laboratory information management system 140 may
include or communicate with one or more storage devices configured
to store data and/or software instructions. The stored data and/or
software instructions may include one or more software programs.
Laboratory information management system 140 may execute the stored
one or more software programs to perform one or more methods
consistent with the disclosed embodiments. In certain aspects,
laboratory information management system 140 may execute the stored
one or more software programs remotely from laboratory information
management system 140. For example, laboratory information
management system 140 may access one or more remote devices to
execute the stored one or more software programs. In certain
embodiments, laboratory information management system 140 may be
configured as a particular apparatus or system based on the
storage, execution, and/or implementation of the software
instructions. Laboratory information management system 140 may be
configured to communicate with other components of system 100, such
as CE device 120 and Annotation device 130. Laboratory information
management system 140 may communicate with these components of
system 100 using Ethernet, FireWire, USB, RS-232, SCSI, WLAN,
Bluetooth, or a similar interface.
[0063] Laboratory information management system 140 may be
configured to manage samples and corresponding data. In other
aspects, laboratory information management system 140 may be used
to automate workflows. In some embodiments, laboratory information
management system 140 may be configured to execute Sample Manager
Laboratory Information Management System, Watson Laboratory
Information Management System, Nautilus Laboratory Information
Management System or Clinical Laboratory Information Management
System. One of skill in the art would readily know of appropriate
Laboratory Information Management Systems. Laboratory Information
Management System 140 may be configured to receive information
concerning the genomic sample. Laboratory Information Management
System 140 may receive this information from Annotation Device 130,
a storage device, or another system. Laboratory Information
Management System 140 may be configured to arrange this information
for storage and display to relevant clinical practitioners.
[0064] As would be appreciated by one of skill in the art, the
particular arrangement of devices depicted in FIG. 1 is not
intended to be limiting. For example, system 100 may include
additional devices or fewer devices. Likewise, functions of
individual devices of system 100 may be distributed across multiple
devices, and multiple functions performed by different devices of
system 100 may be performed by a single device.
[0065] System 100 may be configured to perform the following
methods of sizing a repeat region and optionally reconstructing a
genotype. In some embodiments, size analysis of a repeat region can
comprise amplifying the repeat region and using the amplified
products to determine the repeat region size. In certain
embodiments, the repeat region of a nucleic acid is amplified,
obtaining a ladder of amplification products. The ladder of
amplification products may be used as an internal standard in
sizing a repeat region. In some embodiments, the internal standard
is used without any external standards. In other embodiments, the
internal standard is used in combination with an external standard.
In some embodiments, the external standard may be a
fluorescent-labeled DNA ladder, for example a ROX size standard. In
more specific embodiments, the ROX size standard may be the ROX
1000 Size Ladder (Asuragen P/N: 145194). The amplification pattern
of the repeat region may be used to size the repeat region. The
skilled artisan will understand that the ladder of amplification
products will have certain features useful in sizing the repeat
region, such as a repeat profile, a repeat element periodicity, a
first amplification product, an amplification product count, and/or
a constant element length. In certain embodiments, the sizing
information may be used to generate a reconstructed genotype, to
diagnose a disorder in a patient, or to diagnose a risk of a
disorder of the offspring of the patient, or in treating a patient
with a disorder associated with an expanded repeat region.
[0066] In various embodiments, a method of sizing a repeat region
comprises amplifying a nucleic acid or a portion comprising the
repeat region to generate a series of amplification fragments. The
amplification fragments may be of different lengths corresponding
to the number of repeating units amplified in a particular
fragment. In other embodiments, the nucleic acid or portion
comprising the repeat region is not amplified, but directly
isolated and fragmented for further analysis. In some embodiments,
the amplification fragments (or unamplified fragments) are
separated by size, e.g., using a size resolution technique such as
high resolution fragment analysis, for example, analysis using a
genetic analyzer, microchip analyzer (such as Bioanalyzer),
capillary electrophoresis, or another high resolution method for
analyzing the amplification fragments of the ladder. For instance,
capillary electrophoresis may be used. In certain embodiments,
microchip electrophoresis may be used, such as a Bioanalyzer. In
various embodiments, the high resolution fragment analysis is used
to generate a ladder of amplification products, e.g., by evaluating
peaks corresponding to amplification products of differing repeat
number in a capillary electrophoresis electropherogram. In some
embodiments, the known length of the individual repeating unit can
be used to convert the ladder of repeat units to a ladder
indicating nucleotide base pair (bp) length. For example,
amplification and capillary electrophoresis of a nucleic acid
region comprising repeating units will result in a ladder of
amplification fragments differing in length by units of three
nucleotides, allowing for conversion of the ladder to a measure of
nucleotide length using parameter information for the ladder of
amplification products. In some embodiments, the ladder is used to
determine the size of the repeat region in a nucleic acid of
interest. In some embodiments, the ladder is used to determine the
size of other portions of the nucleic acid comprising the repeat
region or the size of other nucleic acids of interest amplified in
the same reaction that generates the ladder. In some embodiments,
the repeat region size is used to reconstruct a genotype. In
certain embodiments, additional parameters such as the distance in
the forward and reverse directions to any interruptions in the
repeat region are also identified (e.g., from the capillary
electrophoresis electropherogram) and used with the repeat region
size to reconstruct a genotype. In certain embodiments, an
interruption in the repeat sequence is detected in the ladder of
amplification products.
[0067] In various embodiments, disclosed herein are methods for
sizing and/or characterizing a repeat region, for example, a
GC-rich or A/T-rich region and/or methods to reconstruct a genotype
comprising the repeat region. For example, the methods disclosed
herein can be used to size a repeat region from a nucleic acid or
fragment thereof comprising CGG repeats or CCG repeats. The methods
disclosed herein can be used to size a repeat region from a nucleic
acid or a fragment thereof comprising an A/T-rich segment, such as
a homopolymeric segment. The methods of sizing can be used in
conjunction with methods to determine interruptions in the repeat
region, as well as methods of reconstructing a genotype based on
the sizing (alone or in combination with additional parameters such
as the distance in the forward and reverse directions to any
interruptions in the repeat region).
[0068] In some embodiments, the methods can be used to size the
repeat region of the FMR1 or FMR2 gene, or fragments thereof, or
the 5' UTR of FMR1 or FMR2, or fragments thereof, isolated from a
subject. In certain embodiments, the methods disclosed herein are
used to assist in reconstructing a genotype for an FMR1 gene in a
sample from a subject, including the CGG repeat pattern and the
location and organization of AGG interruptions and/or methylation
within the 5' UTR of FMR1. In other embodiments, the methods
disclosed herein are used to assist in reconstructing a genotype
for FMR2, including the CCG repeat pattern, as for FMR1. In yet
other embodiments, the methods disclosed herein are used to assist
in sizing the repeat region of TOMM40. In other embodiments, the
methods are used to assist in sizing the repeat region of
C9ORF72.
[0069] In some embodiments, the methods disclosed herein are used
to determine the size of a repeat region of a nucleic acid or
fragment thereof from a patient sample, wherein the nucleic acid
has at least one repeat or GC or A/T-rich region, and wherein the
related genotype from at least one of the parents of the patient is
not known. In some embodiments, the nucleic acid of interest or a
portion comprising the repeat region is isolated from a patient
sample. Various isolation and purification methods are known and
can be used. In certain embodiments, the methods disclosed herein
are used to determine the size of a CGG or CCG repeat region, for
example of FMR1 or FMR2 from a patient sample. In certain
embodiments, the methods disclosed herein are used to determine the
size of a hexameric repeat, for example the GGGGCC repeat of
C9ORF72 from a patient sample. In certain embodiments, the methods
disclosed herein are used to determine the size of a homopolymeric
repeat, for example the poly-T repeat region of TOMM40 from a
patient sample. In some embodiments, the related genotype, such as
the FMR1, FMR2, C9ORF72, or TOMM40 genotype, from at least one of
the parents of the patient is not known.
[0070] In certain embodiments, a method for the sizing of a repeat
region of a nucleic acid sample comprises providing a sample from a
patient, wherein the sample contains a nucleic acid or fragment
thereof having one or more repeat regions or GC-rich or AT-rich
regions. In some embodiments, information characterizing the
nucleic acid (i.e., "parameter information") is collected. In some
embodiments, the parameter information includes features obtained
from the ladder of amplification products, including a repeat
profile, a repeat element periodicity, a first amplification
product, an amplification product count, and/or a constant element
length. In some embodiments, a repeat profile is the pattern of
peaks observed in the electropherogram. In some embodiments, the
amplification products are the spread of fragments produced by
amplifying the repeat region using the selected primers. In some
embodiments, the total length of the repeat region is calculated
from the parameter information. In some embodiments, additional
parameter information is generated, for example, information on the
percent of GC-richness or A/T-richness of a region of interest,
and/or the distance in the forward and reverse directions to any
interruptions in the repeat or GC-rich or A/T-rich region. In some
embodiments, the collected information is automatically analyzed
using an apparatus comprising a processor programmed to conduct an
automated analysis. In certain embodiments, the accuracy of the
sizing solution or solution genotype can be evaluated by manually
analyzing the genotype to confirm that it comports with the
parameter information, or by conducting any other confirmatory
assay (e.g., restriction enzyme digest, Sanger sequencing, or other
forms of high throughput sequencing). In some embodiments, the
sizing solution or solution genotype can be displayed or stored
electronically on a computer or can be printed for subsequent
diagnostic and therapeutic purposes.
[0071] In certain embodiments, the sizing of the repeat region can
be used to detect a mutation or genotype, or to diagnose, or assist
in diagnosing a disorder, or risk of a disorder associated with a
mutation in a repeat region, for example, an FMR1, FMR2, C9ORF72,
or TOMM40 related mutation, genotype, or disorder.
[0072] In various embodiments, sizing information characterizing a
repeat region of a nucleic acid can be obtained using any suitable
method, such as amplification and high resolution fragment
analyses. In certain embodiments, the sizing information (e.g., a
subset of parameter information, information characterizing the
nucleic acid, includes a repeat profile, a repeat element
periodicity, a first amplification product, an amplification
product count, and/or a constant element length relating to the
ladder of amplification products. In some embodiments, overall
length of the repeat region, as well as the distance from the start
of the repeat region to a first or subsequent interruption in the
forward direction and in the reverse direction are included in the
parameter information. In some embodiments, an apparatus is
provided, comprising a processor programmed to analyze parameter
information and to size a repeat region, and optionally to
reconstruct a genotype from the information. In certain
embodiments, the apparatus is used to reconstruct the genotype of
the nucleic acid from the information characterizing the nucleic
acid. In some embodiments, the apparatus evaluates sizes of each
product in the ladder of amplification products. In some, all
possible genotype reconstructions based on the length of the repeat
region and the interruptions in the forward or reverse direction to
select the reconstruction that satisfies all the parameter
information (e.g., the genotype that places the interruptions in
the correct positions in both the forward and reverse directions).
In certain embodiments, the apparatus provides a report of the
reconstructed genotype that can be displayed on a screen, saved
digitally for future use, or printed as a paper record.
[0073] In various embodiments, parameter information regarding a
nucleic acid can be obtained using any method known in the art, so
long as it includes information regarding the ladder of
amplification products suitable for sizing a nucleic acid. In some
embodiments, the parameter information includes a repeat profile, a
repeat element periodicity, a first amplification product, an
amplification product count, and/or a constant element length
relating to the ladder of amplification products. Restriction
enzymes that cleave a nucleic acid site-specifically can be used to
analyze a repeat region and thereby generate parameter information.
For example, the presence of AGG interruptions within a CGG repeat
tract of FMR1 can be detected by digesting a nucleic acid with the
restriction enzyme EciI (New England Biolabs Inc., Ipswich, Mass.,
USA). Restriction enzymes may be used to generate a ladder of
digested products, which can be used for sizing. In other
embodiments, amplification methods can be used to generate the
necessary information. For example, restriction digest and/or PCR
methods can be used with an FMR1 or FMR2 gene or fragments thereof
isolated from a patient in determining one or more CGG or CCG
repeat regions.
[0074] The methods disclosed in International Publication No.:
WO/2014/015273 are hereby incorporated by reference in their
entirety, including the PCR and capillary electrophoresis methods
disclosed in the publication for analyzing repeat regions,
obtaining parameter information including repeat size, and the
distance in the forward and reverse direction to any interruptions
in the repeat region.
[0075] In some embodiments, suitable methods for amplifying the
repeat region to generate amplification products include polymerase
chain reaction (PCR), real-time PCR (RT-PCR), nucleic acid
sequence-base amplification (NASBA), ligase chain reaction,
multiplex ligatable probe amplification, invader technology (Third
Wave), rolling circle amplification, in vitro transcription, strand
displacement amplification, transcription-mediated amplification
(TMA), RNA (e.g., Eberwine) amplification, loop-mediated isothermal
amplification, or any other methods that are known to one of skill
in the art. For example, FMR1 repeat region amplification can be
generated using a two-tier PCR approach with a CGG linker primer
and the Human FMR1 PCR kit (Asuragen Inc., Austin, Tex., USA). See
Tassone et al., J Mol Diagn. 10(1):43-49 (2008); Chen et al., J Mol
Diagn. 12(5): 589-600 (2010); Yrigollen et al., PLoS One 6(7):
e21728 (2011). For example, a nucleic acid comprising at least one
GC-rich region can be analyzed by (a) providing at least two PCR
primers, including a first primer comprising CGG, CCG, GCG, CGC,
GCC, or GGC repeats, and a second primer that anneals to a position
outside of the GC-rich region; (b) performing PCR on the nucleic
acid with the at least two different primers, wherein the PCR
produces a set of PCR products; (c) resolving the set of PCR
products with a high resolution technique (such as capillary
electrophoresis) to generate a representation of PCR product size
and abundance; and (d) deriving from the PCR product size and
abundance information the length of the GC-rich region and whether
or where within the GC-rich region an interruption is located.
[0076] In various embodiments, PCR-amplified nucleic acids are
analyzed to obtain repeat region sizing information, for example
using a high resolution fragment analyzer such as a capillary
electrophoresis (CE) instrument familiar to those skilled in the
art, such as ABI model 3100, 3130, 3730, or 3500 CE instruments
(Applied Biosystems, Carlsbad, Calif.). Other implementations,
include any instrument capable of electrophoretically or otherwise
sizing and/or sequencing an amplified nucleic acid, can also be
used. Any other method of collecting sizing and other parameter
information can also be used (e.g., Sanger sequencing or other
forms of high throughput sequencing). Various techniques for
analyzing the FMR1 gene or fragments thereof, such as the PCR
methods described in US Publication Nos. 2010/0209970,
2010/0243451, and 2012/0107824, often yield a ladder of
amplification products separated in length by the repeat spacing.
For example, repeat region sizing and genotype reconstruction to
characterize the CGG and CCG repeat loci in the 5' UTRs of FMR1 and
FMR2 or fragments thereof can be generated using the methods
described in U.S. Patent Publication No. 2010/0243451, including
the primers, polymerase, reagents, and reaction conditions
disclosed at paragraphs [0040]-[0051], [0056]-[0060],
[0065]-[0067], [0089], [0094], and [0104], which are hereby
incorporated by reference. Additionally, US Publication Nos.
2010/0209970, 2010/0243451, and 2012/0107824 describe PCR methods
and reagents for analyzing GC-rich regions that are hereby
incorporated by reference in their entirety.
[0077] For example, in some embodiments FMR1 and FMR2 parameter
information can be generated using a primer that anneals outside of
a repeat region and a primer that anneals to repeat sequences,
sequence permutations, or reverse complements of the sequences
(GCG, CCG, CGC, GCC, or GGC). The primers that can anneal outside
(upstream or downstream) of the repeat region may be forward or
reverse primers. The primers may anneal to sequences flanking the
repeat region. Examples of forward primers include CGG TGG AGG GCC
GCC TCT GAG C (SEQ ID NO: 1), CAG GCG CTC AGC TCC GTT TCG GTT T
(SEQ ID NO: 2), CAG TCA GGC GCT CAG CTC CGT TTC G (SEQ ID NO: 3),
TCC GGT GGA GGG CCG CCT CTG AGC (SEQ ID NO: 4), GGT TCG GCC TCA GTC
AGG CGC TCA GCT CCG TTT CG (SEQ ID NO: 5), GGG TTC GGC CTC AGT CAG
GCG CTC AGC TCC GTT TCG (SEQ ID NO: 6), GCG GGC CGG GGG TTC GGC CTC
AGT CA (SEQ ID NO: 7), CAG CGG GCC GGG GGT TCG GCC TCA G (SEQ ID
NO: 8), GCA GCG GGC CGG GGG TTC GGC CTC A (SEQ ID NO: 9), GGG CCG
GGG GTT CGG CCT CAG TCA G (SEQ ID NO: 10), GGG GTT CGG CCT CAG TCA
GGC GCT CA (SEQ ID NO: 11), GGG GTT CGG CCT CAG TCA GGC GCT CAG
(SEQ ID NO: 12), GGC GCT CAG CTC CGT TTC GGT TTC ACT TCC (SEQ ID
NO: 13), TCA GGC GCT CAG CTC CGT TTC GGT TTC A (SEQ ID NO: 14), CAC
TTC CGG TGG AGG GCC GCC TCT GA (SEQ ID NO: 15), TTC CGG TGG AGG GCC
GCC TCT GAG C (SEQ ID NO: 16), and TCA GGC GCT CAG CTC CGT TTC GGT
TTC ACG GCG GCG GCG GCG GA (SEQ ID NO: 44). Examples of reverse
primers include CGC ACT TCC ACC ACC AGC TCC TCC A (SEQ ID NO: 17),
GGA GCC CGC CCC CGA GAG GTG (SEQ ID NO: 18), GGG AGC CCG CCC CCG
AGA GGT (SEQ ID NO: 19), CGC ACT TCC ACC ACC AGC TCC TCC AT (SEQ ID
NO: 20), CGG GAG CCC GCC CCC GAG AGG TG (SEQ ID NO: 21), CCG GGA
GCC CGC CCC CGA GAG GT (SEQ ID NO: 22), CCG GGA GCC CGC CCC CGA GAG
GTG (SEQ ID NO: 23), CGC CGG GAG CCC GCC CCC GAG AGG TG (SEQ ID NO:
24), GCG CCG GGA GCC CGC CCC CGA GAG GT (SEQ ID NO: 25), CGC CGG
GAG CCC GCC CCC GAG AGG T (SEQ ID NO: 26), GCG CCA TTG GAG CCC CGC
ACT TCC ACC A (SEQ ID NO: 27), GCG CCA TTG GAG CCC CGC ACT TCC A
(SEQ ID NO: 28), AGC GCC ATT GGA GCC CCG CAC TTC C (SEQ ID NO: 29),
CGC CAT TGG AGC CCC GCA CTT CCA C (SEQ ID NO: 30), TTG GAG CCC CGC
ACT TCC ACC ACC A (SEQ ID NO: 31), AGC CCC GCA CTT CCA CCA CCA GCT
CCT C (SEQ ID NO: 32), GAG CCC CGC ACT TCC ACC ACC AGC TCC T (SEQ
ID NO: 33), CAT TGG AGC CCC GCA CTT CCA CCA CCA G (SEQ ID NO: 34),
CCC GCA CTT CCA CCA CCA GCT CCT CCA TCT (SEQ ID NO: 35), TAG AAA
GCG CCA TTG GAG CCC CGC ACT TCC (SEQ ID NO: 36), AAG CGC CAT TGG
AGC CCC GCA CTT CC (SEQ ID NO: 37), AAG CGC CAT TGG AGC CCC GCA CTT
CCC CGC CGC CGC CGC CG (SEQ ID NO: 43), and AAG CGC CAT TGG AGC CCC
GCA CTT CCC CGC CGC CGC CGC CT (SEQ ID NO: 45).
[0078] In some embodiments, FMR1 and FMR2 assays can use the
primers
TABLE-US-00001 (SEQ ID NO: 38)
TCAGGCGCTCAGCTCCGTTTCGGTTTCACTTCCGGT, (SEQ ID NO: 39)
AGCGTCTACTGTCTCGGCACTTGCCCGCCGCCGCCG, (SEQ ID NO: 40) TCA GGC GCT
CAG CTC CGT TTC GGT TTC A, and (SEQ ID NO: 41)
TCAGGCGCTCAGCTCCGTTTCGGTTTCA CGGCGGCGGCGGCGG.
The methods can additionally involve using primers comprising the
sequence of any of SEQ ID NOs 1-38 or 40 and comprising additional
repeats of CGG or the permutations and reverse complements thereof
(e.g., GCG, CCG, CGC, GCC, or GGC) appended to the 3' end. In some
embodiments, the number of CGG repeats or permutations in the
primer is four or five. In some embodiments, the primer contains a
sequence of CGG repeats (or permutations thereof) stretching for
12-15 nucleotides or more. In some embodiments, the primer contains
sequence of CGG repeats (or permutations thereof) ranging from 3 to
10 repeats. The primer may contain 3, 4, 5, 6, 7, 8, 9, or 10
repeats, and optionally an additional partial repeat of 1 or 2 C
and/or G residues.
[0079] In some embodiments, the primer that anneals to the repeat
region or GC rich region has a preferential binding activity for
sites in the region comprising an interruptor element. Preferential
binding to site of an interruptor element can result in selective
amplification of at least one product comprising the interruptor
element, e.g., by using the primer in a PCR reaction with an
oppositely oriented second primer that binds outside of the repeat
or GC rich region. Preferential binding activity can be specific,
for example, for sites comprising CGG and AGG elements, or the
permutations and/or reverse complements thereof, such as a site
comprising (1) one AGG element or a part of an AGG element
comprising an A, and (2) three, four, five, or six CGG elements and
optionally an additional partial CGG element.
[0080] In some embodiments, the primer that anneals to a repeat
region or a GC rich region and binds preferentially to a site or
sites comprising an interruptor element may comprise an A, T, or U
residue within or at the end of the part of the primer that anneals
to repeat or GC rich sequences. For example, the primer can have an
A, T, or U among or at the end of a stretch of CGG, CCG, GCG, CGC,
GCC, or GGC repeats; see, for example, SEQ ID NOs 44 and 45 above.
The A, T, or U residue can occur at the 3' end of the primer. When
the A, T, or U residue occurs at the end of the CGG, CCG, GCG, CGC,
or GCC, GGC repeats, there may or may not be a partial CGG, CCG,
GCG, CGC, GCC, or GGC repeat between the A, T, or U residue and the
last complete CGG, CCG, GCG, CGC, GGC, or GGC repeat. It is
possible to substitute unnatural nucleotide residues that
preferentially base pair with T/U or A residues relative to other
natural nucleotide residues for the A, T, or U residue. Likewise,
it is also possible to substitute one or more unnatural nucleotide
residues that preferentially base pair with C or G residues
relative to other natural nucleotide residues for one or more G
and/or C residues that make up the CGG, CCG, GCG, CGC, GCC, or GGC
repeats. The presence of one or more such unnatural residues within
a sequence otherwise made up of CGG, CCG, GCG, CGC, GCC, or GGC
repeats (optionally with an A, T, U, or corresponding unnatural
residue as discussed above) does not negate the identity of said
sequence within the context of the present disclosure as a sequence
of CGG, CCG, GCG, CGC, GCC, or GGC repeats. Unnatural nucleotide
residues are nucleotide residues comprising a nucleobase other than
adenine, thymine, guanine, cytosine, and uracil (A, T, G, C, and U,
respectively). Examples of unnatural nucleotide residues that
preferentially base pair with A or T/U residues include, without
limitation, adducts of T, U, or A residues that preferentially base
pair with A or T/U residues relative to other natural residues
(e.g., 5-substituted uracil analogs); and residues comprising
nucleobases such as, for example, pseudouracil and
diaminopurine.
[0081] In some embodiments, C9ORF72 parameter information can be
generated using a primer that anneals outside of a repeat region
and a primer that anneals to repeat sequences, sequence
permutations, or reverse complements of the sequences. The primers
that can anneal outside of (upstream or downstream) the repeat
region may be forward or reverse primers, as appropriate. These
sequences may anneal to sequences flanking the repeat region.
Examples of a forward primer include TGC GCC TCC GCC GCC GCG GGC
GCA GGC ACC GCA ACC GCA (SEQ ID NO: 46). Examples of reverse
primers include CGC AGC CTG TAG CAA GCT CTG GAA CTC AGG AGT CG (SEQ
ID NO: 47), TGC GCC TCC GCC GCC GCG GGC GCA GGC ACC GCA ACC GCA CCC
CGG CCC CGG CCC CGG (SEQ ID NO: 48), CGC AGC CTG TAG CAA GCT CTG
GAA CTC AGG AGT CGC CGG GGC CGG GGC CGG GG (SEQ ID NO: 49).
[0082] In some embodiments, TOMM40 parameter information can be
generated using a primer that anneals outside of a repeat region
and a primer that anneals to repeat sequences, sequence
permutations, or reverse complements of the sequences. The primers
that can anneal outsider of (upstream or downstream) of the repeat
region may be forward or reverse primers. These sequences may
anneal to sequences flanking the repeat region. An example of a
forward primer includes CCA AAG CAT TGG GAT TAC TGG C (SEQ ID NO:
50). An example of a reverse primer includes GAT TGC TTG AGC CTA
GGC ATT C (SEQ ID NO: 51).
[0083] In some embodiments, a first primer is used when generating
the ladder of amplification products and has a preferential binding
activity for sites in the repeat or GC rich region that do not
comprise interruptor elements. The presence of an interruptor
element can be signaled in the results of this method by a
relatively low level of products whose synthesis would have
involved extension of the first primer bound to sites comprising
the interruptor element. These low levels can appear as a gap or
set of low peaks surrounded by higher peaks in an electropherogram.
In some embodiments, a first primer is provided that has a
preferential binding activity for sites in the repeat or GC rich
region that comprise interruptor elements. The presence of an
interruptor element is signaled in an anchored assay by a
relatively high level of products whose synthesis involved
extension of the first primer bound to sites comprising the
interruptor element. The high level can appear as a spike
surrounded by lower peaks and/or baseline signal in an
electropherogram.
[0084] The methods of generating parameter information may relate
to amplification reactions comprising providing at least two or at
least three different primers. In some embodiments, at least three
different primers are provided and one of the primers is a primer
that preferentially binds outside the repeat or GC rich region, a
second primer preferentially binds within the repeat or GC rich
region, and the third primer is a subsequence of either the first
or second primer. In some embodiments, one primer is a chimeric
primer comprising CGG repeats and a 5' flap sequence, and another
primer has the sequence of the 5' flap sequence of the chimeric
primer. It should be noted that the primer having the sequence of
the 5' flap sequence of the chimeric primer can, but does not
necessarily, have the entire non-repeat sequence of the chimeric
primer. In other words, the sequence of part or all of one primer
can be comprised by the sequence of another primer; for example,
the chimeric primer comprises a 5' flap sequence, and another
primer can comprise the sequence of part or all of the 5' flap. In
some embodiments, the primer contains 12-15 nucleotides of a CGG
repeat sequence. The 5' flap sequence may correspond to a sequence
adjacent to or near to the CGG repeat region or it may be unrelated
to sequences in and around the CGG repeat region. In some
embodiments, the length of the chimeric primer may be approximately
35, 40, 45, 50, or 55 nucleotdies. In some embodiments, one or more
of the primers has a melting temperature ranging from 60.degree. C.
to 75.degree. C., for example, approximately 60.degree. C.,
65.degree. C., 70.degree. C., or 75.degree. C.
[0085] In some embodiments, at least three different primers are
provided and one primer is provided at a concentration lower than
the concentration of another primer. For example, the chimeric
primer is optionally provided at a lower concentration than the
primer with the sequence of the 5' flap sequence of the chimeric
primer. The ratio of concentrations, expressed as a fold
difference, may range from 2 to 10,000 or more, for example, 10,
20, 50, 100, 200, 500, 1,000, 2,000, 5,000, or 10,000 (or any value
in between). In such embodiments, the primer present at a lower
concentration can be depleted in early rounds of the amplification
reaction, such that extension is generally all, or nearly all, from
the primers still present (which were initially present at
relatively higher concentrations).
[0086] In some embodiments, the methods of generating parameter
information comprise providing dNTPs in a GC/AT ratio greater than
one, and at a total dNTP concentration conducive to synthesis of
DNA comprising repeat or GC-rich templates. See U.S. Publication
No. 2010-0209970. The GC/AT ratio may be about 1.1, 1.2, 1.4, 1.6,
2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 25, or higher. The GC/AT ratio may be between 1.1 and 20,
1.1 and 15, 1.1 and 10, 1.1 and 8, 1 and 15, 1.1 and 7, 1.1 and 6,
1.1 and 5, 1.2 and 25, 1.4 and 25, 1.6 and 25, 2 and 25, 3 and 25,
4 and 25, 5 and 25, 2 and 15, 2.5 and 10, or 4 and 10. The total
dNTP concentration may be about 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1,
1.2, 1.5, 2, or 3 mM. The dNTP concentration may be between 0.4 and
3 mM, 0.5 and 3 mM, 0.6 and 3 mM, 0.7 and 3 mM, 0.8 and 3 mM, 0.9
and 3 mM, 1 and 3 mM, 0.4 and 2 mM, 0.4 and 1.5 mM, 0.4 and 1.2 mM,
0.4 and 1 mM, 0.4 and 0.9 mM, 0.4 and 0.8 mM, 0.4 and 0.7 mM, 0.5
and 2 mM, 0.5 and 1 mM, or 0.6 and 0.9 mM. "GC/AT Ratio" means the
ratio of the concentration of the sum of dCTP, dGTP, and all
nucleotide analogs thereof, to the concentration of the sum of
dATP, dTTP, dUTP, and all nucleotide analogs thereof, in a given
solution or mixture. "dNTP" stands for deoxynucleotide triphosphate
and refers to dATP, dCTP, dGTP, dTTP, dUTP, and analogs thereof.
"Nucleotide analogs" are molecules or ions comprising a base moiety
other than the natural bases adenine (A), cytosine (C), guanine
(G), thymine (T), or uracil (U), a sugar moiety identical or
similar to deoxyribose, and at least one phosphate or multiple
phosphate (e.g., diphosphate or triphosphate) moiety. The
nucleotide analog is an analog of a specific nucleotide, in
particular dATP, dCTP, dGTP, dTTP, or dUTP, when it comprises a
triphosphate and a sugar moiety, the structure and configuration of
both of which are suitable for incorporation into a nucleic acid
double helix by a polymerase, and a base whose base pairing
properties in a nucleic acid double helix and loci of incorporation
by DNA polymerases in a nucleic acid double helix are most similar
to one of the five previously listed nucleotides, with the
exception that analogs of dTTP will generally also be analogs of
dUTP and vice versa. The term "analog" used in conjunction with
terms including but not limited to "nucleoside", "base",
"nucleobase", or "residue" is to be interpreted in the same manner
as if it were used in conjunction with "nucleotide."
[0087] In some embodiments, the methods of generating parameter
information can further comprise providing buffers for the PCR
amplification reactions. The buffers may comprise, for example and
without limitation, tris(hydroxymethyl)aminomethane (Tris),
bis-tris propane, bicarbonate, phosphate, glycine, histidine,
4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES),
3-(N-morpholino)propanesulfonic acid (MOPS), and various conjugate
bases/acids and salts thereof.
[0088] In some embodiments, the methods of generating parameter
information can comprise providing at least one DNA polymerase to
synthesize DNA from dNTPs in a template dependent manner. The DNA
polymerase may comprise a wild-type, modified, thermophilic,
chimeric, engineered, and/or a mixture of more than one polymerase.
The DNA polymerase may comprise Exact Polymerase (5 PRIME GmbH),
AccuSure.TM. DNA Polymerase (Bioline), Phusion.TM. AccuPrime.TM.
Pfx (Invitrogen), Platinum Taq DNA Polymerase High Fidelity
(Invitrogen), Phire.TM. Hot Start DNA Polymerase (New England
Biolabs), Phusion.RTM. Hot Start High-Fidelity DNA Polymerase (New
England Biolabs), JumpStart.TM. REDTaq.TM. DNA Polymerase
(Sigma-Aldrich), PfuUltra.TM. Hotstart DNA Polymerase (Stratagene),
PfuTurbo.RTM. Cx Hotstart DNA Polymerase (Stratagene),
PrimeSTAR.TM. HS DNA Polymerase (Takara), Extensor Hi-Fidelity PCR
Enzyme (ABgene), ACCUZYME.TM. DNA Polymerase (Bioline), SAHARA.TM.
DNA Polymerase (Bioline), VELOCITY DNA Polymerase (Bioline),
GeneChoice.RTM. AccuPOL.TM. DNA Polymerase (GeneChoice, Inc.),
GeneChoice.RTM. UniPOL.TM. DNA Polymerase (GeneChoice, Inc.),
Elongase Enzyme Mix (Invitrogen), Pfx50.TM. DNA Polymerase
(Invitrogen), Phusion DNA Polymerase (New England Biolabs), KOD
HiFi DNA Polymerase (Novagen), KOD XL DNA Polymerase (Novagen),
Expand 20 kb PLUS Thermostable DNA polymerase mixture (Roche
Applied Science), Expand High Fidelity PLUS Thermostable DNA
polymerase mixture (Roche Applied Science), Expand High Fidelity
Thermostable DNA polymerase mixture (Roche Applied Science), Expand
Long Template Thermostable DNA polymerase mixture (Roche Applied
Science), Easy-ATM High-Fidelity PCR Cloning Enzyme (Stratagene),
EXL.TM. DNA Polymerase (Stratagene), Herculase.RTM. Enhanced DNA
Polymerase (Stratagene), Herculase.RTM. II Fusion DNA Polymerase
(Stratagene), Kapa LongRange.TM. DNA Polymerase (Kapa Biosystems),
Kapa HiFi.TM. DNA Polymerase (Kapa Biosystems), Kapa2G.TM. Robust
DNA Polymerase (Kapa Biosystems), Kapa2G.TM. Robust HotStart DNA
Polymerase (Kapa Biosystems), Kapa2G.TM. Fast DNA Polymerase (Kapa
Biosystems), Kapa2G.TM. Fast HotStart DNA Polymerase (Kapa
Biosystems), LA TAQ DNA Polymerase (Takara), Optimase DNA
Polymerase (Transgenomic, Inc.), Exo-Pfu DNA Polymerase
(Stratagene), HotMaster Taq DNA Polymerase (5 PRIME GmbH), HotTaq
DNA Polymerase (Abnova Corporation), AmpliTaq Gold.RTM. DNA
Polymerase (Applied Biosystems), Bst DNA Polymerase Lg Frag (New
England Biolabs), MasterAmp.TM. Tfl DNA Polymerase (EPICENTRE
Biotechnologies), Red Hot DNA Polymerase (ABgene), Thermoprime Plus
DNA Polymerase (ABgene), Taq-red DNA Polymerase (AppliChem GmbH),
BIO-X-ACT.TM. Long DNA Polymerase (Bioline), BIO-X-ACT.TM. Short
DNA Polymerase (Bioline), Bioline HybriPol.TM. DNA Polymerase
(Bioline), BioTherm Taq DNA Polymerase (eEnzyme LLC), EU-Taq DNA
Polymerase (eEnzyme LLC), Synergy Taq DNA Polymerase (eEnzyme LLC),
GeneChoice.RTM. RedPOL.TM. DNA Polymerase (GeneChoice, Inc.),
AccuPrime.TM. GC-Rich DNA Polymerase (Invitrogen), PyroPhage.RTM.
3173 DNA Polymerase, Exo Minus (Lucigen), 9 Degrees North
(Modified) DNA Polymerase (New England Biolabs), Therminator DNA
Polymerase (New England Biolabs), Pwo DNA Polymerase (Roche Applied
Science), Pag5000.TM. DNA Polymerase (Stratagene), YieldAce.TM. DNA
Polymerase (Stratagene), e2TAK.TM. DNA Polymerase (Takara), or
naturally occurring DNA polymerases from P. kodakaraensis, P.
furiosus, T. gorgonarius, T. zilligii, T. litoralis "Vent.TM.", P.
GB-D "Deep Vent", T. 9N-7, T. aggregans, T. barossii, T.
fumicolans, T. celer, Pyrococcus sp. strain ST700, T. pacificus, P.
abysii, T. profundus, T. siculi, T. hydrothermalis, Thermococcus
sp. strain GE8, T. thioreducens, P. horikoshii or T. onmurineus
NA1, Thermococcus sp. 9.degree. N-7, Thermococcus sp. GI-J,
Thermococcus sp. MAR-13, Thermococcus sp. GB-C, Thermococcus sp.
GI-H, Thermus aquaticus, Thermus thermophilus, Thermus caldophilus,
Thermus filiformis, Thermus flavus, Thermotoga maritima, Bacillus
stearothermophilus, or Bacillus caldotenax.
[0089] In some embodiments, at least one of the primers comprises a
radiologically or electromagnetically detectable moiety.
Radiologically detectable moieties include radioactive isotopes
that emit detectable particles, such as beta or gamma particles,
for example, .sup.14C, .sup.3H, .sup.32P, .sup.33P, .sup.35S, and
.sup.125I. Electromagnetically detectable moieties include chemical
entities that interact with electromagnetic radiation (including
absorbance, emission, or both) in a detectable way, such as
chromophores and fluorophores, for example, fluorescein, FAM,
cyanine dyes, rhodamine dyes, etc. Exemplary fluorophores include
FAM.TM. (fluorescein), HEX.TM., TET.TM., JOE.TM., VIC.RTM.,
NED.TM., PET.RTM., ROX.TM., TAMRA.TM., and Texas Red.RTM..
[0090] In another example, repeat region sizing and genotype
reconstruction to characterize the A/T-rich segment loci of TOMM40
or fragments thereof can be generated using methods described in
U.S. Provisional Application No. 62/196,239, including the primers,
polymerase, reagents, and reaction conditions are incorporated by
reference.
II. Ladder of Amplification Products
[0091] In various embodiments, the amplification products (also
referred to herein as amplification fragments) generated from an
amplification of a nucleic acid of interest (or a portion of that
nucleic acid comprising a repeat region) is subjected to
electrophoresis, preferably capillary electrophoresis, and the size
of the repeat region is determined using a ladder of amplified
products generated by the electrophoresis. In some embodiments, the
ladder of amplified products is used as an internal standard on its
own to determine the repeat region length. The internal standard
may be calculated from the ladder of amplification products, for
example using internal sizing ladder calibration. In other
embodiments, a ladder of amplified products is used as an internal
standard to determine the size of a nucleic acid and in combination
with an external standard. In certain embodiments, the external
standard may be calculated using external sizing ladder
calibration. In additional embodiments, the internal sizing ladder
calibration (ladder of amplified products) may be used in
combination with external sizing ladder calibration (external
standard). As described in detail below, the goodness-of-fit of the
internal standard, the goodness-of-fit of the external standard,
and the consistency between the internal standard and the external
standard may also be used for sample quality control.
[0092] In methods provided herein, a ladder of amplification
products can be obtained by amplifying a region of a nucleic acid
comprising a repeat region and performing a high resolution
fragment analysis method, such as capillary electrophoresis. In
some embodiments, electrophoresis (e.g., capillary electrophoresis)
can distinguish amplification products differing by only one repeat
unit (e.g., in a CGG repeat region, amplification products
differing by only 3 nucleotides can be distinguished). In some
embodiments, the repeat unit is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides in length.
In some embodiments, the repeat unit is 2 nucleotides in length. In
some embodiments, the repeat unit is 3 nucleotides in length. In
some embodiments, the repeat unit is 6 nucleotides in length. In
some embodiments, electrophoresis can distinguish amplification
products of three nucleotides or fewer. In some embodiments,
electrophoresis can distinguish amplification products of one bp.
In some embodiments, the repeat region is a homopolymeric segment
and electrophoresis distinguishes amplification products differing
by one nucleotide in length.
[0093] In various embodiments, a repeat profile is generated from
the ladder of amplification products produced by the
electrophoresis. In some embodiments, the repeat profile is used in
sizing, e.g., sizing the repeat region or any other region of
interest in the nucleic acid being evaluated in a patient sample.
In some cases the repeat profile includes detecting the beginning
of the repeat signal. In yet other embodiments, the repeat profile
includes a start length in the electropherogram. In additional
embodiments, the repeat profile includes a repeat number.
[0094] In various embodiments, the algorithm for repeat peak
identification works in several stages. In certain embodiments, the
beginning of the repeat signal is first detected using information
about the window in which the repeat signal starts based on the
sampling frequency of the instrument. In other embodiments,
quantile-based analysis is then used to determine the range in
which the repeat signal starts and ends. In some embodiments, a
frequency-based analysis is used to determine repeat periodicity in
sampling units. In certain embodiments, the repeat periodicity is
used to inform the window size for which repeat peaks will be
called. In other embodiments, a quantile-based approach is used to
derive a threshold at which repeat peaks should be called. In some
embodiments, a sliding window is used to call single repeat peaks,
where the called peaks for each window are defined as having a
negative second-order derivative with the largest magnitude in the
range. In certain embodiments, if no peaks are found or the signal
falls below the threshold where the repeat periodicity is used to
inform the window size for which repeat peaks will be called, the
location of the repeat peak at the center of the window may be
extrapolated. In certain embodiments, as peaks are called, the size
of the window based on the difference between repeat peaks in
sampling units may be adjusted.
[0095] In various embodiments, the algorithm for repeat peak
identification works by first detecting the beginning of the repeat
signal using information about the window in which the repeat
signal starts based on the sampling frequency of the instrument. In
certain embodiments, the algorithm for peak identification first
works by detecting the beginning of the repeat signal using
information about the window in which the repeat signal starts
based on the sampling frequency of the instrument and secondly
using quantile-based analysis to determine the range in which the
repeat signal starts and stops. In other embodiments, the algorithm
for peak identification first works by detecting the beginning of
the repeat signal using information about the window in which the
repeat signal starts based on the sampling frequency of the
instrument; second, using quantile-based analysis to determine the
range in which the repeat signal starts and stops; and third, using
frequency-based analysis to determine repeat periodicity in
sampling units. In some embodiments, the algorithm for peak
identification first works by detecting the beginning of the repeat
signal using information about the window in which the repeat
signal starts based on the sampling frequency of the instrument;
second, using quantile-based analysis to determine the range in
which the repeat signal starts and stops; third, using
frequency-based analysis to determine repeat periodicity in
sampling units; and fourth, using the repeat periodicity to inform
the window size for which repeat peaks will be called. In certain
embodiments, the algorithm for peak identification first works by
detecting the beginning of the repeat signal using information
about the window in which the repeat signal starts based on the
sampling frequency of the instrument; second, using quantile-based
analysis to determine the range in which the repeat signal starts
and stops; third, using frequency-based analysis to determine
repeat periodicity in sampling units; fourth, using the repeat
periodicity to inform the window size for which repeat peaks will
be called; and fifth, using a quantile based approach to derive a
threshold at which repeat peaks will be called. In some
embodiments, the algorithm for peak identification first works by
detecting the beginning of the repeat signal using information
about the window in which the repeat signal starts based on the
sampling frequency of the instrument; second, using quantile-based
analysis to determine the range in which the repeat signal starts
and stops; third, using frequency-based analysis to determine
repeat periodicity in sampling units; fourth, using the repeat
periodicity to inform the window size for which repeat peaks will
be called; fifth, using a quantile based approach to derive a
threshold at which repeat peaks will be called; and sixth, using a
sliding window to call single repeat peaks, where the called peaks
for each window are defined as having a negative second-order
derivative with the largest magnitude in the range. In some
embodiments, if no peaks are found or the signal falls below the
threshold determined in the fourth stage; the location of the
repeat peak as the center of the window may be extrapolated. In
other embodiments, as peaks are called, the size of the window
based on the difference between repeat peaks in sampling units may
be adjusted.
[0096] In certain embodiments, a repeat element periodicity is
determined. In other embodiments, the repeat element size is
determined using a frequency-based analysis, such as Fourier
Transform analysis. In some embodiments, the methods include
converting from the instrument sampling domain, to the base-pair
repeat domain. In other embodiments, the information is used to
determine the size of each amplification product in the ladder of
amplification products. In some embodiments, the information
derived from determining the length in nucleotides of the repeat
elements in the ladder is used to generate a calibration curve to
determine the size of the repeat region. In more specific
embodiments, the repeat region capillary electrophoresis is
normalized against a ladder of amplification products to size the
repeat region. In certain embodiments, the entire repeat profile is
used for generating a calibration curve. In other embodiments,
peaks are selected for use in a calibration curve, for example
based on peak height and/or peak shape. In yet other embodiments,
peaks are selected based on qualities of the consistency of repeat
profile periodicity. In additional embodiments, consistency of
repeat profile periodicity is determined by applying a threshold to
a transformed version of the repeat peak location signal, for
example as an entropy filter or a frequency filter.
[0097] In certain embodiments, annotating device 130 may be
configured to use the entire repeat profile to generating a sizing
standard. In various embodiments, annotating device 130 may select
peaks for generating a sizing standard based on peak
characteristics. The peak characteristics may be peak height and/or
peak shape. In various embodiments, annotating device 130 may
select peaks for generating a sizing standard based on repeat
profile periodicity characteristics, such as the consistency of the
repeat profile periodicity. For example, consistency of repeat
profile periodicity may be determined by annotating device 130 by
applying a threshold to a transformed version of the repeat peak
location signal, either as an entropy filter on differences between
peaks, or as a frequency filter.
[0098] In certain embodiments, a repeat element periodicity
reflects the frequency of the repeating motif or repeating
sequence, for example, a repeat element periodicity is three base
pairs for the FMR1 genetic loci. A repeat element periodicity may
be one, two, three, four, five, or six or more depending on the
repeat length in the genetic locus. In certain embodiments, a first
amplification product is the length of the shortest amplified
product in the set of amplification products within a ladder. In
certain embodiments, the amplification product count is the number
of different length products. In certain embodiments, the
amplification number count can be corrected in accordance with the
expected periodicity. In certain embodiments, the constant element
length for each template is a fixed fragment determined by the
primer and the template.
[0099] In further embodiments, the methods involve correcting for a
signal artifact. Such signal artifacts can include contaminating
peaks, missing peaks, air bubbles, flare up signals, or
bleed-through of signal from other fluorescent channels.
[0100] One of skill in the art would readily recognize that sizing
of the repeat region involves use of the ladder of amplification
products for sizing instead of an external standard in various
embodiments.
[0101] In various embodiments, additional parameter information is
obtained from the electrophoresis electropherogram, such as the
distance in the forward and reverse directions to any interruptions
in the repeat region. In some embodiments, the repeat region size,
combined with the additional parameter information, is used to
reconstruct a genotype. In some embodiments, the reconstruction is
done on an apparatus running a machine readable medium that
evaluates all potential genotypes satisfying some of the parameter
information and identifies a solution genotype satisfying all the
parameter information. In some embodiments, the apparatus, method,
and machine readable medium for this automated reconstruction of a
genotype are those described in International Publication No.
WO/2014/015273, which is incorporated by reference in its
entirety.
III. Automated Sizing Analysis
[0102] In various embodiments, a method for automated sizing of the
repeat region is provided. In some embodiments, the automated
sizing of the repeat region first identifies and defines repeat
primer peaks and gene-specific primer peaks in the sample. In
certain embodiments, peak shape, peak magnitude, or distance
between peaks in a local window are considered. In other
embodiments, a standard curve may be generated using at least 3 of
the repeat primer peaks. In more specific embodiments, a standard
curve may be generated with all of the repeat primer peaks. In
certain embodiments, a standard curve is generated using all the
repeat primer peaks by determining the value for a set of
variables. In some embodiments, the variables may include, but are
not limited to, for example, repeat element size, base pairs
preceding the first peak, the number of repeat elements in a repeat
primer minus one multiplied by repeat element size, peak count, and
the constant element length used for complimentary priming. In
certain embodiments, a standard curve is generated by determining
the value for the following variables: -Z=repeat element size (in
bp), -X=base pairs preceding the first peak (X=[number of repeat
elements in RP primer-1]*Z), -N=peak count (sum peaks from 1-to-N)
and Anc=the constant element length used for complimentary priming
(in bp). In more specific embodiments, a standard curve is
generated using a 30CGG repeat normal male. In some embodiments,
using a 30CGG repeat male, the following variables are calculated:
-Z=repeat element size (in bp) (For AmplideX FMR1: 3 bp repeat),
-X=base pairs preceding first peak, X=[number of repeat elements in
RP primer-1] (for AmplideX FMR1: X=[5XCGGs-1]*3=12 bp), -N=peak
count (sum peaks from 1-to-N) (for the last peak in a 30CGG normal
FXS male sample N=26 (26 stutter peaks present)), -Anc=the constant
element length used for complimentary priming (in bp) (for AmplideX
FMR1 example. Constant element length is 127 bp). In some
embodiments, once the standard curve has been generated, the
automated process calculates the exact size of each n peak in the
stutter pattern: e.g., size (bp) for the N peak=[X+ZN+Anc]. In
certain embodiments, the automated process accounts for sequence
interruptions to the repeat profile by extrapolating the gap (in
bp)+X to the overall size. For example, for FMR1 and AGG
interruption: =3 bp+12 bp=15 bp. In certain embodiments, the
automated process creates a calibration curve from the repeat
primer peaks by plotting the observed CE sizes for peaks (or their
timestamp) vs. calculated size (as described above) and used the
derived linear regression function for gene-specific size
calculations.
[0103] In certain embodiments, the sizing standard may be used to
generate sizes for fragments within the repeat profile. In other
embodiments, the sizing curve may be used to generate sizes of
fragments outside of the repeat region by extrapolation.
[0104] In certain embodiments, Repeat Primed-PCR (RP-PCR) may be
used for repeat region assessment. RP-PCR may use a degenerate
primer to generate a multitude of repeat fragments, consistent with
a genotype. Some embodiments using RP-PCR may circumvent sizing and
use direct counting of stutter peaks as a method for repeat
assessment. This direct counting may use the formula: r (repeat
region count) a=N+X/Z. For example, for FMR1, 26+12/3=30CGGs.
[0105] In various embodiments, the method can be conducted using an
apparatus comprising a processor (e.g., a computer) programmed to
conduct sizing analysis. In some embodiments, the processor is
programmed to receive information (parameter information) regarding
a nucleic acid and then reconstruct a solution genotype for the
nucleic acid. In some embodiments, the parameter information is
repeat region size information. In some embodiments, the parameter
information can further comprise the distance in the forward and
reverse directions to any interruptions in the repeat region. In
some embodiments, the repeat region size and optionally the
distance in the forward and reverse directions to any interruptions
are used by the apparatus for automated reconstruction of a
genotype. In some embodiments, the apparatus also comprises a
monitor to display input information and/or the solution genotype.
In some embodiments, the solution genotype is stored electronically
on the apparatus, and/or is capable of being printed for further
diagnostic or therapeutic uses.
[0106] As described in more detail in the examples below, the
method can be used, in some embodiments, to size a genotype of the
CGG repeat region in the FMR1 gene. The 5' UTR of FMR1 can comprise
one or more CGG repeat regions, each of which may contain one or
more AGG interruptions within the region. Where more than one AGG
interruption is present, these generally do not occur contiguously
(i.e., it is rare to find a CGG repeat region comprising
(AGG).sub.n, where n is greater than or equal to 2).
IV. Samples
[0107] Various samples containing a nucleic acid of interest can be
used in the disclosed methods of sizing a repeat region or a
nucleic acid. In various embodiments, a sample is obtained from a
human or non-human animal. For example, the sample may be a patient
sample. A "patient sample" is any biological specimen from a
patient. The term sample includes, but is not limited to,
biological fluids such as blood, serum, plasma, urine,
cerebrospinal fluid, tears, saliva, lymph, dialysis fluid, lavage
fluid, semen, and/or other liquid samples, as well as cells and
tissues of biological origin. Cells and tissues may include buccal
cells, mouthwash collections, or skin cells, including hair
follicles. The term also includes cells isolated from a human or
cells derived therefrom, including cells in culture, cell
supernatants, and cell lysates. It further includes organ or tissue
culture-derived fluids, tissue biopsy samples, tumor biopsy
samples, stool samples, and fluids extracted from physiological
tissues, as well as cells dissociated from solid tissues, tissue
sections, and cell lysates. It may also include post-mortem solid
tissue samples, such as those from the brain. The term sample also
includes any other cellular or non-cellular specimen obtained from
a human or non-human animal that comprises a nucleic acid of
interest. In some embodiments, the sample contains less than about
80, 100, 150, 200, 500, 1,000, 1,500, 2,000, 2,500, 3,000, 4,000,
or 5,000 ng of the nucleic acid of interest.
[0108] In some instances, the sample includes one or more nucleic
acids of interest. The nucleic acid of interest can be genomic DNA.
The genomic DNA or other nucleic acid of interest may be separated
from other DNA and non-DNA components of the sample before being
subjected to the methods of the invention. Many methods of DNA
purification and separation are known in the art and may be used
with the disclosed methods. In some embodiments, the nucleic acid
of interest can comprise nucleic acid synthesized in vitro.
Examples of in vitro nucleic acid synthesis include an
amplification reaction such as PCR, in vitro transcription, in
vitro reverse transcription, in vitro primer extension, a
sequencing reaction, phosphoramidite-based nucleic acid synthesis,
and combinations thereof.
[0109] In some embodiments, the nucleic acid of interest in the
sample may comprise a repeat region, for example one or more
repeating GC-rich segments. In certain embodiments, the nucleic
acid of interest in the sample may comprise the FMR1 and/or FMR2
genes or fragments thereof, or at least part of the 5' UTR of FMR1
and/or FMR2 (e.g., a portion that comprises the CGG repeats of the
5' UTR of FMR1 or the CCG repeats in the 5' UTR of FMR2). In
certain embodiments, the size of the nucleic acid may be about 50,
100, 200, 300, 500, or 700 bp, or 1, 1.5, 2, 2.5, 3, 4, 5, 7, or 10
kb, or any value in between. In some embodiments, the size of the
nucleic acid may be between 50 bp and 10 kb, 100 bp and 10 kb, 200
bp and 10 kb, 300 bp and 10 kb, 500 bp and 10 kb, 700 bp and 10 kb,
1 kb and 10 kb, 1.5 bp and 10 kb, 2 bp and 10 kb, 3 by and 10 kb,
50 bp and 7 kb, 50 bp and 5 kb, 50 bp and 4 kb, 50 bp and 3 kb, 50
bp and 2 kb, 50 bp and 1.5 kb, 100 bp and 7 kb, 200 bp and 5 kb, or
300 bp and 4 kb.
[0110] In various embodiments, the nucleic acid of interest in the
sample may comprise one or more repeating A/T-rich segments, such
as a homopolymeric segment. In certain embodiments, the A/T-rich
segment is: (i) a homopolymeric segment comprising at least 10 A
residues, at least 10 T residues, or at least 10 U residues,
wherein the at least 10 A, T, or U residues are consecutive or
interrupted once by one to three other nucleotides; or (ii) a
segment comprising (T.sub.nA).sub.m, (AT.sub.n).sub.m,
(TA.sub.n).sub.m, or (A.sub.nT).sub.m, wherein n is 2 or greater
and m is such that the length of the repeating A/T-rich segment is
10 or more residues. In some embodiments, the nucleic acid template
can be known to comprise one or more repeating A/T-rich segments,
such as homopolymeric segments. The nucleic acid template can be
suspected of comprising one or more repeating A/T-rich segments
such as homopolymeric segments. In certain embodiments, the size of
the nucleic acid may be about 50, 100, 200, 300, 500, or 700 bp, or
1, 1.5, 2, 2.5, 3, 4, 5, 7, or 10 kb, or any value in between. In
some embodiments, the size of the nucleic acid may be between 50 bp
and 10 kb, 100 bp and 10 kb, 200 bp and 10 kb, 300 bp and 10 kb,
500 bp and 10 kb, 700 bp and 10 kb, 1 kb and 10 kb, 1.5 bp and 10
kb, 2 bp and 10 kb, 3 bp and 10 kb, 50 bp and 7 kb, 50 bp and 5 kb,
50 bp and 4 kb, 50 bp and 3 kb, 50 bp and 2 kb, 50 bp and 1.5 kb,
100 bp and 7 kb, 200 bp and 5 kb, or 300 bp and 4 kb.
[0111] In various embodiments, a multiplex assay can be used for
parallel analysis of more than one nucleic acid region. In some
embodiments, a multiplex PCR-reaction can be used to size at least
one repeat region of a nucleic acid. In certain embodiments, a
first and second nucleic acid region are amplified. In specific
embodiments, a second nucleic acid region is amplified. In other
embodiments, a first, a second and a third nucleic acid region are
amplified. In other embodiments, a second, and a third nucleic acid
region are amplified. In yet other embodiments, a second nucleic
acid and optionally a third nucleic acid region are amplified. In
certain embodiments, the nucleic acid region is distinct from the
template for the ladder amplification products. In some
embodiments, at least one nucleic acid region, at least two nucleic
acid regions, at least three nucleic acid regions, at least four
nucleic acid regions or at least five nucleic acid regions are
amplified. In some embodiments, the multiplex assay can be used to
size two or more genetic loci. In some embodiments, the multiplex
assay can be used to size three or more genetic loci. In some
embodiments, the multiplex assay can be used to size at least one
repeat region of FMR1 and FMR2. In other embodiments, the multiplex
assay can be used to size at least one repeat region of FMR1 and
C9ORF72. In some embodiments, the multiplex assay can be used to
size at least one repeat region of FMR2 and C9ORF72. In certain
embodiments, the multiplex assay can be used to size at least one
repeat region of FMR2 and C9ORF72. In other embodiments, the
multiplex assay can be used for disorders associated with an
expanded repeat region, for example spinocerebellar ataxia,
myotonic dystrophy or Huntington's disease. In certain embodiments,
the multiplex assay can be used with two or more fluorescent
labels.
V. Repeat Region Sizing Apparatus and Machine-Readable Medium
[0112] In various embodiments, an apparatus is disclosed for the
use in sizing one or more repeat regions and optionally in the
reconstruction of a genotype for a nucleic acid containing the
repeat region. In some embodiments, information regarding the size
or characteristics of a nucleic acid repeat region is provided to
the apparatus for use in reconstructing a genotype. In some
embodiments, the repeat region also comprises interruptions, e.g.,
AGG interruptions in a CGG repeat region. In some embodiments,
parameter information including repeat region size and distance in
the forward and reverse directions to any interruptions is provided
to the apparatus for use in reconstructing a genotype.
[0113] In various embodiments, an apparatus is disclosed to size a
repeat region of a nucleic acid sample and optionally to generate a
reconstructed genotype, for example. In some embodiments, the
apparatus comprises a processor communicatively coupled to a memory
device. In some embodiments, machine-executable instructions are
stored on the memory device that, when executed by the processor,
cause the processor to conduct repeat region sizing and genotype
reconstruction analysis. In certain embodiments, the
machine-executable instructions cause the processor to (a)
amplifying the repeat region; (b) performing high resolution
fragment analysis (c) obtaining a ladder of amplification products;
and (d) using the ladder of amplification products as an internal
standard to determine repeat region length. In certain embodiments,
the apparatus further comprises a monitor communicatively coupled
to the processor and memory device, wherein the machine-executable
instructions stored on the memory device instruct the processor to
display the solution genotype on the monitor. In some embodiments,
the apparatus further comprises a printer communicatively coupled
to the processor and memory device, wherein the machine-executable
instructions stored on the memory device instruct the processor to
print the solution genotype on the printer.
[0114] In various embodiments, the apparatus used to size a repeat
region is capable of accepting the input of parameter information
regarding a nucleic acid (e.g., a repeat profile, a repeat element
periodicity, a first amplification product, an amplification
product count, and/or a constant element length relating to the
ladder of amplification products). In some embodiments, the
apparatus is programmed to use the parameter information to
determine the size of each amplification product in the ladder of
amplification products and/or the total size of the nucleic acid.
The apparatus can be programmed to display and/or archive the
result. In some embodiments, the apparatus comprises a means for
displaying and/or archiving the result.
[0115] In various embodiments, an apparatus disclosed herein
comprises a processor and memory device, wherein the memory device
contains machine-readable instructions that instruct the processor
to accept the input of parameter information regarding a nucleic
acid and conduct sizing analysis, which can be represented by the
formula to generate a standard curve: -Z=repeat element size (in
bp), -X=base pairs preceding first peak, X=[number of repeat
elements in repeat primer-1]*Z, -N=peak count (sum peaks from
1-to-N)-Anc=the constant element length used for complimentary
priming (in bp). In some embodiments, to determine the exact size
of each n peak in the shutter pattern the following formula may be
used: size (base pairs) for the N peak=[X+ZN+Anc]. As a result,
this formula provides the size of the repeat region.
[0116] In some embodiments, the apparatus further comprises a means
to display the solution genotype (e.g., a monitor to display the
genotype visually, a data storage medium to save the genotype in a
digital format, and/or a connection for transmitting the solution
genotype to a printer or other electronic storage or display
device).
[0117] In some embodiments, the apparatus is a computer, wherein
the computer comprises a processor and a memory device having
computer code stored on it, wherein the computer code instructs the
processor to accept the input of parameter information regarding a
nucleic acid and then determine a repeat profile of the ladder of
amplification products, and thereby size the repeat region. In some
embodiments, the computer also comprises a monitor to display input
information and/or the reconstructed genotype. In some embodiments,
the reconstructed genotype is stored electronically on the computer
and/or is capable of being printed for further diagnostic or
therapeutic uses. In various embodiments, the computer comprises a
device to allow for user interaction. For example, the computer may
comprise a keyboard and/or pointing device (e.g., a mouse or a
trackball) that allows a user (such as a patient, doctor, or other
healthcare worker) to enter parameter information and/or to access
and manipulate the reconstructed genotype.
[0118] In various embodiments, the instructions to conduct sizing
of a repeat region and optional reconstruction of a genotype may be
stored on an apparatus in a machine-readable medium (e.g.,
machine-executable instructions, software, computer code, computer
programs, etc.). For example, the machine-readable medium can
comprise computer code stored in C++, C#, Java, Perl, Python,
Julia, R, Go, Ruby, Scala, Javascript or any other suitable format
for computer code. The machine-readable medium can provide
instructions to the apparatus for conducting sizing of a repeat
region using parameter information regarding a nucleic acid. In
various embodiments, the instructions on the machine-readable
medium can instruct an apparatus to (a) amplifying the repeat
region; (b) performing high resolution fragment analysis; (c)
obtaining a ladder of amplification products; and (d) using the
ladder of amplification products as an internal standard to
determine repeat region length.
[0119] In some embodiments, the instructions on the
machine-readable medium instruct the apparatus to display the size
result on a monitor. In some embodiments, the instructions on the
machine-readable medium instruct the apparatus to print the size
result on a printer.
[0120] The instructions stored on a machine-readable medium can be
any codes, symbols, or other signals that provide instructions,
information, and/or data that can be used by an apparatus (e.g., by
a processor in a computer). In some embodiments, the instructions
stored on the machine-readable medium encode a program that
instructs the apparatus to receive parameter information regarding
a nucleic acid, conduct analysis to size the repeat region, and
store or transmit the size of the nucleic acid.
[0121] In some embodiments, the instructions stored on the
machine-readable medium instruct the apparatus to execute a repeat
region sizing analysis program. In some embodiments, the program
includes instructions to display and/or archive the size of the
nucleic acid (e.g., to display the size on a monitor, to save the
size to a data storage medium, and/or to transmit the size to a
printer or other electronic storage or display device).
[0122] In some embodiments, the instructions stored on the
machine-readable medium further encode a user interface that
provides a graphical display on a monitor. In some embodiments, the
interface allows a user to enter parameter information regarding a
nucleic acid (e.g., by allowing the user to upload a data file or
by allowing the user to enter information into display fields shown
on the user interface). In some embodiments, the user interface
provides the user with options for analyzing the parameter
information, such as various methods for displaying and/or saving
the input data and/or size result (e.g., by displaying the data on
the user's monitor, sending the data to a specified electronic
device or electronic address, printing, and/or saving the data to a
particular location).
[0123] In various embodiments, a nucleic acid size can be stored as
data in a storage medium physically connected to the apparatus
(e.g., on an internal memory device such as a hard drive on a
computer) and/or stored on a remote storage device that is
communicatively connected to the apparatus (e.g., by a wired or
wireless intranet or internet connection and the like). In some
embodiments, the user interface provides the user with options for
automatically storing the size in a particular location, printing
the size, and/or sending the size to a specified electronic device
or electronic address (e.g., to the email address of the medical
professional that requested the nucleic acid size).
VI. Methods of Use
[0124] In various embodiments, methods disclosed above can be used
to detect an expanded repeat region, for example, a GC-rich region
or A/T-rich repeat region, and/or to diagnose a disorder in a
patient, or to diagnose a risk of a disorder in offspring of the
patient. In some embodiments, the methods can be used to diagnose,
diagnose a risk, or treat a genetic disorder associated with a
repeat region, comprising, for example, (1) obtaining a sample from
a patient; (2) isolating a nucleic acid from the sample that has
one or more repeat regions, such as a region comprising CGG or CCG
repeats or a repeating A/T-rich segment; (3) amplifying a region of
the nucleic acid that has one or more repeat regions; (4)
performing capillary electrophoresis; (5) obtaining a ladder of
amplification products; (6) using the ladder of amplification
products as an internal standard to determine repeat region length.
In some embodiments, the method can further comprise detecting any
interruptions in the repeat region, and determining the distance in
the forward and reverse directions to the interruption. In some
embodiments, the repeat region size and optionally the distance in
the forward and reverse directions to any interruptions are used to
reconstruct a genotype. In some embodiments, the repeat region size
and optionally the reconstructed genotype are used to detect an
expanded repeat region or to diagnose a genetic disorder associated
with an expanded repeat region, for example, a GC-rich or A/T-rich
region, such as homopolymeric segments. In some embodiments, the
repeat region length and/or reconstructed genotype is used to
predict the risk of a genetic disorder in a patient or an offspring
of the patient. In some embodiments, the repeat region length
and/or reconstructed genotype are used to detect a genetic disorder
in a patient. In some embodiments, the repeat region length and/or
reconstructed genotype are used to detect a risk of a genetic
disorder in offspring of the patient. In certain embodiments, the
methods include making a suitable treatment decision based on the
repeat region length and/or reconstructed genotype (e.g., providing
pregnancy counseling and/or fertility treatment). In some
embodiments, the methods include administering a suitable treatment
to a patient identified as having a genetic disorder based on the
repeat region length and/or reconstructed genotype.
[0125] For example, the method can comprise isolating an FMR1 or
FMR2 nucleic acid or fragments thereof from a patient sample,
amplifying the CGG or CCG-rich repeat region, performing capillary
electrophoresis to obtain a ladder of amplification products, and
using the ladder of amplification products to determine the CGG or
CCG-rich repeat region size in order to diagnose and/or predict the
risk of and/or make treatment decisions regarding disorders
associated with an expanded FMR1 or FMR2 allele. For instance, a
size of greater than 200 CGG or CCG repeats can be used to detect
Fragile X syndrome or Fragile X (FRAXE) mental retardation in a
patient, or a range of a size of greater than 35-45 CGG or CCG
repeats can be used to detect the risk of Fragile X syndrome or
Fragile X (FRAXE) mental retardation in offspring of the patient.
In some embodiments, the method also comprises detecting the
distance in the forward and reverse directions to any interruptions
in the CGG or CCG-rich repeat region, reconstructing a genotype for
the FMR1 or FMR2 allele using the repeat size and interruption
information, and using the reconstructed genotype to detect
disorders associated with an expanded FMR1 or FMR2 allele.
[0126] Numerous genes and genomic regions comprise repeat regions,
including those comprising GC-rich or A/T-rich regions, which are
associated with genetic disorders, making them potential diagnostic
and therapeutic targets. Accordingly, in various embodiments the
methods of sizing a repeat region disclosed herein can be used for
these genetic loci and can be used to diagnose, prognose, treat,
and/or guide treatment decisions for the associated genetic
disorders. In some embodiments, the methods of sizing a repeat
region disclosed herein can be used to analyze the FMR1 or FMR2
genes. In some embodiments, these methods can assist in the
diagnosis of FXS, FRAXE, FXTAS, FXPOI, and dopamine-responsive
Parkinsonism, which are associated with the length of CGG repeat
regions in the 5' UTR of FMR1 and CCG repeat regions in the 5' UTR
of FMR2. For example, a reconstructed FMR1 genotype having greater
than about 45 CGG repeats, and particularly a genotype having
greater than about 200 CGG repeats, in the 5' UTR can be used to
diagnose FXS and associated disorders, as well as to diagnose the
risk of the disorders in offspring of the patient.
[0127] In further embodiments, the methods of sizing a repeat
region may be used to detect genotypes associated with other
disorders of expanded repeat regions, such as spinocerebellar
ataxia type 1, spinocerebellar ataxia type 2, spinocerebellar
ataxia type 3, spinocerebellar ataxia type 6, spinocerebellar
ataxia type 7, spinocerebellar ataxia type 8, Friedrich's ataxia,
progressive myoclomus epilepsy, myotonic dystrophy I, myotonic
dystrophy II, Huntington's disease, spinobulbar muscular atrophy,
Dentatorubropallidoluysian atrophy, spinocerebellar ataxia,
amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD),
and Alzheimer's disease. Genetic loci associated with these
conditions are known in the art and include, without limitation,
SCA1, SCA2, SCA3, CACNA1A, SCAT, SCA8, X25, CSTB, C9ORF72, DMPK,
ZNF9, HTT, AR, ATN1, ATXN1-3, ATXN7, ATXN10, CACNA1A, SCA8,
PPP2R2B, CNBP, TBP and TOMM40. See, e.g., Nat Genet. 1996 May;
13(1):105-8; Nat Genet. 1996 May; 13(1):109-13. Hyperexpansion
and/or hypermethylation of the GC-rich and/or repeat regions at
these loci are associated with the diseases, and detection of these
mutations and expansions using the methods disclosed herein can be
used as part of treatments or to guide treatments for the detected
conditions.
[0128] Table 1 shows examples of genetic loci that can be used with
the methods disclosed herein, and the relationship between repeat
regions in those loci and disease genotypes or phenotypes. In
certain embodiments, the methods detect repeat lengths of greater
than 20, 30, 35, 40, 50, 100, 110, or 200 repeats within a repeat
region allele.
TABLE-US-00002 TABLE 1 Genetic loci that may be used with the
methods disclosed herein, and the relationship between repeat
regions in those loci and disease genotypes or phenotypes. Repeat
number Repeat Repeat Disease Gene Normal Mutant position variant
Fragile X syndrome FMR1 (CGG) < 45 (CGG) > 200 5'-UTR AGG
Fragile X (FRAXE) FMR2 (CCG) < 35 (CCG) > 200 5'-UTR CTG
mental retardation Myotonic dystrophy DMPK (CTG) < 35 (CTG) >
50 3'-UTR CCG, CTC Spinocerebelllar SCA8 (CTG) < 40 (CTG) >
110 Antisense CCG, CTA, CTC, ataxia type 8 RNA CCA or CTT
Friedrich's ataxia X25 (GAA) < 35 (GAA) > 100 Intron 1 GGA,
GAG Spinobulbar muscular AR (CAG) < 30 (CAG) > 40 Coding
atrophy Huntington disease IT15 (CAG) < 40 (CAG) > 40 Coding
Dentatorubral DRPLA (CAG) < 35 (CAG) > 50 Coding
pallidoluysian atrophy Spinocerebelllar SCA1 (CAG) < 40 (CAG)
> 40 Coding CAT ataxia type 1 Spinocerebelllar SCA2 (CAG) <
30 (CAG) > 35 Coding CAA ataxia type 2 Spinocerebelllar SCA3
(CAG) < 40 (CAG) > 40 Coding ataxia type 3 Spinocerebelllar
CACNA1A (CAG) < 20 (CAG) > 20 Coding ataxia type 6
Spinocerebelllar SCA7 (CAG) < 40 (CAG) > 40 Coding normal
allele has ataxia type 7 no interruption Progressive myoclomus CSTB
(C.sub.4GC.sub.4GCG (C.sub.4GC.sub.4GCG Promoter epilepsy type (SEQ
ID NO: (SEQ ID NO: 10)) < 3 10)) > 50 Alzheimer's Disease
TOMM40 Amyotrophic lateral C9ORF72 (GGGGCC) < 30 (GGGGCC) >
30 sclerosis and Frontotemporal Dementia Myotonic dystrophy CNBP
(CCTG) < 26 (CCTG) > 75 Intron 1 type II (ZNF9)
[0129] For example, sizing a repeat region and/or reconstructing a
genotype can be used to detect genotypes associated with disorders
of SCA1 or SCA2, such as Spinocerebelllar ataxia types 1 and 2,
which are associated with expansion of their CAG repeat regions.
For example, sizing the repeat region can provide information
regarding the total length of one or more CAG repeats in the SCA1
or SCA2 genes, as well as the distance in the forward and reverse
directions to the CAT or CAA interruptions in the CAG repeats.
Sizing the repeat regions, using the total length of the one or
more CAG repeats and either the distance in the forward or reverse
direction to any interruptions, can be applied to generate a set of
potential genotypes for the SCA1 or SCA2 gene. Sizing the repeat
region can be used to detect a mutation or a genotype, or to
diagnose or assist in diagnosing, an SCA1 or SCA2 related mutation,
genotype, or disorder, as well as to treat and guide treatment
decisions for the disorder.
[0130] In other embodiments, the methods of sizing a repeat region
and/or reconstructing a genotype may be used to detect other
disorders associated with expanded repeat regions, such as
disorders associated with a repeating A/T-rich segment. In some
embodiments, the disorder is a neurodegenerative disease. In some
embodiments, the neurodegenerative disease is Alzheimer's disease.
The Alzheimer's disease can be late-onset Alzheimer's disease.
Other genetic loci associated with a repeating A/T-rich segment are
known in the art, for instance the gene TOMM40. In some
embodiments, the repeat locus being assessed is all or a portion of
intron 6 of TOMM40. In some embodiments, the portion of intron 6 of
the TOMM40 gene being assessed contains a poly-T repeat
polymorphism (re 10524523).
Automatic Sizing Analysis
[0131] FIG. 2 illustrates an exemplary process for automatically
analyzing genomic repeat regions. This process may comprise the
steps of signal preprocessing 201, generating a sizing standard
203, and gene product sizing 205. As would be appreciated by one of
skill in the art, additional steps may be performed, steps may be
removed, and the order of the steps may vary without departing from
the envisioned embodiments. Annotation device 130 may be configured
to perform these steps, consistent with disclosed embodiments.
[0132] In step 201, annotation device 130 may be configured to
receive and pre-processes raw data. The raw data may be received
from CE device 120, or from another device. For example, annotation
device 130 may be configured to retrieve the raw data from a
storage device. The raw data may be received in data files that
store signal intensities for each channel of the CE experiment.
These data files may store the signal intensities in a JSON-based
format.
[0133] In some embodiments, the PCR assay may be run in conjunction
with the Applied Biosystems family of Genetic Analyzer instruments
(3130/3500/3700), all of which export data in a proprietary format
maintained by Applied Biosystems. This format is referred to as the
Fragment Sequence Analysis (FSA) format and contains fluorescence
data from capillary electrophoresis (CE) experiments, encoded
according to a proprietary set of specifications. Annotation device
130 may be configured to directly access this file format. For
example, Annotation device 130 may use a parser designed to decode
and organize the information in the files into a j son-based format
for programmatic access and manipulation. This parser may use an
open-source module for the perl programming language called
Bio::Trace::ABIF (licensed as free software). This parser has been
validated on >1000 samples run across different Genetic Analyzer
Instruments (3130/3500/3700), and has been shown to be exactly
concordant with unprocessed fluorescence data viewed through
GeneMapper, the current standard for accessing the FSA format. The
output of the parser may comprise data files which may store signal
intensities for multiple channels of the CE experiment. These data
files may store the signal intensities in a JSON-based format.
[0134] At least one channel of the data file may correspond to the
ladder of amplification products. This channel may include a repeat
profile. In some embodiments, another channel of the data file may
correspond to a ladder of products having known sizes. For example,
this other channel of the data file may correspond to an external
ladder, such as a ROX ladder.
[0135] In some embodiments, annotation device 130 may be configured
to detect the region of interest in the data file in which the
repeat profile exists. Annotation device 130 may dynamically
determine the periodicity of the repeat profile using a frequency
based analysis. Annotation device 130 may dynamically determine a
threshold for calling repeat peaks in the repeat profile. The
threshold may be an amplitude threshold. Annotation device 130 may
call the repeat peaks in the repeat profile using a sliding window.
Annotation device 130 may interpolate repeat peaks in the repeat
profile below the threshold. This interpolation may improve the
accuracy of the sizing standard generated by annotation device 130.
Annotation device 130 may use the estimated starting peak location
in the repeat profile and subsequent peak location to generate a
sizing standard that maps from sampling units to base pair
units.
[0136] Preprocessing may normalize the parsed data, adjusting for
differences across samples run with different configurations. For
example, the PCR assay uses a CE-based readout for data
interpretation, and may be subject to convolution by signal
artifacts that are systematically present on CE instruments. Each
channel of the parsed data may be filtered by annotation device 130
to simplify and increase the robustness of the downstream data
processing. In some aspects, a low-pass filter or band-pass filter
may be applied to one or more of the channels in order to smooth
the data. The low-pass filter may be a Butterworth, Savitzky-Golay,
a moving average, or other similar filter. Each channel may be
normalized by annotation device 130 to account for improper
instrument calibration and/or variability in instrument
configuration across labs, as baseline fluorescence values for each
channel in an CE device must be continuously calibrated throughout
the lifetime of the device. Annotation device 130 may be configured
to re-calibrate a channel by subtracting a value from the channel.
This value may be a statistic of the signal intensities of the
channel. For example, the value may be the 10.sup.th percentile of
signal intensities of the channel. The 10.sup.th percentile may
robustly represents the lower values in the signal, without being
affected by commonly encountered sharp negative fluctuations in
signal intensity. In the equations below, let s(x, c) represent the
signal intensity at location x for the instrument channel c:
b(c)=Q.sub.10(s(x,c))
s.sub.norm(x,c)=s(x,c)-b(c)
[0137] Annotation device 130 may be configured to remove artifacts
arising from artifacts such as air bubbles or contaminants during
signal pre-processing. Air-bubbles present in capillary tubes
during a CE experiment may produce large spikes in signal
intensity. These spikes may be erroneously interpreted as
gene-specific products or ROX channel sizing peaks, producing
incorrect results. However, fluorescence from air-bubbles affects
all of the channels to a similar degree of magnitude, enabling
annotation device 130 to identify and remove air-bubble
artifacts.
[0138] In a first step, annotation device 130 may be configured to
find the locations of all peaks exceeding 50 RFU across all
channels in the parsed data. Annotation device 130 may be
configured to determine candidate air-bubble artifact locations as
the intersection of peak indices occurring across multiple
channels. For example, the intersection of peak indices in channels
({FAM, HEX, NED, ROX}) may be represented as follows:
C=P(FAM).andgate.(HEX).andgate.P(NED).andgate.P(ROX)
[0139] Annotation device 130 may be configured to determine whether
an air-bubble artifacts exists at each candidate location. As a
first step, annotation device 130 may be configured to identify a
window in the channel including a potential air bubble location. In
some embodiments, annotation device 130 may be configured to
determine left and right shoulders [h.sub.il,h.sub.ir] of the
signal intensity peak at the candidate location. In the equation
below, S(i, c) may be a function of the signal intensity between
the left and right shoulders for channel c, and i may represent the
candidate peak locations.
S(i,c)=s([h.sub.il,h.sub.ir],c)
[0140] As a second step, annotation device 130 may be configured to
determine a correlation between signal intensities across the
multiple channels within the window. This correlation may be a
pairwise rank correlation significance test or any other measure of
similarity comparing signal intensities across channels:
PC(i)={p.sub.prank(S(i,X),S(i,Y))|X,Y.epsilon.CHCH}
[0141] Here the set of instrument channels tested is CH, and PC(i)
is the set of pairwise rank correlation values for candidate peak
location i.
[0142] Annotation device 130 may be configured to determine an air
bubble artifact exists at a candidate location when the rank
correlation significance test generates a significance value less
than a significance threshold across all pairwise comparisons:
B={i|i,max PC(i)<T}
[0143] Here, B indicates the candidate locations with an air bubble
artifact, and T is the significance threshold. T may be between
0.0001 and 0.01, for example a significance threshold of 0.005 has
been empirically verified using an independent training
dataset.
[0144] Annotation device 130 may be configured to replace the air
bubble artifact. In some aspects, the signal intensities for the
channels within the window may be replaced by annotation device 130
with simulated noise. The simulated noise may be Gaussian noise,
with mean and standard deviation determined by annotation device
130 using signal intensities for the region surrounding the air
bubble:
bkg(i,c)=s([h.sub.il-d,h.sub.il],c).orgate.s([h.sub.ir,h.sub.ir+d],c)
.mu.(i,c)=mean bkg(i,c)
.sigma.(i,c)=std bkg(i,c)
s([h.sub.il,h.sub.ir],c).about.(.mu.(i,c),.sigma.(i,c))
[0145] Here bkg(i, c) is the set of values for the regions
surrounding the air bubble. In this example, the regions extend d
location units from the left and right peak shoulders. As a
non-limiting example, d may be between 5 and 50.
[0146] Annotation device 130 may be configured to extend the
dynamic range of a channel in the data file. In some embodiments,
the channel may be configured to detect a first electromagnetically
detectable moiety. For example the channel may be the FAM channel.
Annotation device 130 may extend the dynamic range of the channel
by extrapolating peak shape over regions of signal saturation.
Saturation occurs may occur an electromagnetically detectable
moiety fluoresces with a luminescence greater than the collection
limit of the instrument RFU sensors, resulting in a loss of
information on peak shape. However, since the wavelength spectra
for collection allows for bleed-over across channels, peak shape
for saturated regions can be extrapolated from channels capturing
fluorescence at a similar wavelength.
[0147] In a first step, annotation device 130 may be configured to
identify a window in the channel. The window may include a
saturated region of the channel. For example, annotation device 130
may determine regions of the channel in which signal intensities
exceed an amplitude threshold. This amplitude threshold may be
empirically-derived, and may be instrument-specific. In the
equations below, let s(x, c) represent the signal intensity at
index x for instrument channel c, let L represent the set of all
location indices meeting saturation criteria, and let T be some
instrument-specific threshold:
L={x|x,s(x,c)>T}
[0148] In some aspects, c may comprise the FAM channel. In various
aspects, T may be between 1000 and 40000 RFUs, with T describing
the RFU levels at which saturation occurs. Annotation device 130
may be configured to modify the signal intensities at indices in L.
For example, annotation device 130 may determine combined signal
intensities using the signal intensities within the window for the
channel and signal intensities within the window for one or more
other channels in the data file. These other channels may be
configured to detect other electromagnetically detectable moieties.
For example, these other channels may be the NED channel or the HEX
channel. The signal intensities may be combined linearly or
non-linearly. In some embodiments, determining the combined signal
intensities comprises extrapolating the shape of a calculated peak
in the first channel. For example, the combined signal intensities
may comprise a linear combination of the signal intensities within
the window for the channels. The combined signal intensities may
further include an offset. As a non-limiting example, annotation
device 130 may be configured to combine the RFU values from the NED
channel into the FAM channel:
s ext ( x , c ) = { s ( x , FAM ) + s ( x , NED ) , x .di-elect
cons. L s ( x , FAM ) , otherwise ##EQU00001##
[0149] As shown in the preceding example, annotation device 130 may
be configured to replace the signal intensities for the channel
within the window with the combined signal intensities.
[0150] Annotation device 130 may be configured to generate a sizing
standard in step 203, consistent with disclosed embodiments.
Annotation device 130 may be configured to use this sizing standard
to converting from location units in the signal (analogous to
distance travelled in POP7 gel) into base-pair sizes. The sizing
standard may comprise data or instructions stored in a
non-transitory memory. As described below, annotation device 130
may be configured to use at least one of an internal sizing
standard and an external sizing standard to generate this overall
sizing standard. In some embodiments, annotation device 130 may be
configured to output the sizing standard to a display, printer,
another component of system 100 (e.g., Laboratory Information
Management System 140), or another system.
[0151] Annotation device 130 may be configured to identify and size
genotype peaks in step 205, consistent with disclosed embodiments.
In this step, annotation device 130 may generate a background model
that compensates for the differing effects of the various primer
sets in the assay on the measured signals. This background model
may be used by annotation device 130 to identify at least one
gene-specific product peak in a measured signal.
[0152] Annotation device 130 may also be configured to size at
least one of the repeat region and the at least one gene-specific
product peak using a sizing standard. Annotation device 130 may use
the sizing standard generated in step 203. This sizing standard may
be an internal sizing standard, an external sizing, or, a combined
sizing standard generated from both the internal sizing standard
and the internal sizing standard or another sizing standard. For
example, annotation device 130 may also be configured to use a
sizing standard retrieved from a storage device, received from
another component of system 100, or received from another system.
In some embodiments, annotation device 130 may be configured to
output an indication of the at least one gene-specific product
peak, and/or the size of the repeat region to a display, printer,
another component of system 100 (e.g., Laboratory Information
Management System 140), or another system. This output may include
an indication of a genotype of the patient providing the original
genomic sample.
[0153] FIG. 3A depicts an exemplary external sizing ladder for use
in generating an external sizing standard. As described above,
annotation device 130 may be configured to generate a sizing
standard in step 203. The current gold-standard for fragment sizing
in CE experiments requires the use of externally added dye-labelled
molecules of known sizes, which produce fluorescence peaks in a
band outside of the frequency spectrum produced by the other
electromagnetically detectable moieties used in the PCR assay
(e.g., AmplideX.RTM. FMR1 PCR products). These fluorescence peaks
may be identified independently of target products generated by the
assay. In some aspects, these peaks may be used by annotation
device 130 to generate an external sizing standard that relates
location in the FSA signal (in sampling units) to fragment size (in
base pairs). Annotation device 130 may be configured to
automatically identify and label ROX fluorescence peaks, while
detecting artifacts that might otherwise result in mislabeled peaks
(e.g., when using GeneMapper-based workflows, or similar software).
The labeling systems and methods used by annotation device 130 may
to extend arbitrary sizing ladders (e.g., ROX 1000 or ROX 200)
envisioned for used in future assays.
[0154] In a first step, annotation device 130 may be configured to
identify artifacts arising in a channel in the data file. This
channel may be associated with the externally added dye-labelled
sizing ladder molecules of known sizes. The artifacts may be
"bleed-over" artifacts.
[0155] When signal intensities in a channel corresponding to a PCR
product exceed an amplitude threshold, the PCR product may be
fluorescing with sufficient intensity to affect other channels.
Thus annotation device 130 may be configured to first identify a
potential bleed-over locations based on signal intensities in the
channel corresponding to the PCR product. These locations may have
signal intensities exceeding an instrument-specific threshold. This
instrument-specific threshold may be empirically determined. It may
be otherwise estimated. In the equations below, let s(x, c)
represent the signal at the index x for the instrument channel c,
let T (instrument) be an instrument-specific threshold, and let B
represents the set bleed-over location indices in c.
f ( x , c ) = .DELTA. 1 sgn ( .DELTA. 1 s ( x , c ) ) ##EQU00002##
B = { x | f ( x , c ) = - 2 s ( x , c ) > T ( instrument ) T (
instrument ) = { 20000 , instrument = 35 XX , 37 XX 8000 ,
instrument = 31 XX ##EQU00002.2##
[0156] In some aspects, c may be the FAM channel. The RFU values
and instruments listed above are exemplary, and not intended to be
limiting. Similar values may be used for the same instructions, and
additional values may be determined for similar instruments.
[0157] In a second step, annotation device 130 may be configured to
determine the extent of the bleed-over from a first channel into a
second channel. In some embodiments, annotation device 130 may
determine windows including the bleed-over locations. These windows
may be determined by annotation device 130 based on signal
intensities in the first channel. For example, annotation device
130 may define a window as the region between left and right peak
shoulder locations surrounding a bleed-over locations. The left and
right peak shoulder locations may be determined by annotation
device 130 by assessing the noise profile in regions to the left
and right of the peak, and then use parameters from the noise
profile to determine a threshold for which the peak signal deviates
significantly from the noise. In some embodiments, the noise
profile is assumed to follow a Gaussian distribution, and peak
shoulders are labelled as the point at which the signal deviates 2
standard deviations above the mean value. As would be appreciated
by one of skill in the art, other amplitude threshold values or
noise distribution models may be used. In some aspects, the left
and right noise profiles may be parameterized independently. The
equations below describe a non-limiting example of this process for
bleed-over location i:
bkg.sub.l(i)=s([i-60,i-30],c)
bkg.sub.r(i)=s([i+30,i+60],c)
.mu..sub.l(i)=mean bkg.sub.l(i),.mu.r(i)=mean bkg.sub.r(i)
.sigma..sub.l(i)=std bkg.sub.l(i),.sigma..sub.r(i)=std
bkg.sub.r(i),
h.sub.il=min({x,x.epsilon.[i-d,i]s(x,c)>.mu..sub.l(i)+2.sigma..sub.l(-
i)})
h.sub.ir=max({x,x.epsilon.[i,i+d]s(x,c)>.mu..sub.r(i)+2.sigma..sub.r(-
i)})
[0158] Here c is the first channel, and may be the FAM channel or
another channel, and d may be between 30 and 100 sampling
units.
[0159] In a third step, annotation device 130 may be configured to
simulate noise over the region between the left and right peak
shoulders. This simulated noise may be Gaussian with parameters
chose by annotation device 130 to mimic the signal background in
the second channel. The equations below describe a non-limiting
example of this process for bleed-over location i:
bkg(i)=s([h.sub.il-d.sub.2,h.sub.il],ROX).orgate.s([h.sub.ir,h.sub.ir+d.-
sub.2],c.sub.2)
.mu.(i)=mean bkg(i)
.sigma.(i)=std bkg(i)
s([h.sub.il,h.sub.ir],c.sub.2).about.(.mu.(i),.sigma.(i))
[0160] Here c.sub.2 is the second channel, and may be the ROX
channel or another channel, and d.sub.2 may be between 5 and 50
sampling units. In a fourth step, annotation device 130 may be
configured to replace the signal intensities within the window for
the other channel with the simulated noise.
[0161] FIG. 3A depicts channel 300, the output of the bleed-in
location removal, which may comprise signal intensities 301 (in
RFUs) over a range of sampling units. Channel 300 may correspond to
the ROX PCR products. As shown, signal intensities 301 may include
actual peaks and artifacts (e.g., artifacts 315 and 317).
Annotation device 130 may be configured to identify peaks in
channel 300 when the peaks exceeding a local noise threshold. This
identification may include removing peak artifacts that are likely
false-positive peak calls.
[0162] In a first step, annotation device 130 may be configured to
determine potential peaks in the channel that exceed an amplitude
threshold calculated using a sliding window. This sliding window
may be run across signal intensities 301 by annotation device 130,
and may be between 250 and 750, sampling units wide. For example,
the sliding window may be 500 sampling units wide. Annotation
device 130 may determine statistics, such as mean and standard
deviation, of signal intensities 301 within the window. Annotation
device 130 may determine the potential peaks as those exceeding an
amplitude threshold based on the determined statistics. For
example, annotation device 130 may identify peaks over 3 standard
deviations above the mean noise level.
[0163] In a second step, annotation device 130 may be configured to
identify false positive peaks caused by "shoulder" artifacts. To
identify false positive peaks annotation device 130 may select only
the largest of nearby peaks. For example, annotation device 130 may
compare peak height within an interval including a potential peak.
Annotation device 130 may determine the potential peak is smaller
than another potential peak in the interval. Thus annotation device
130 may therefore determine the potential peak is a false positive
peak, and may exclude this potential peak from the peaks in the
channel for subsequent analysis. The interval may be between 25 and
75 sampling units wide.
[0164] Annotation device 130 may be configured to associate the
peaks in the channel with fragment sizes to generate an external
sizing standard, consistent with disclosed embodiments. In some
embodiments, annotation device 130 may use an iterative approach to
choose the most likely peaks in the channel for association with
fragment sizes. For example, annotation device 130 may iteratively
re-estimate, using identified peaks in a first region of the
channel, a linear relationship between peak location and fragment
size using a first set of fragment sizes and a first set of
corresponding peak locations. This approach takes advantage of the
low-noise profile associated with larger fragment sizes for
selecting initial conditions. As would be recognized by those with
skill in the art, annotation device 130 may be alternatively
configured to associate peaks with fragment sizes using an
optimization routine that minimizes residuals in accordance with a
model applied to the data. This optimization routine can
iteratively include and remove peaks for consideration in the model
based on criteria that quantify goodness-of-fit for the model.
[0165] In a first step, annotation device 130 may be configured to
automatically associate expected fragments sizes greater than a
predetermined base-pair length with the furthest (by distance in
the capillary) peaks in the channel. For example, the last peak
location (e.g., peak location 321) may be automatically associated
with the largest expected fragment size (e.g., fragment size 323).
The next largest peak may be associated with the next largest
expected fragment size. The predetermined base-pair length may be
approximately 500 base pairs.
[0166] In a second step, annotation device 130 may be configured to
fit a model to labeled peaks within a predetermined range of base
pairs. As would be appreciated by one of skill in the art, values
expressed in terms of base pairs may also be expressed in terms of
repeat numbers, and the expression of values in terms of base pairs
is not intended to be limiting. For example, the linear sizing
ladder may be generated by annotation device 130 by fitting a
1.sup.st order least-squares regression to the labeled peaks within
the predetermined range. The predetermined range may begin between
350 and 550 base pairs, and may end between 650 and 750 base pairs.
The model may enable annotation device 130 to convert sampling
units into base pair lengths and/or repeat numbers.
[0167] In a third step, annotation device 130 may be configured to
iteratively re-estimate this model using identified peaks in first
region 310 of channel 300. In some embodiments, the relationship
between sampling unit and base pair may be linear over first region
310. This region of the channel may include peaks corresponding to
fewer than a predetermined number of base pairs. Annotation device
130 may be configured to iteratively re-estimate the linear
relationship between peak location and fragment size using a first
set of fragment sizes and a first set of corresponding peak
locations. Annotation device 130 may progress from larger fragment
sizes to smaller fragment sizes, including progressively smaller
fragment sizes into the set of fragment sizes and set of
corresponding peak locations used to re-estimate the linear
relationship. For example, when fragment size 313 is the next
largest fragment size, annotation device 130 may be configured to
add fragment size 313 and corresponding peak location 311 to the
first set of fragment sizes and first set of corresponding peak
locations. Annotation device 130 may then re-estimate the linear
relationship between peak location and fragment size using fragment
size 313 and the larger fragment sizes in first region 310, and
peak location 311 and the larger peak locations in first region
310.
[0168] In each iteration, in some embodiments, annotation device
130 may be configured to determining the peak location
corresponding to the next fragment size using the current linear
relationship. In some embodiments, annotation device 130 may
determine a predicted peak location using the next fragment size
and current linear relationship. This next fragment size may be one
of the progressively smaller fragment sizes and the current linear
relationship may be one of the re-estimated linear relationships
discussed above. Annotation device 130 may then determine a window
including the predicted peak location. This window may include
between 5 and 50 sampling units on either side of the predicted
peak location. Annotation device 130 may determining an actual peak
location in the channel within a window including the predicted
peak location. This actual peak location may be determined as
described above, using the derivative of the sign of the derivative
of signal intensities 301. Annotation device 130 may be configured
to include the actual peak location into the first set of
corresponding peak locations. In this manner, as the actual peak
locations vary, the current linear relationship may be
appropriately updated. Furthermore, this method may provide an
additional way to identify artifacts in the data. For example,
artifact 317 does not fall within a window (e.g., window 319), and
so annotation device 130 may skip this artifact 317 when
re-estimating the linear relationship. Thus estimation of the
linear relationship may be improved.
[0169] In this manner annotation device 130 may continuously update
the linear relationship with new data points in a way that
increases the accuracy of peak association in the noisier region of
the gel. This iterative approach has been shown to be more specific
than current methods (GeneMapper, GeneMarker) in ignoring signal
artifacts in a ROX channel that can contribute to improper sizing
ladder parameterization, and is also robust to mistaking
primer-dimer peaks as ROX fragment peaks.
[0170] Annotation device 130 may be configured to determine a
non-linear relationship between peak location and fragment size for
second region 320. In some embodiments, this non-linear
relationship may comprise a spline model, such as a 1st-, 2nd- or
3rd-order spline model, or a 2nd- or 3rd-order polynomial model.
Annotation device 130 may determine the non-linear relationship
using a set of fragment sizes and a set of corresponding identified
peak locations in second region 320, such as fragment size 323 and
peak location 321. In some aspects, as shown in FIG. 3A, first
region 310 and second region 320 may not overlap. For example, the
lower bound of second region 320 may equal the upper bound of first
region 310. The second region 320 may include fragments greater
than 650 to 750 base pairs.
[0171] As shown in FIG. 3B, annotation device 130 may be configured
to generate external sizing standard 350 by combining a linear
relationship 331 for first region 330 and a nonlinear relationship
341 for second region 340. To generate external sizing standard
350, annotation device 130 may generate additional points by
resampling linear relationship 331 and the nonlinear relationship
341. A univariate spline model is fit to these additional points to
generate external sizing standard 350. In some embodiments the
additional points may be evenly-spaced along the external sizing
standard, for example differing by a constant number of base pairs.
The number of additional points may be substantially larger than
the original number of fragment sizes. For example, 2 to 10 times
as many additional points may generated than the original number of
fragment sizes. For example, between 40 and 200 additional points
may be used.
[0172] In some alternative embodiments, annotation device 130 may
be configured to estimate a sizing standard satisfying an
optimality criteria. In some aspects, annotation device 130 may
associate peaks with fragment sizes using an optimization routine
that minimizes residuals in accordance with a model applied to the
data. This optimization routine can iteratively include and remove
peaks for consideration in the model based on criteria that
quantify goodness-of-fit for the model.
[0173] For example, annotation device 130 may be configured to
generate two or more subsets of the potential peaks in the channel.
This generation may be deterministic, or may be at least partially
random. As a non-limiting example, each subset may include the
first peak. Annotation device 130 may be configured to determine a
sizing standard for each subset. These sizing standards may
comprise linear relationships, or non-linear relationships, such as
spline models or 2.sup.nd or 3.sup.rd order polynomial models. In
some aspects, annotation device 130 may be configured to resample
the sizing standards to generate comparison points. Annotation
device 130 may be configured to calculate a cost function for at
least some of the resampled sizing standards, based on a comparison
between the resampled sizing standards and a reference model. The
reference model may comprise an expected sizing standard. As would
be appreciated by one of skill in the art, sizing standards
generated using subsets including artifacts may differ greatly from
the reference model. The value of the cost function for these
models will likely be larger than the value of the cost function
for subjects including few, or no, artifacts. The cost function may
include the L1 norm, the L2 norm, or other cost functions known to
one of skill in the art. Thus annotation device 130 may be
configured to use the sizing standard minimizing the cost function
when sizing at least one of the repeat region and the at least one
gene-specific peak. This method may advantageously not require
identification of artifacts in the potential peaks, as such
artifacts might result in higher costs. As would be appreciated by
one of skill in the art, this method may be used to estimate an
internal sizing standard or an external sizing standard.
[0174] FIG. 4A depicts an exemplary channel of a data file,
consistent with disclosed embodiments. In some instances, the
external sizing standard described above may be inaccurate because
of differences in composition between the externally added
dye-labelled PCR products of known sizes and the PCR fragments of
the genomic region. For example, ROX fragment mobility in capillary
electrophoresis differs from FMR1 fragment mobility, because of the
GC-rich nature of FMR1 fragments relative to the
nucleotide-balanced nature of ROX fragments. Because of these
inaccuracies, annotation device 130 may be configured to generate
an internal sizing standard. In some embodiments, annotation device
130 may be configured to generate a mobility corrected sizing
standard using the internal sizing standard and an external sizing
standard. The external sizing standards may be derived from the ROX
channel as described above with regard to FIGS. 3A and 3B.
Generation of the internal sizing standard by annotation device 130
may include identifying a repeat profile in a channel of a data
file and estimating a linear relationship between repeat peak
location and repeat fragment size.
[0175] As shown in FIG. 4A, the channel of the data file may
include a repeat profile 410. The repeat profile may comprise the
portion of the channel beginning with the smallest detected PCR
fragment peak and ending with the largest detected PCR fragment
peak or gene product peak. For example, repeat profile 410 may
begin after 2000 sampling units and may end between 4500 and 5000
sampling units. As depicted in FIG. 4A, repeat profile 410 may
display a repetitive sequence of peaks corresponding to
incrementally larger fragments. Depending on the genomic sample,
repeat profile 410 may also display one or more gene product peaks,
such as the peaks around 4500 sampling units.
[0176] Annotation device 130 may be configured to identify repeat
profile 410 in a channel of a data file. In some embodiments,
annotation device 130 may use an external sizing standard, such as
a ROX ladder, to predict and approximate location for the beginning
of repeat profile 410. When annotation device 130 cannot generate
an external sizing standard satisfying quality control criteria
(described below), or when only using an internal model, annotation
device 130 may use the following process to determine the beginning
of repeat profile 410.
[0177] In a first step, annotation device 130 may be configured to
determine an approximate starting location of the repeat profile.
Annotation device 130 may perform a summation transformation of the
channel using a first window size W:
t(i)=.SIGMA..sub.i.sub.x.sup.i.sup.x.sup.+Ws(i.sub.x,c),i.sub.x=(Wx|x=1,-
2,3 . . . )
[0178] Thus t(i), the transformed data, may comprise the sum of the
signal intensities within the first window for the channel, for
non-overlapping first windows. As would be recognized by one of
skill in the art, annotation device 130 may additionally or
alternatively low-pass filter the channel. The channel c may be the
FAM channel, and the first window size W may be between 50 and 1000
sampling units. For the largest peak in that transformed signal
(which may be caused by primer-dimer amplification events),
annotation device 130 may find at least the right-most peak
shoulder, according to the discussion of peak shoulder provided
above.
[0179] After using t(i) to determining the location of the
right-most peak shoulder, annotation device 130 may be configured
to transform the signal intensities within second windows of the
channel into the frequency domain and determine when a dominant
frequency of the signal intensities within the second window
satisfies a frequency criterion. For example, annotation device 130
may calculate the dominant frequency of the channel within a second
window, beginning at this location. The second window may be
between 100 and 200 sampling units wide. Annotation device 130 may
determine the approximate beginning of the repeat profile as the
initial second window in which the difference between the dominant
frequency of the signal and a predetermined frequency satisfies an
empirically-derived difference criterion.
[0180] In a second step, annotation device 130 may be configured to
determine a precise starting location of the repeat profile.
Annotation device 130 may determine this precise starting location
using statistical measures of signal intensities within a third
window. For example, the exact repeat start site may be determined
by annotation device 130 as the first location greater than a
predetermined percentile of the signal intensities within the third
window. In some embodiments, the third window may begin at the
approximate starting location. In the equation below, let a
represent the approximate location of the signal start site and let
c be the channel:
w=[a,a+1000]
start=min({x,x.epsilon.ws(x,c)>Q.sub.85(s(w,c))})
[0181] In this example, the statistical measure is the 85.sup.th
percentile, but the percentile may be between the 70.sup.th and
99.sup.th percentile, for example. Likewise, the width of the third
window is 1000 sampling units, but the third window may be between
50 and 5000 sampling units wide. Amplitude thresholds derived from
other statistical measures such as means and standard deviations
may also be used. The channel c may be the FAM channel.
[0182] In a second step, annotation device 130 may be configured to
determine an ending location of the repeat profile. In some
embodiments, annotation device 130 may filter the channel to
determine the end of the ending location of the repeat profile. For
example, Annotation device 130 may apply a percentile filter across
the channel using a fourth window. After the transformation is
applied, the signal end location may be selected by annotation
device 130 as the last transformed region that exceeds an amplitude
threshold:
t(i)=Q.sub.90(s([i.sub.x,i.sub.x+100],c)),i.sub.x=(100x|x=1,2,3 . .
. )
end=100*max({i,t(i)>100})
[0183] Thus t(i), the transformed data, may comprise the
percentile-filtered value of channel c over the fourth window. In
this example, the percentile is the 90.sup.th percentile, but the
percentile value may range from the 70.sup.th to the 99.sup.th
percentile. Likewise, the width of the fourth window is 100
sampling units, but the third window may be between 50 and 5000
sampling units wide. Amplitude thresholds derived from other
statistical measures such as means and standard deviations may also
be used. The channel c may be the FAM channel. Here end may be the
index of the final value in repeat profile 410. The amplitude
threshold may be instrument-specific and may be empirically
derived. Here the value is 100 RFUs, but this value is not intended
to be limiting.
[0184] FIG. 4B depicts generation of an internal sizing standards
using a repeat profile, consistent with disclosed embodiments.
Annotation device 130 may be configured to identify amplification
peaks in the channel arising from the repeat primers. Annotation
device 130 may associate these peaks with expected fragment sizes
to generate the internal sizing standard. In some aspects, as
discussed in greater detail below, annotation device 130 may
iteratively call repeat peaks using a window derived from the
periodicity of the repeat profile. Annotation device 130 may adjust
this window as periodicity shifts in the repeat profile, and may
interpolate peak locations where repeat peaks are suppressed (e.g.
at AGG interruption sites).
[0185] In a first step, after the start and end locations of the
signal are identified, annotation device 130 may be configured to
determine an initial interval value and an initial amplitude
threshold. As described above, annotation device 130 may
dynamically determine the periodicity of the repeat profile using a
frequency based analysis. For example, annotation device 130 may
perform a Fourier transform on an initial portion of the repeat
profile to identify a dominant frequency of the repeat profile. The
initial portion may begin at the start location. Annotation device
130 may calculate the initial interval value for determining
predicted peak locations using the inverse of the dominant
frequency:
f rp = { s ( [ start , start + 1000 ] , c ) } ##EQU00003## d rp = 1
f rp ##EQU00003.2##
[0186] Here f.sub.rp is the dominant frequency of channel c over
the initial portion. In this example, the initial portion is 1000
sampling units wide, but the initial portion may be between 500 and
5000 sampling units wide. The channel c may be the FAM channel.
[0187] Annotation device 130 may dynamically determine a threshold
for calling repeat peaks in the repeat profile. The threshold may
be an amplitude threshold. For example, annotation device 130 may
be configured to determine the initial amplitude threshold for
identifying repeat locations using a statistical measure calculated
over an initial portion of the repeat profile. The initial portion
may begin at the start location, and the statistical measure may be
a percentile:
t.sub.rp=Q.sub.25(s([start,start+2000]),c)
[0188] Here t.sub.rp is the initial amplitude threshold. In this
example, the 25.sup.th percentile is the statistical measure, but
the percentile may range between the 5.sup.th and 50.sup.th
percentiles. Likewise, the initial portion is 2000 sampling units
wide, but may be 100 to 5000 units wide. The channel c may be the
FAM channel. Annotation device 130 may determine the initial
amplitude threshold using other statistical measures such as means
and standard deviations.
[0189] In a third step, annotation device 130 may be configured to
iteratively generate a set of repeat peak locations. In various
aspects, annotation device 130 may determine predicted peak
location 425 location in repeat profile 410 for each iteration.
Predicted peak location 425 may depend on previous peak location
421 and interval value 423. In some aspects, annotation device 130
may call the repeat peaks in the repeat profile using a sliding
window. In some embodiments, interval value 423 may be derived from
the initial interval value. Annotation device 130 may identify a
repeat peak location within window 427 including predicted peak
location 425. In some embodiments, the window for selecting peaks
may be between 25 to 100% of interval value 423. In certain
embodiments, the window is between 50 to 100% or 80 to 100% of
interval value 423. Annotation device 130 may be configured to
determine the location of the largest peak within window 427. When
a signal intensity at this identified peak location exceeds
amplitude threshold 429, this actual peak 431 may be added to the
set of repeat peak locations. In some embodiments, amplitude
threshold 429 may be derived from the initial amplitude threshold.
In the next iteration, the previous peak location may be the actual
peak 431 identified during this iteration.
[0190] In some embodiments, annotation device 130 may be configured
to update one or more of amplitude threshold 429 and interval value
423. For example, amplitude threshold 429 may be the average of the
signal intensity at the actual peak for two or more previous
iterations. Likewise interval value 423 may be the average of the
differences in location between the actual peak and the previous
peak for two or more previous iterations. In some embodiments,
these averages may be over the 3 to 50 previous iterations. In this
manner, annotating device 130 may accommodate shifts in the
periodicity and amplitude of the repeat peaks over the course of
the repeat profile:
x next = arg max i ( s ( [ + d rp 2 , + 3 2 d rp ] ) ) ##EQU00004##
p next = { x next , s ( x next , c ) > t rp + d rp , otherwise
##EQU00004.2##
[0191] Here the x.sub.next is the location of the largest peak
within the window, and this peak is added to the set of repeat peak
locations when the signal intensity for channel c, which may be the
FAM channel, exceeds the amplitude threshold. In some embodiments,
annotation device 130 may interpolate repeat peaks in the repeat
profile below the threshold. For example, when the signal intensity
does not exceed the amplitude threshold, the predicted peak
location is added to the set of repeat peak locations. In this
manner annotating device 130 may interpolate peaks in regions where
the repeat peak amplitudes are diminished. This interpolation may
improve the accuracy of the sizing standard generated by annotation
device 130.
[0192] In a fourth step, annotation device 130 may be configured to
generate an internal sizing standard describing the relationship
between repeat peak location and repeat fragment size. In some
embodiments, annotation device 130 may use the estimated starting
peak location in the repeat profile and subsequent peak location to
generate a calibration curve (i.e. sizing standard) that maps from
sampling units to base pair units. For example, annotation device
130 may generate the internal sizing standard using the set of
repeat peak locations and a set of corresponding fragment sizes.
Annotation device 130 may establish the correspondence between the
repeat peak locations and the fragment sizes by associating the
first repeat peak location with the smallest fragment size, the
second repeat peak location with the next smallest fragment size,
etc. In some embodiments, each additional repeat peak location may
be associated with a fragment size that is one additional repeat
larger than the previous fragment size. Annotation device 130 may
generate the relationship by regressing the set of corresponding
fragment sizes against the set of repeat peak locations.
[0193] As described above, with regard to FIG. 2, annotation device
may be configured to use an internal sizing standard, an external
sizing mode, or a combined sizing standard. For example, annotation
device 130 may be configured to use the internal sizing standard
and an external sizing standard to generate a mobility corrected
sizing standard. Annotation device 130 may generate the mobility
corrected sizing standard by generating an affine transformation
using the internal sizing standard and the external sizing
standard. This affine transformation may ensure that both the
linear and nonlinear components of the external sizing ladder
contribute to the mobility corrected sizing standard. The affine
transformation may be described by the following equations: let
L.sub.rp(x) represent the internal sizing standard, L.sub.ROX(x)
represent the external model, and L.sub.NL(x) represent the
univariate spline model for the non-linear region of the external
model:
L rp ( x ) = m rp x + b rp ##EQU00005## L ROX ( x ) = { m ROX x + b
ROX , x .di-elect cons. linear range L NL ( x ) , otherwise L final
( x ) = L ROX ( x ) ( m rp m ROX ) + b rp - b rox ( m rp m ROX )
##EQU00005.2##
[0194] In this example, the sensitivity of the mobility corrected
sizing standard depends on the ratio of the sensitivities of the
external sizing standard L.sub.ROX(x) and the internal sizing
standard L.sub.rp(x), while the offset of the mobility corrected
sizing standard depends on the offsets of the internal sizing
standard and external sizing standard, and the ratio of the
sensitivities of the external sizing standard and the internal
sizing standard. Annotation device 130 may be configured to apply
the affine transformation to the external sizing standard to obtain
the mobility corrected sizing standard. For example, annotation
device 130 may calculate the mobility corrected sizing standard
using the above equations.
[0195] As described above with regard to FIG. 11, annotation device
130 may be configured to identify and size genotype peaks. Channels
may exhibit both repeat-segment and gene-specific amplification, so
annotation device 130 may separate these two components of signal
intensity prior to identifying the genotype peaks. Additionally,
annotation device 130 may also be configured to identify abnormal
genotype peaks, as described in greater detail below.
[0196] In a first step, annotation device 130 may be configured to
generate a background model for separating the signal contribution
of repeat amplification events from the signal contribution of
gene-specific amplification events. Creation of the background
model may address gaps in the repeat profile created by
interruptions in the repeat region, such as those arising from AGG
interruptions in a FMR1 repeat region. Creation of the background
model may also address gene-specific product peaks that deviate
from the repeat peak component of the repeat profile, but without
characteristics that would allow a frequency-based filtering
approach to deconvolve.
[0197] FIG. 5 depicts an exemplary repeat profile 510 annotated
with background model 520 and dynamic threshold 530. In some
embodiments, the background model may depend on the magnitude of
the repeat profile within a given window. Where gaps in the repeat
profile arise from sequence interruptions 570 in the repeat region,
the background model may depend on the magnitude of local repeat
peaks proximal to the interruption. The background model during
gene-specific product peak 540, expansion peak 550, and mosiacism
peaks 560 may also depend on the magnitude of local repeat peaks
proximal to the interruption.
[0198] In a first step, annotation device 130 may be configured to
generate background model 520 by filtering repeat profile 510. In
some embodiments, annotation device 130 may filter repeat profile
510 using a filter configured to output values based on at least
one statistical measure of a sliding window of input data. For
example, the filter may output the sum of the median and the
interquartile range of the repeat profile 510 within the window.
The window may be between 3 and 30 repeats wide, for example 11
repeats wide. This design may enable annotation device 130 to
capture the height of repeat peaks in the window, while rejecting
large fluctuations in the repeat signal caused by AGG interruptions
and gene-specific products. As would be recognized by one of skill
in the art, other filter types, such as median smoothing filters
and linear filters (e.g., Butterworth filters) may alternatively be
used.
[0199] In a second step, annotation device 130 may be configured to
further filter background model 520 using a filter configured to
attenuate high-frequency components of the background model. In
some embodiments, annotation device 130 may use a Savitzky-Golay
filter to attenuate the high-frequency components of background
model 520. The Savitzky-Golay filter may be between 3 and 30 wide,
for example 7 repeats wide. The Savitzky-Golay filter may be tuned
to match the repeat profile dynamics, preventing the peaks and
troughs visible in the output of other filter designs.
[0200] In a third step, annotation device 130 may be configured to
determine peak shoulder in the resulting background model 520
according to the methods described above. In some embodiments,
annotation device 130 may replace the signal intensities within the
peak shoulders with values linearly interpolated between the peak
shoulder values.
[0201] Annotation device 130 may be configured to generate a
dynamic threshold from the background model. The dynamic threshold
may be used by annotation device 130 to identify gene-specific
product peaks. By dynamically scaling the background model to
generate the dynamic threshold, annotation device 130 may increase
specificity in the lower fragment size ranges, while increasing
sensitivity in the higher fragment size ranges. In some
embodiments, annotation device 130 may be configured to determine a
first region of the background model corresponding to amplification
products with sizes above a first fragment size and below a second
fragment size. Annotation device 130 may also determine a second
region of the background model corresponding to amplification
products with sizes above the second fragment size. Annotation
device 130 may multiply the first region of the background model by
a first scaling factor that varies from an initial scaling factor
to a second scaling factor less than the initial scaling factor.
For example, the first scaling factor may vary linearly from the
initial scaling factor to the second scaling factor. Annotation
device 130 may also multiply the second region of the background
model by the second scaling factor.
[0202] In the equations below, let M.sub.BG represent background
model 520, let M.sub.G represent the dynamic threshold 530, let
r.sub.1 represent the index corresponding to the first fragment
size, and let r.sub.2 represent the index corresponding to the
second fragment size:
M G ( x ) = { 3 M BG , x < r 0 M BG ( 3 ( x - r 1 ) + 1.5 ( r 2
- x ) r 2 - r 1 ) , r 1 < x < r 2 1.5 M BG , x > r 2
##EQU00006##
[0203] In this example, the first scaling factor is 3 and the
second scaling factor is 1.5, but these values are not intended to
be limiting. The first scaling factor may vary between 1.25 and 10,
and the second scaling factor may vary between 1.25 and 10. In some
aspects, the first fragment size may correspond to 0 repeats, or to
as many as 20 repeats. In various aspects, the first fragment size
may correspond to between 70 and 190 repeats, for example 120
repeats. In this example, annotation device 130 may apply a
piecewise scale factor to background model 520 that decreases from
3 to 1.5 in the region between 0 and 120 repeats, and then remains
constant at 1.5 after 120 repeats, to obtain dynamic threshold
530.
[0204] Annotation device 130 may be configured to determine the
genotype peak set using dynamic threshold 530. Annotation device
130 may identify potential locations in the repeat profile based on
the derivative of the sign of the derivative of the signal
intensity, as described above. When the value of the repeat profile
at a potential peak location exceeds the value of the dynamic
threshold at that potential peak location, annotation device 130
may include the potential peak location in the genotype peak
set.
[0205] Annotation device 130 may be configured to associate repeat
sizes with the gene-specific product peaks using a sizing ladder
and the gene-specific product peak locations. The sizing standard
used by annotation device 130 may comprise one of the internal
sizing standard, the external sizing standard, and the mobility
corrected sizing standard. In some aspects, annotation device 130
may use the following equation to associate to repeat sizes with
product peak locations:
S G = { L sizing ( i ) - s p s r , .A-inverted. i .di-elect cons. G
} ##EQU00007##
[0206] In this example, let L.sub.sizing(x) be the sizing standard,
let S.sub.p be the size of the gene-specific product primer in base
pairs, let S.sub.r be the size of the repeat in base pairs, and let
G be the genotype peak set identified using the dynamic threshold.
As a non-limiting example, S.sub.p may range between 20 and 1000
base pairs, for example 240 base pairs, though other values are
possible. Likewise, S.sub.r may be 3 base pairs, though other
values are possible. Gene-specific or repeat-specific variation in
these and other parameters is within the scope of the described
configurations.
[0207] Annotation device 130 may be configured to resolve
gene-specific product peaks that do not present as normal
gene-specific product peaks (i.e. homozygous female samples, n/n+1
genotypes, expanded samples) after determining the genotype peak
set. Homozygous female peaks may be resolved using supplied sex
information. For example, singly-called gene-specific product peaks
may be resolved into a homozygous genotype for female samples.
[0208] As shown in FIG. 6A, annotation device 130 may be configured
to determine gene-specific product peak locations for genomic
samples with proximal genotypes (n/n+1). Identifying such samples
may be a challenging problem, and such samples may comprise 10% of
female samples. Annotation device 130 may identify such samples by
identifying second peak 633 beside first peak 631. For example,
annotation device 130 may identify second peak 633 in repeat
profile 610a at a second location adjacent to first peak 631.
Second peak 633 may be similar in magnitude to the first peak 631.
For example, annotation device 130 may determine that a second
value of the repeat profile at the second location satisfies an
amplitude criterion based on the first value. For example, the
second value of the repeat profile at the second location may
exceed an amplitude threshold that is between 70% and 90% of the
first value. In some embodiments, both first peak 631 and second
peak 633 may exceed dynamic threshold 620.
[0209] As shown in FIG. 6B, annotation device 130 may be configured
to label a sample as an extended sample when no genotype peaks are
identified, but repeat profile 610b shows expansion. As would be
appreciated by one of skill in the art, when the repeat region is
FMR1 such samples may occur for samples with repeat profiles
expanding far past 200 repeats.
[0210] As with any complex PCR-based workflow, products can
sometimes fail to amplify as a result of either operator or
instrument error. Because this can render samples uninterpretable,
annotation device 130 may be configured to flag samples for
re-analysis. Annotation device 130 may perform multiple
quality-control measures to safeguard users against misinterpreting
results. In some embodiments, annotation device 130 may include two
categories of these quality-control criterions: three criteria
(sizing standard criteria, repeat profile signal-to-noise
criterion, and repeat profile contamination criterion) explicitly
fail the sample and don't produce genotype calls, and one criterion
(minor allele sensitivity criterion) produces genotype calls that
should be interpreted by users with greater skepticism. This "at
risk" QC category is designed to protect users from setting
thresholds for genotype calls below the level which their data can
reliably produce.
[0211] FIGS. 7A-7C depict samples with different quality control
criteria. FIG. 7A depicts a sample with a poor ROX ladder. This
sample may fail the sizing standard criteria. The sizing standard
criteria ensures that the sizing standard is derived correctly and
that it matches expectations with respect to internal calibrators.
Annotation device 130 may use three different criteria for ensuring
this, and may require satisfaction of at least one criteria for the
sample to pass. For example, satisfaction of all three criteria may
be required. The first of the criteria may be that the coefficient
of determination (R.sup.2) for the external model fit against
external ladder peaks is greater than 0.98. The second criteria may
be that the coefficient of determination for the internal model fit
against internal ladder peaks is greater than 0.98. The final
criteria may be that a consistency criterion that compares the
internal sizing standard to the external sizing standard. This
consistency criterion may be satisfied when the external model fit
against the internal model fit for evenly spaced points throughout
the fit is greater than 0.98. The coefficient of determination
thresholds above were determined empirically from the independent
training set, by selecting a level that accurately discriminated
between samples that produced incorrect sizing and samples that
produced correct sizing. In other embodiments, a frequency-based
analysis may be determine whether the repeat profile is of
sufficient periodicity to use in sizing.
[0212] FIG. 7B depicts a sample with poor PCR amplification. This
sample may fail the repeat profile signal-to-noise criterion. This
criterion safeguards users against interpreting results for samples
with poor amplification. Samples with poor amplification may
violate assumptions of the algorithm during processing and could
potentially result in incorrect or false-negative genotypes being
reported/missed. At a high-level, the algorithm may verifies there
exists a sufficient signal-to-noise ratio for the start of the
repeat profile against the noise-level of the instrument proximal
to the start of the repeat profile. The SNR threshold for this QC
may be determined empirically from an independent training set, by
selecting a level that accurately discriminated between samples
that produced incorrect sizing and samples that produced correct
sizing.
L n = Q 75 ( s ( [ I rp ( 1 ) - 200 , I rp ( 1 ) - 50 ] , c ) )
##EQU00008## L rp = median ( { s ( I rp ( i ) , c ) , i = ( 1 , 2 ,
3 , 4 , 5 ) } ) ##EQU00008.2## QC SM { PASS , L rp - T ma > 150
FAIL , otherwise ##EQU00008.3##
[0213] In these exemplary equations, channel c may be the FAM
channel. In this example, a 75.sup.th percentile may be calculated
by annotation device 130 over a windowed portion of the repeat
profile. One of skill in the art would recognize that other
percentiles may be used, for example the 60.sup.th to 95.sup.th
percentiles, or other statistical measures, such as mean and
standard deviations. Similarly, in this limiting example the
windowed portion of the repeat profile extends from 200 sampling
units before the start location to fifty units before the start
location. But other windows of the channel may also be used.
[0214] FIG. 7C depicts a sample with a contamination peak. This
sample may fail the repeat profile contamination criterion. The
repeat profile contamination criterion may be used to identify
instances of off-target amplification or amplification artifacts
related to improper sample preparation, which have the potential to
contribute to incorrect genotypes being reported. The failure
criteria for this QC may be defined as a gene-specific product peak
being identified in a range that cannot possibly be produced by the
gene-specific primers. For instance, if the repeat number derived
for a gene-specific product peaks is less than 0 repeats (i.e. in
some cases less than 240 bp), then the sample may be flagged as
having contamination.
QC CT { PASS , min ( S G ) > 0 FAIL , otherwise ##EQU00009##
[0215] The minor allele sensitivity criterion may caution users
against setting minor allele calling thresholds below levels that
are possible for the assay, with respect to the ratio between the
background noise of the instrument and the largest genotype peak in
the sample. If the ratio between the noise level (as explained
above) and the largest genotype peak exceeds the minor allele
frequency, then minor alleles at that level cannot be accurately
identified, and the sample is flagged with an "at risk" QC that
users should interpret with more rigor. In the equation below, let
G represent the set of genotype peak locations, c represent the
channel, T.sub.ma represent the minor allele threshold specified by
the user, and L.sub.n represent the background noise level of the
signal calculated for the minor allele sensitivity criterion:
P ma x = max { s ( i , c ) , .A-inverted. i .di-elect cons. G }
##EQU00010## QC MA { PASS , L n P ma x > T ma FAIL , otherwise
##EQU00010.2##
[0216] FIG. 8 depicts an exemplary computing system for genotype
peak sizing. In some embodiments, computing system 800 includes a
processor 801, memory 803, display 805, I/O interface(s) 807, and
network adapter 809. These units may communicate with each other
via bus 811, or wirelessly. The components shown in FIG. 8 may
reside in a single device or multiple devices.
[0217] Consistent with disclosed embodiments, processor 801 may be
a microprocessor, central processing unit (CPU), graphical
processing unit (GPU), or similar device. Memory 803 may include
non-transitory memory containing non-transitory instructions, such
as a computer hard disk, random access memory (RAM), removable
storage, or remote computer storage. In some aspects, memory 803
may be configured to store software programs. In some aspects,
processor 801 may be configured to execute non-transitory
instructions and/or programs stored on memory 803 to configure
computing system 800 to perform operations of the disclosed systems
and methods. In various aspects, as would be recognized by one of
skill in the art, processor 801 may be configured to execute
non-transitory instructions and/or programs stored on a remote
memory to perform operations of the disclosed systems and methods.
Display 805 may be any device which provides a visual output, for
example, a computer monitor, an LCD screen, etc. I/O interfaces 807
may include means for communicating information to computing system
800 from a user of computing system 800, such as a keyboard, mouse,
trackball, audio input device, touch screen, infrared input
interface, or similar device. Network adapter 809 may include means
for enabling computing system 800 to exchange information with
external networks. For example, network adapter 809 may include a
wireless wide area network (WWAN) adapter, a Bluetooth module, a
near field communication module, or a local area network (LAN)
adapter.
[0218] Kits of reagents, analysis software, and macromolecules for
carrying out the assays disclosed herein are also provided herein.
In certain embodiments, kits for genotyping a repeat region in a
sample comprise one or more primers for amplification of a repeat
region, a buffer, and analysis software or software key as
described herein. In other embodiments, kits for genotype-peak
sizing comprise one or more primers for amplification of a repeat
region, a buffer, and a non-transitory media sorting analysis
software and/or a software key. In certain aspects, the kits
comprise a primer that is identical or complementary to a portion
of a repeat region of a genetic locus as described herein. In other
aspects, the kits further comprise an amplification primer set or
multiple amplification primer sets, wherein at least one of the
primers comprises a sequence that is identical to or complementary
to a portion of a repeat region of a genetic locus as described
above. The analysis software as described herein may comprise data
and/or instructions stored on a non-transitory computer-readable
medium such as a CD-ROM or other data storage device. The term
"software key" refers to a software license key, cryptographic key,
URL, URL, and/or password configured to enable downloading of or
access to the analysis software described herein. This "software
key" may be displayed on a non-transitory medium such as paper,
cardstock, a sticker, or a similar medium; or may be stored on a
non-transitory computer-readable medium such as a CD-ROM, or other
data storage device (e.g., located in a "readme" file). The kits
specifically contemplate including, for example, the analysis
software that performs the data processing and calculating methods
of any of claims 1-83 of this specification.
[0219] The kits further optionally comprise an enzyme for carrying
out the assays described herein, including but not limited to a
polymerase such as a DNA polymerase or reverse transcriptase. In
certain aspects, the kits include an external sizing ladder. The
sizing ladder may be a ROX ladder or a sizing ladder as described
herein. In certain aspects, the kits include a positive control
sample, for example a template control sample or a pooled cell line
control sample.
[0220] The kit may also comprise reagents for amplifying a repeat
region, including primers, dNTPs, polymerase, and/or buffers. Such
a kit may include one or more buffers, such as a reaction,
amplification, and/or a polymerase buffer, compounds for preparing
a DNA sample, and components for isolating and/or detecting an
amplification product, such as a probe or label, for example.
[0221] In some embodiments, kits of the invention include one or
more of the following (consistent with methods, reagents, and
compositions discussed above): components for sample purification,
including a lysis buffer with a chaotropic agent; a glass-fiber
filter or column; an elution buffer; a wash buffer; an alcohol
solution; and a nuclease inhibitor. The components of the kits may
be packaged either in aqueous media or in lyophilized form, for
example, and will be provided in a suitable container. The
components of the kit may be provided as dried powder(s). When
reagents and/or components are provided as a dry powder, the powder
can be reconstituted by the addition of a suitable solvent. It is
envisioned that the solvent may also be provided in another
container. The container will generally include at least one vial,
test tube, flask, bottle, syringe, and/or other container or
equivalent, into which the solvent is placed, optionally aliquoted.
When the components of the kit are provided in one and/or more
liquid solutions, the liquid solution is an aqueous solution, with
a sterile aqueous solution being particularly preferred. The kits
may also comprise a second container or equivalent for containing a
sterile, pharmaceutically acceptable buffer and/or other
solvent.
[0222] Such kits may also include components that preserve or
maintain DNA or RNA, such as reagents that protect against nucleic
acid degradation. Such components may be nuclease or RNase-free or
protect against RNases, for example. Any of the compositions or
reagents described herein may be components in a kit. Additional
materials may include a suitable reaction container, a barrier
composition, reaction mixtures for amplification and/or PCR
(including buffers and reagents such as dNTPs), nuclease- or
RNAse-free water, RNase inhibitor, and/or any additional buffers,
compounds, co-factors, ionic constituents, proteins, enzymes,
polymers, and the like that may be used in the reactions.
EXAMPLES
[0223] The following examples serve to illustrate, and in no way
limit, the present disclosure.
Example 1: Amplification of the Repeat Region
[0224] PCR-based workflow for size analysis of the CGG-repeat
region may be accomplished using the AmplideX.TM. FMR1 PCR assay
(Asuragen Catalog No. 49402; U.S. Publication No. 2010/0209970).
See FIGS. 9 and 16.
Example 2: Workflow for Sizing a Repeat Region of Nucleic Acid
using an Internal Standard
[0225] A sizing method was developed to utilize the repeat profile
associated with priming events along the CGG-rich region in the
FMR1 gene. Since each peak in the capillary electrophoresis plot
"repeat peak" corresponds to an adjacent priming event, i.e., an
amplification product of one extra repeat in length, the length of
the peaks in nucleotides can be estimated by assuming the size (in
base pairs) of the first repeat peak (taking into account primer
sequence length), then assuming that each repeat peak after the
first is 3 base pairs longer than the previous peak. This
information is used to generate a calibration curve that relates
location in the fragment sequence analysis (FSA) signal (in
sampling units) to fragment size (in base pairs).
[0226] In detail, the algorithm for repeat peak identification
works in several stages. First, the beginning of the repeat signal
is detected using information about the window in which the repeat
signal starts based on the sampling frequency of the instrument.
Second, quantile-based analysis is used to determine the range in
which the repeat signal starts and ends. Third, frequency-based
analysis is used to determine repeat periodicity in sampling units.
Fourth, the repeat periodicity is used to inform the window size
for which repeat peaks will be called. Fifth, a quantile-based
approach is used to derive a threshold at which repeat peaks should
be called. Sixth, a sliding window is used to call single repeat
peaks, where the called peaks for each window are defined as having
a negative second-order derivative with the largest magnitude in
the range. If no peaks are found or the signal falls below the
threshold determined in the fourth stage, the location of the
repeat peak as the center of the window may be extrapolated. As
peaks are called, the size of the window based on the difference
between repeat peaks in sampling units maybe adjusted. FIG. 10
depicts a graphic of the process by which peak location is utilized
for sizing gene-specific products. The expected location of each
repeat peak is used to generate a calibration curve for sizing.
FIG. 10 was generated using AmplideX reagents in combination with
the FMR2 set of custom primers (p/n 49541).
[0227] After repeat peaks are identified, the software may generate
a model for sizing using a cubic splines interpolation of all peak
indices (in sampling units) against expected fragment lengths for
the repeat peaks (in nucleotides). In some embodiments, the sizing
standard may be piecewise, applying a first-order polynomial fit to
the linear region of the gel, and a univariate spline fit to the
nonlinear region of the gel.
Example 3: Automatic Gene-Specific Product Identification of Major
Alleles
[0228] An algorithm developed to automatically generate FMR1
genotypes for this assay can be used in conjunction with the sizing
methods. The algorithm takes a magnitude-based approach to
identifying gene-specific products, and labels all allele peaks
down to a specified threshold. The workflow for this algorithm
involves identifying regions with a peak-like shape, having a
first-order derivative of 0 and a negative second-order derivative,
and ranking the regions by relative fluorescence units (RFU)
magnitude. An optional step may involve providing gender as an
input, returning the number of peaks required for each gender and
automatically resolving numbers of alleles (i.e., for homozygous
female samples). For full mutation analysis the repeat profile for
expansion is analyzed using a quantile-based approach to assess
present repeat peaks passed called repeats. If expansion occurs the
expanded allele is reported. Gene-specific repeat genotypes are
determined in the AmplideX software using the internally derived
ladder.
Example 4: Automatic Gene-Specific Product Identification of Minor
Alleles
[0229] In addition to major allele genotyping, a process for minor
allele detection was established that enables lab-specific
definitions of minor allele cut-offs. Since minor alleles and
mosaicism are typically only clinically relevant if the phenomenon
occurs in a clinically-relevant category, the algorithm was
specifically designed to search for minor alleles in the pre- to
full-mutation range. Specifically, the algorithm for minor allele
detection may take the following steps: (1) from the ranked list of
peaks detected in the major allele genotyping phase of the
algorithm, determine if any excess peaks are >54 CGG repeats
long, and (2) for those peaks, determine which (if any) have an RFU
magnitude higher than a user defined threshold percentage of the
largest gene-specific product identified in the signal. The
threshold currently defaults to 10%, but can be specified as inputs
to the algorithm.
Example 5: Application of Using Workflow for Sizing a Repeat
Region
[0230] The performance of the algorithm discussed above was
evaluated against a comprehensive set of 500 randomly-selected and
previously-annotated clinical samples which span the entire FMR1
genotype range. Samples that passed QC criteria were genotyped
using the methods described in the previous section, and accuracy
was measured as the difference between expected and observed peak
size (in repeat units). In addition, concordance between the ROX
ladder, for example ROX 1000 size ladder, P/N: 145194 (an external
reference standard) and internal sizing methods was evaluated using
a correlation analysis across the sample cohort. Minor allele
detection capabilities were also tested using a selected cohort of
7 samples with operator-identified mosaicism. For this, the
algorithm was parameterized to call down to 5% minor allele
sensitivity, and results were compared against the manually-sized
minor alleles. Finally, the algorithm was tested using a 5%
sensitivity control (Asuragen, P/N 145303) to evaluate the
analytical sensitivity and utility of the repeat profile for
flagging expansions. This sensitivity control was composed of 95%
short female normal and premutation alleles (CGG=30, and 5% of an
expanded allele (>200 CGG).
[0231] FIG. 11 shows the distribution of patient FMR1 genotypes
tested in the study, along with the published distribution of FMR1
genotypes in the general population (Tassone et al., 2012). The
distribution of patients in the testing set was denser in higher
size ranges to assess the sensitivity of the algorithm for
genotypes with greater clinical relevance. Overall, 472 of the 500
samples passed QC thresholds for signal integrity, and were
genotyped using the methodology described in the previous section.
Embedded QC steps accurately identified 28 failed samples (5.6%),
which upon visual inspection were confirmed to have either complete
signal dropout (9 samples) or significant loss of repeat peak
height (19 samples).
[0232] In addition, the repeat-peak sizing method was found to
correlate with previously used methodology (using a ROX ladder)
with R 2>0.95, suggesting a minimized need for external
calibration components (ROX ladder and control samples) as a part
of the AmplideX FMR1 workflow. FIG. 12 depicts an example of the
concordance between ROX and internal sizing methods.
[0233] For the 472 samples that pass embedded QC, the algorithm
successfully identified all major (non-mosaic) alleles (855),
flagging alleles of greater than >200 repeats as full-mutations.
Alleles having greater than 200 repeats were accurately positioned,
using an internal sizing method, to within +1 CGG of their
previously reported size (determined independently using manual
analysis). Table 2 details genotyping accuracy with respect to
clinical mutation category. Overall, all samples with the exception
of 2 were correctly identified in their expected category, with the
2 incorrectly labelled samples being genotyped to with +1 CGG of
their previously reported size. FIG. 13 shows genotyping accuracy
in greater detail, depicting the expected versus observed FMR1
genotypes for all non-mosaic peaks in the cohort. FIG. 13 depicts
the correlation between manual and automated sizing of all major
alleles (855) in the feasibility study. FIG. 13 shows that the
automated FMR1 genotyping workflow produced concordant results with
the manual assignment based workflow.
TABLE-US-00003 TABLE 2 Genotyping accuracy with respect to clinical
mutation category. Automated Analysis Inter- Pre- Full- Normal
mediate Mutation Mutation (0-44) (45-54) (55-200) (>200) Manual
Normal 162 *6 0 0 Analysis (0-44) Intermediate 0 295 *9 0 (45-54)
Pre-mutation 0 *1 527 0 (55-200) Full-Mutation 0 0 0 35 (>200)
*within .+-.1 CGG of manually annotated size
[0234] In addition, the process for minor allele identification
correctly identified all minor alleles from the set of 7 manually
annotated minor alleles to within +1 CGG of their manually-derived
size. FIG. 14A shows a diagram detailing minor allele detection
capabilities. All minor alleles in the figure were automatically
detected by the FMR1 software and labelled concordantly with manual
analysis. The figure also depicts the sensitivity-adjustment
functionally developed for user-specific minor allele
detection.
[0235] Finally, the sensitivity control sample was accurately
genotyped and flagged as an expanded allele. FIG. 14B depicts the
algorithm's labelling of gene-specific products.
[0236] FIGS. 15A and 15B shows a diagram detailing analytical
sensitivity of the algorithm across FMR1 genotype range. Arrows
indicate calls made by automated sizing. FIG. 8 depicts the
AmplideX PCR/CE FMR1 Reporter, a highly accurate, automated FMR1
analysis engine and software interface was designed that improves
the efficiency and consistency of capillary electrophoresis-based
assays targeting the CGG-repeat region of the FMR1 gene, for
example. This software was tested on >1000 clinical samples
processed using AmplideX.RTM. FMR1 PCR reagents and demonstrated
100% agreement with manual genotyping. The software also
demonstrated high sensitivity to detect low-abundance gene-specific
products. This software is likely to improve analysis times for
FMR1 assay workflows by greater than two orders of magnitude, and
has the potential to improve inter-operator consistency in
resolving CE profile ambiguities.
Example 6: Detecting Failed Samples
[0237] An automated strategy of flagging samples for re-analysis
was developed to detect signal dropout by using a quantile-based
analysis, in a way that is robust to signal artifacts that can
occur toward the beginning of the signal (e.g. small first peak,
AGG interruptions). The algorithm works by calculating the 95th
quantile of the RFU magnitude (signal) and the 5th quantile of the
RFU magnitude (background), and fails samples where the difference
between those values falls below 200 RFU. The 200 RFU threshold for
failing a sample was empirically determined using a set of control
samples that were titrated from normal input amounts down to a 12.4
pg genomic DNA input amount. The threshold value was determined by
considering the last input amount that resulted in correct genotype
calls, and was calculated as the mean separation between signal and
noise for those samples. In addition to identifying signal dropout,
an algorithm was developed to identify prematurely stopped runs by
recognizing the absence of ROX ladder peaks in the higher size
ranges. If the number of identified ROX peaks is lower than
expected, the algorithm flags the sample as having incomplete data.
This can potentially guard against errors that might result in
incorrectly genotyped samples with clinical relevance.
Example 7: Identifying Mislabeled ROX Peaks
[0238] Along with automated checks for signal integrity, an
algorithm was developed to identify mislabeling issues in the ROX
channel which could potentially contribute to minor sizing errors.
See FIG. 17 for a depiction of how mislabeling issues could
contribute to incorrect sizing. The dots in the figure demonstrate
the locations of expected ROX ladder peaks in the signal, and the
black line demonstrates size interpolation using a second order
polynomial. The correlation between these actual and interpolated
points serves as a good indicator of gel integrity, and can be used
to identify mislabeling issues. Although these small mislabeling
issues can affect interpolative sizing via ROX ladder, they do not
have as great of an effect on sizing via repeat profile, since
there are many more points to use for interpolating a sizing ladder
via repeat profile. The RA2 value was calculated by correlating the
sizes predicted by a second-order polynomial fit to the data
against the actual size. Deviation from >=0.98 RA2 indicates
that there could have been mislabeling issues that may contribute
to incorrect sizing.
Example 8: FMR1 Sizing Analysis
[0239] 8.1. Workflow Overview
[0240] The current workflow for FMR1 CGG analysis is streamlined
through the point at which genotypes are interpreted from capillary
electrophoresis data. Manual interpretation is a significant
bottleneck for high-volume testing, and an automated algorithm has
the potential to drastically improve the entire process. In this
study, an automated solution to FMR1 analysis was developed to
generate highly accurate FMR1 sizing results. An overview of the
components of the algorithm is described in detail within this
section. At a high-level, the algorithm works in several stages.
The first stage of the algorithm extracts raw data from fragment
sequence analysis files, and performs pre-processing to normalize
the differences across samples run with different configurations.
The second stage involves parameterizing a sizing ladder for
converting from location units in the signal (analogous to distance
travelled in POP7 gel) into base-pair sizes, using components
internal to each sample. The final stage of the algorithm involves
parameterizing a model for deconvolving amplification from
different primer sets in the assay, and using that model to
identify genotype peaks.
[0241] 8.1.1. Fragment Sequence Analysis (FSA) File Parsing
[0242] The AmplideX.RTM. FMR1 PCR assay is designed to be run in
conjunction with the Applied Biosystems family of Genetic Analyzer
instruments (3130/3500/3700), all of which export data in a
proprietary format maintained by Applied Biosystems. This format is
referred to as the Fragment Sequence Analysis format and contains
fluorescence data from capillary electrophoresis experiments,
encoded according to a proprietary set of specifications described
in "Applied Biosystems Genetic Analysis Data File Format, 2009" and
hereby incorporated by reference in its entirety. In order to
directly access this file format, a specialized parser was designed
to decode and organize the information into a JSON-based format
that is easy to programmatically access and manipulate. This parser
heavily utilizes an open-source module for the perl programming
language called Bio::Trace::ABIF. The parsing software has been
validated on >1000 samples run across different Genetic Analyzer
Instruments (3130/3500/3700), and has been shown to be exactly
concordant with unprocessed fluorescence data viewed through
GeneMapper, the current standard for accessing the Fragment
Sequence Analysis format.
[0243] 8.1.2. Preprocessing within the FMR1 Sizing Analysis
[0244] 8.1.2.1. Signal Smoothing
[0245] Since the AmplideX.RTM. FMR1 PCR assay relies on a capillary
electrophoresis-based readout for data interpretation, the assay
was subject to convolution by signal artifacts that are
systematically present on CE instruments. As a first step in
processing, a Savitzky-Golay filter was applied to each of the
channels in order to smooth the data. This allowed for the
assumptions made by the algorithm in downstream processes to be
simplified, and also increased robustness in post-processing
operations.
[0246] 8.1.2.2. Baseline Normalization
[0247] After smoothing, each channel was then normalized to account
for improper instrument calibration. To normalize each channel, the
signal was subtracted by the 10th percentile of RFU values in the
signal. The 10th percentile was chosen empirically because it
robustly represented the lower values in the signal, without being
affected by sharp negative fluctuations commonly encountered.
Alternate suitable values would similarly represent the lower
values in the signal without being affected by sharp negative
fluctuations.
[0248] 8.1.2.3. Air-Bubble Contamination Removal
[0249] As a pre-processing step to the AmplideX.RTM. FMR1
algorithm, air-bubble artifacts were identified and removed. Such
air-bubbles may produce large spikes in signal intensity that may
be interpreted as gene-specific products or ROX channel sizing
peaks in a way that produces incorrect results. But the presence of
air-bubbles capillary tubes during a CE run resulted in
fluorescence affects all of the channels to a similar degree of
magnitude. The air bubble artifacts were identified and removed by
taking advantage of this multi-channel presence of noise peaks. For
each of the sites where an air bubble was found, the air bubble was
removed by simulating Gaussian noise between the peak shoulders.
The mean and standard deviation for the noise was determined from
the region surrounding the air bubble.
[0250] 8.1.2.4. Signal Saturation Resolution
[0251] Another preprocessing step involved extrapolating peak shape
over regions where signal saturation occurred in the FAM channel.
Saturation occurred when products fluoresced at a luminescence
greater than the collection limit of the instrument RFU sensors,
resulting in a loss of information on peak shape. However, since
the wavelength spectra for collection allows for bleed-over across
channels, peak shape for saturated regions can be extrapolated from
channels capturing fluorescence at a similar wavelength. In
identified regions of saturation, the RFU values from the NED
channel were added onto the HEX channel:
[0252] 8.1.3. Automated Sizing Ladder Calibration
[0253] The current gold-standard for fragment sizing in capillary
electrophoresis experiments requires the use of externally added
dye-labelled PCR products of known sizes, which produce
fluorescence peaks in a band outside of the frequency spectrum
produced by AmplideX.RTM. FMR1 PCR products. These fluorescence
peaks can be identified independently of target products generated
by the assay and are used to generate a calibration curve that
relates location in the fragment sequence analysis signal (in
sampling units) to fragment size (in base pairs). Although the
process of identifying these fluorescence peaks is handled
automatically in the GeneMapper software, it often requires manual
inspection to correct mislabeled peaks, which can significantly
increase the time required to perform AmplideX.RTM. analyses. To
improve on this, an algorithm was developed to automatically
identify and label ROX fluorescence peaks in a way that is robust
to the mislabeling phenomena that impedes GeneMapper-based
workflows. Additionally, the algorithm was developed to extend
easily to arbitrary sizing ladders (ROX 1000, ROX 200) for use in
future assay development.
[0254] 8.1.3.1. Bleed-Over Artifact Removal
[0255] A first stage involve of analysis included removing
bleed-over artifacts from the HEX channel that can convolute the
assignment of expected sizes to ROX fragment peaks. In order to
detect these bleed-over artifacts, the algorithm identified
locations above an empirically-derived, instrument-specific
threshold in the HEX channel, and then simulated Gaussian noise
mimicking the signal background over that region.
[0256] 8.1.3.2. ROX Fragment Peak Calling
[0257] A second stage involve of analysis included calling a set of
candidate ROX fragment peaks, and removing peak artifacts that are
likely false-positive peak calls. To identify the candidate set of
ROX fragment peaks, a sliding filter across the data that was 500
location units wide was run. For each window: (a) the mean and
standard deviation of the signal within that range was taken and
(b) peaks that are over 3 standard deviations above the mean noise
level (noise profiles for CE instruments are assumed to follow a
Gaussian distribution) were called. After detecting these candidate
peaks, proximal false positive peaks caused by "shoulder" artifacts
were resolved by selecting the largest peak in a window around
selected peaks.
[0258] 8.1.3.3. ROX Fragment Peak Association
[0259] A third stage of analysis included using an iterative
approach to choose the most likely peaks associated with labelled
fragment peaks. This approach took advantage of the low-noise
profile associated with larger fragment sizes for selecting initial
conditions. In short, all expected ROX fragment sizes greater than
500 bp were automatically associated with the furthest (by distance
in the capillary) set of candidate peaks. A linear sizing ladder
for all labelled ROX peaks between 500 and 700 bp was then fitted
using a 1st order least-squares regression and used to predict the
location of the next (sub-500 bp) fragment peak. The candidate peak
closest to that predicted location was then labelled with that
fragment size, and the linear sizing ladder was re-fitted to
include that data point. The algorithm iteratively labels candidate
peaks with ROX peak fragment sizes in this fashion, continuously
training with new data points in a way that increases the accuracy
of peak association in the noisier region of the gel. This
iterative approach has been shown to be more specific than current
methods (GeneMapper, GeneMarker) in ignoring signal artifacts in
the ROX channel that can contribute to improper sizing ladder
parameterization, and is also robust to mistaking primer-dimer
peaks as ROX fragment peaks.
[0260] 8.1.3.4. Sizing Ladder Parameterization
[0261] A piecewise sizing standard was then generated using the
final sizing ladder. The piecewise model employed a linear model
for ladder peaks below 650 bp, and a univariate spline model
(similar to the local southern method (see, Analytical Biochemistry
100(2):319-323 (1979)) for ladder peaks above 650 bp. To
parameterize a final smoothed sizing ladder, the piecewise model
was used to resample 100 evenly-spaced points with location unit to
fragment size associations. These resampled points were fit to
these data using a univariate spline model in order to generate a
smoothed final version of the ladder.
[0262] 8.1.4. Sizing Ladder Mobility Correction
[0263] ROX fragment mobility in capillary electrophoresis differs
from FMR1 fragment mobility, because of the GC-rich nature of FMR1
fragments relative to the nucleotide-balanced nature of ROX
fragments. In order to account for these differences, mobility
correction factors were parameterized from the FMR1 repeat signal
and applied to the sizing ladder derived from the ROX channel.
There were several steps involved in this process: 1)
identification of the start and end locations of the repeat signal,
2) labelling of all repeat fragment peaks, 3) application of the
mobility correction.
[0264] 8.1.4.1. FMR1 Signal Window Parameterization
[0265] As a precursor to labelling repeat peaks, the region of
interest of the signal was determined. The ROX ladder was used to
predict an approximate location for the beginning of the repeat
profile. If the ROX ladder parameterization failed or the resulting
fit did not match ROX QC criteria (see section 8.1.6), then the
following steps were employed.
[0266] A summation transformation on the data with a window size of
200 location units was used. For the largest peak in that
transformed signal (caused by primer-dimer amplification events),
the peak shoulders were identified. From the right-most shoulder, a
transformation was applied that calculated the dominant frequency
of the signal within 100 location unit windows. The location of the
first window in which the dominant frequency of the signal was
within an empirically-derived tolerance was used as the approximate
beginning of the repeat profile.
[0267] Once the approximate location of the repeat region was
identified, the exact repeat start site was determined by
identifying the first location greater than the 85th percentile of
the signal within a 1000 location unit window from the approximate
repeat start site.
[0268] The end location of the repeat region was identified using a
transformation that applied a 90th percentile filter across the
signal with a window size of 100 location units. After the
transformation was applied, the signal end location was selected as
the last transformed region that falls above an
empirically-derived, instrument-specific threshold.
[0269] 8.1.4.2. Repeat Primer Peak Calling
[0270] After the start and end locations of the signal were
identified, the analysis proceeded by determining all amplification
peaks in the signal generated by the repeat primer set. These peaks
were then associated with sizes resulting from expected priming
events, and a linear ladder fit was generated. At a high-level, the
repeat peaks were called used a window derived from the periodicity
of the signal to iteratively call repeat peaks, and the window was
adjusted as periodicity shifts in the signal, and peak location
were interpolated where repetitive peaks were suppressed (i.e. AGG
interruption sites).
[0271] A Fourier Transform on a 1000 location unit window from the
start site was used to identify the periodicity of the repeat
profile. The expected starting distance between peaks was
calculated using the inverse of the periodicity. The 25th
percentile of a 2000 location unit window from the start site was
used to derive a threshold for interpolating peak location.
[0272] To determine the length of the repeat profile (between the
start and end locations determined above), the next peak call peaks
that were above the repeat threshold were iteratively chosen by
predicting a window containing the approximate peak location and
selecting the largest peak in that range. The window for selecting
peaks was calculated as 1/2 of the size of the distance between
peaks (as determined by the periodicity of the signal).
[0273] 8.1.4.3. Sizing Ladder Mobility Correction
[0274] After all peaks for the repeat profile were identified, they
were labelled with expected fragment sizes and used to generate a
linear sizing ladder. This linear sizing ladder served as a
size-corrected ladder that the ROX ladder can be mapped to using an
affine transformation. Mapping via affine transformation ensured
that both the linear and nonlinear components of the sizing ladder
had mobility corrections applied to them.
[0275] 8.1.5. Genotype Peak Identification and Sizing
[0276] An important piece of the sizing analysis was genotype peak
identification and sizing. Since signals exhibit both
repeat-segment and gene-specific amplification, identification of
genotype peaks is nontrivial and involves steps to deconvolve these
two components of the signal. Additionally, challenging genotypes
that do not present as normal genotype peaks were identified
throughout the process.
[0277] 8.1.5.1. Repeat Profile Background Estimation
[0278] A first step in identifying gene-specific amplification
events involved parameterizing a background model for deconvolving
the signal contribution of repeat amplification events from the
signal contribution of gene-specific amplification events. Among
the signal artifacts that make this process difficult, the most
significant were: (1) AGG interruptions in the FMR1 repeat region
that significantly diminished the magnitude of the repeat profile,
creating "gaps" in the profile that must be ignored while creating
the background model; and (2) Gene-specific product peaks
significantly deviating from the repeat component of the signal,
but lacking characteristics that would allow a frequency-based
filtering approach to deconvolution.
[0279] The deconvolution process modeled the "background" of the
repeat profile as the height of the repeat signal within a given
window. For AGG interruptions, the "background" was at the level of
local repeat peaks proximal to the AGG interruption. For
gene-specific product peaks, the "background" was similarly at the
level of local repeat peaks proximal to the gene-specific
deviations in the signal.
[0280] The analysis included the following steps to generate the
background model. For all of the peaks in the repeat signal, a
filter added the median and interquantile range of the data over a
window size of 11 repeats. This filter was designed to capture the
height of repeat peaks in the window, but was robust to large
fluctuations in the repeat signal caused by AGG interruptions and
gene-specific products. For the resulting signal, a Savitzky-Golay
filter with a window size of 7 repeats to smooth the data was used.
Any "peaks" in the resulting signal were interpolated linearly
through the peak shoulders.
[0281] 8.1.5.2. Genotype Peak Identification Using Dynamic
Threshold
[0282] A dynamic threshold was derived from the deconvolution model
above, by applying a dynamic scaling approach that makes calling in
the lower size ranges more specific, and calling in the higher size
ranges more sensitive. At a high-level, the scaling approach
applied a piecewise scale factor to the deconvolution model, which
decreased from 3 to 1.5 in the region between 0 and 120 repeats,
and then remained constant at 1.5 after 120 repeats. The genotype
peak set was determined using this threshold in accordance with the
method above for identifying regions with peak-like shape.
Gene-specific product peaks were converted to repeat sizes using
the sizing ladder derived in a previous section, and converted to
repeat numbers using the known fragment size of non-repeat
components from gene-specific amplification products (in this case
240 bp).
[0283] 8.1.5.3. Resolution of Challenging Genotypes
[0284] After the initial pass at determining the genotype peak set
G, challenging genotypes that do not present as normal
gene-specific product peaks (i.e. homozygous female samples, n/n+1
genotypes, expanded samples) were resolved. Homozygous female peaks
were resolved using supplied sex information, and singly-called
peaks were resolved into a homozygous genotype for female samples.
Samples with proximal genotypes (n/n+1) in the normal range were
resolved by using repeat peaks next to genotype peaks. When the
repeat peak occurred adjacent to a genotype peak, and the signal
intensity of the repeat peak was within 90% of the height of the
adjacent genotype peak, the repeat peak was also labelled as a
genotype peak. Finally, when no genotype peaks were identified for
a sample, but the repeat profile showed expansion, then the sample
was labelled as an expanded sample. This usually occurred for male
samples lacking gene-specific product peaks in which the repeat
profile expanded well beyond 200 repeats.
[0285] 8.1.6. Automated Embedded Quality Control
[0286] Several quality-control measures were used to prevent
misinterpretation of results. There were two categories of
quality-control measures. No genotype calls were produced for
samples failing the first category of quality-control measures
(Sizing Ladder QC, Signal Magnitude QC, and Contamination QC).
Genotype calls for samples failing the second category of
quality-control measures (Minor Allele Sensitivity QC) were
interpreted with greater skepticism. This second category should
protect users from making genotype calls not reliably supported by
their data.
[0287] 8.1.6.1. Sizing Ladder QC
[0288] The Sizing Ladder QC verified that the sizing ladder was
derived correctly and matched expectations with respect to internal
calibrators. This measure combined three different criteria, each
of which must be satisfied for the sample to pass. First, the
coefficient of determination (R2) for the ROX ladder fit against
ROX ladder peaks must be greater than 0.98. Second, the coefficient
of determination for the internal ladder fit against internal
ladder peaks must be greater than 0.98 Third, the coefficient of
determination for the ROX ladder fit against the internal ladder
fit for evenly spaced points throughout the fit must be greater
than 0.98. These coefficient of determination thresholds were
determined empirically from an independent training set, by
selecting a level that accurately discriminated between samples
that produced incorrect sizing and samples that produced correct
sizing.
[0289] 8.1.6.2. Signal Magnitude QC
[0290] The Signal Magnitude QC verified that the samples underwent
sufficient amplification. Samples with poor amplification violate
assumptions of the algorithm during processing and could
potentially result in incorrect or false-negative genotypes being
reported/missed. At a high-level, the algorithm verifies that there
is a sufficient signal-to-noise ratio for the start of the repeat
profile against the noise-level of the instrument proximal to the
start of the repeat profile. The SNR threshold for this QC was
determined empirically from the independent training set, by
selecting a level that accurately discriminated between samples
that produced incorrect sizing and samples that produced correct
sizing.
[0291] 8.1.6.3. Contamination QC
[0292] The Contamination QC verified that samples did not undergo
off-target amplification, or include amplification artifacts
related to improper sample preparation, which may contribute to
incorrect genotypes reporting. Samples failed this QC when a
gene-specific product peak was identified in a range that cannot be
produced using the gene-specific primers. For example, when the
repeat number derived for a gene-specific product peaks was less
than 0 repeats (or equivalently less than 240 bp), then the sample
was flagged as having contamination.
[0293] 8.1.6.4. Minor Allele Sensitivity QC
[0294] The Contamination QC verified that samples had sufficiently
low background noise for the chosen minor allele calling
thresholds. This QC depended on the ratio between the background
noise of the instrument and the largest genotype peak in the
sample. When the ratio between the noise level in the signal and
the largest genotype peak exceeds minor allele frequency, then
minor alleles at that frequency cannot be accurately identified,
and the sample is flagged with an "at risk" QC that users should
interpret with more rigor.
[0295] 8.1.7. FMR1 Sizing Performance
[0296] The performance above FMR1 sizing analysis was tested on
several large cohorts across multiple instruments. For each of
these studies, the algorithm correctly flagged expected QC failures
and sized 100% of QC passing samples in accordance with assay
guidelines. Assay guidelines for sizing were defined as +/-1 repeat
for genotypes <70 repeats, +/-3 repeats for genotypes <120
repeats, and +/-5% of the repeat number for genotypes >=120
repeats. In addition, low-level mosaic peaks were detected for a
number of samples that were identified by operators as having
mosaic peaks. Samples failing QC were independently verified as
deserving a failure status by trained manual operators. Truth data
in this study were generated by trained operators via manual sizing
through GeneMapper software. Furthermore, the distribution of
genotypes tested in the study only differed slightly from genotypes
expected in the normal population. The distribution of genotypes in
this study was purposefully enriched with clinically-relevant
alleles in the intermediate, permutation, and full-mutation range,
in order to stress-test the algorithm for cases in which genotyping
accuracy has a greater clinical impact.
[0297] 8.1.8. Sally Nolan Performance Study
[0298] The Sally Nolan sample set was generated on a 3500 CE
instrument, with normal inputs and conditions. This was designed to
test the algorithm at an external lab, where input amounts are in
accordance with normal usage of the assay. A total of 1040 samples
were evaluated in this study, and 100% of genotypes for samples
passing QC were accurately sized in accordance with assay
guidelines. FIG. 18A shows the genotype distribution of samples in
the study. FIG. 18B shows a comparison between automated and manual
genotypes produced for assay results, and Table 3 details a
comparison between manual and automated categorical calls.
TABLE-US-00004 TABLE 3 Categorical performance table for Sally
Nolan sample set. All discrepancies between manual and automated
assignment of clinical category were resolved by an independent
operator as either being near mutational boundaries but within
sizing guidelines for the assay, or resolved as having a low-level
mosaic peak not originally labelled in the truth set. Manual
Analysis Inter- Pre- Normal mediate Mutation Expansion (0-44)
(45-54) (55-200) (>200) Automated Normal 147 0 0 0 Analysis
(0-44) Intermediate 10 237 0 0 (45-54) Pre-mutation 0 16 476 0
(55-200) Expansion 0 0 1 18 (>200)
[0299] 8.1.9. Multi-instrument Rush Input Amount Study
[0300] For this performance study, the RUSH set of samples
(commonly used in testing the assay for the diverse distribution of
genotypes and sample features) were run at different input amounts,
in order to test the robustness of the assay to operator error. In
addition, each input level was run on three different instruments
to test the multi-instrument capabilities of the assay. Input
levels were 100 ng/.mu.l, 20 ng/.mu.l, 4 ng/.mu.l, and 0.8
ng/.mu.l, which span normal input amounts for the assay (20
ng/.mu.l) on the high and low end. A total of 31 samples were
evaluated in this study, and 100% of genotypes for samples passing
QC were accurately sized in accordance with assay guidelines. FIG.
19A shows the genotype distribution of samples in the study. FIG.
19B shows a comparison between automated and manual genotypes
produced for assay results, and Table 4 details a comparison
between manual and automated categorical calls.
TABLE-US-00005 TABLE 4 Categorical performance table for study.
Each unique sample/input amount in the study was run three times
across different instruments (3130, 3730, 3500). Manual Analysis
Inter- Pre- Normal mediate Mutation Expansion (0-44) (45-54)
(55-200) (>200) Automated Normal 30 0 0 0 Analysis (0-44)
Intermediate 10 0 0 0 (45-54) Pre-mutation 0 0 33 0 (55-200)
Expansion 0 0 0 37 (>200)
[0301] 8.1.10. Artificial Minor Allele Input Titration Study
[0302] This analysis can flag low-level minor alleles across the
full spectrum of genotypes. To demonstrate this capability, a study
simulated the presence of low-level minor alleles in the background
of a premutation sample (30 and 56 repeats). Minor alleles at 76,
96, and 119 were independently mixed at different input levels into
the premutation background (20 ng/.mu.l). The spectrum of input
levels for minor alleles in this experiment included 20 ng/.mu.l,
10 ng/.mu.l, 5 ng/.mu.l, 2.5 ng/.mu.l, and 1 ng/.mu.l. Analytical
performance in sizing the premutation peaks in the sample and in
sizing the mixed mosaic peaks was assessed. A total of 40 samples
were evaluated in this study, and 100% of genotypes for samples
passing QC (including minor alleles) were accurately sized in
accordance with assay guidelines. FIG. 20A shows the genotype
distribution of samples in the study. FIG. 20B shows a comparison
between automated and manual genotypes produced for assay results,
and Table 5 details a comparison between manual and automated
categorical calls.
TABLE-US-00006 TABLE 5 Categorical performance table for artificial
minor allele study. All genotypes are permutation genotypes because
each sample was spiked with a permutation minor allele. Manual
Analysis Inter- Pre- Normal mediate Mutation Expansion (0-44)
(45-54) (55-200) (>200) Automated Normal 0 0 0 0 Analysis (0-44)
Intermediate 0 0 0 0 (45-54) Pre-mutation 0 0 38 0 (55-200)
Expansion 0 0 0 0 (>200)
[0303] 8.1.11. Rush Sample Titration Study
[0304] For this performance study, the RUSH set of samples
underwent 5 2-fold serial dilutions from normal input amounts, in
order to test stress-test the robustness of the algorithm at low
sample input levels. The input levels tested in this experiment
include 100% (20 ng/.mu.l), 50% (10 ng/.mu.l), 25% (5 ng/.mu.l),
12.5% (2.5 ng/.mu.l), 6.2% (1.25 ng/.mu.l), and 3.1% (0.75
ng/.mu.l). A total of 66 samples were evaluated in this study, and
100% of genotypes for samples passing QC were accurately sized in
accordance with assay guidelines. FIG. 21A shows the genotype
distribution of samples in the study. FIG. 21B shows a comparison
between automated and manual genotypes produced for assay results,
and Table 6 details a comparison between manual and automated
categorical calls.
TABLE-US-00007 TABLE 6 Categorical performance table for titration
study. Manual Analysis Inter- Pre- Normal mediate Mutation
Expansion (0-44) (45-54) (55-200) (>200) Automated Normal 15 0 0
0 Analysis (0-44) Intermediate 0 0 0 0 (45-54) Pre-mutation 0 0 22
0 (55-200) Expansion 0 0 0 22 (>200)
[0305] 8.1.12. QC Failure Mode Simulation Study
[0306] In order to test the ROX QC failure modes in the study, a
sample set was generated to have two different types of ROX
failures across a range of sample genotypes. The first failure mode
included CE analysis of the RUSH sample set without labelled ROX
fragments. The second failure mode included CE analysis of the RUST
sample set with the ROX 400 ladder (ROX 1000 is required for this
assay). A total of 13 samples without ROX and 12 samples with ROX
400 were analyzed and properly failed by the algorithm on the
Sizing Ladder QC status.
[0307] 8.1.13. Improvements in Result Turn-Around Time
[0308] Annotation device 130 greatly improves turn-around time for
producing FMR1 results. For a 1000-sample cohort, manual operators
were assumed to require 1 minute per sample, yielding a required
16.6 hours to process the entire cohort. In contrast, annotation
device 130 produced results for the entire cohort (on a machine
using 2 cores) in 1 minute and 24 seconds, demonstrating a >700
fold increase in time-to-result.
[0309] 8.1.14. Analytical Capabilities
[0310] To further illustrate the analytical sensitivity of the
assay, FIGS. 22A to 25B depict a range of different genotypes and
corner-cases that the algorithm is able to correctly account for,
producing sizing in accordance with manual operators. For example,
FIGS. 22A and 22B depict the results of automatic sizing analysis
for samples with normal genotypes. FIGS. 22C and 22D depict the
results of automatic sizing analysis for samples with permutation
genotypes. FIGS. 23A and 23B depict the results of automatic sizing
analysis for expanded samples. FIGS. 23C and 23D depict the
low-level minor allele identification and sizing. FIG. 24 depicts
the results of automatic sizing analysis for a control sample with
a mixture of genotypes throughout the normal, permutation, and
expanded genotype ranges. FIGS. 25A and 25B depicts the results of
automatic sizing analysis for a control sample including a mixture
of 5% full mutation sample in a background of 95% permutation
sample. FIG. 25A depicts the full sample including all called
genotypes, while FIG. 25B depicts a zoomed-in version showing the
full mutation call.
[0311] 8.2. Discussion
[0312] An AmplideX.RTM. FMR1 PCR in-silico CGG fragment-size
analysis tool was developed. This novel tool will enable rapid and
accurate identification and sizing the full range of clinically
relevant FMR1 genotypes, supporting high-volume sample processing
and automated data analysis.
[0313] The studies summarized in the preceding examples demonstrate
a high-performing annotation device for FMR1 PCR fragment size
analysis with several important features. Among these are: (1)
accurate genotyping for the entire clinically-relevant spectrum of
FMR1 CGG repeat sizes; (2) the ability to accurately identify and
size low-level mosaicism (down to 1%); (3) multi-instrument
compatibility with the ABI family of Genetic Analyzers (3130, 3500,
3730); (4) robustness to signal artifacts produced by capillary
electrophoresis instruments (air-bubbles, improper calibration,
bleed-over artifacts, signal saturation, and collection noise); (5)
significantly reduced analysis time when compared with manual
processing of samples (>500 fold), and/or (6) automated QC
analysis and sample flagging to protect users against poor
amplification, contamination artifacts, poor-quality ROX ladders,
or discrepancies in expected allele detection capability vs
sample-inferred allele detection capabilities.
[0314] The preceding examples are intended to illustrate and in no
way limit the present disclosure. Other embodiments of the
disclosed devices and methods will be apparent to those skilled in
the art from consideration of the specification and practice of the
devices and methods disclosed herein.
[0315] The foregoing disclosed embodiments have been presented for
purposes of illustration only. This disclosure is not exhaustive
and does not limit the claimed subject matter to the precise
embodiments disclosed. Those skilled in the art will appreciate
from the foregoing description that modifications and variations
are possible in light of the above teachings or may be acquired
from practicing the inventions. In some aspects, methods consistent
with disclosed embodiments may exclude disclosed method steps, or
may vary the disclosed sequence of method steps or the disclosed
degree of separation between method steps. For example, method
steps may be omitted, repeated, or combined, as necessary, to
achieve the same or similar objectives. In various aspects,
non-transitory computer-readable media may store instructions for
performing methods consistent with disclosed embodiments. These
instructions may exclude disclosed method steps, or vary the
disclosed sequence of method steps or disclosed degree of
separation between method steps. For example, non-transitory
computer-readable media may store instructions for performing
methods consistent with disclosed embodiments that omit, repeat, or
combine, as necessary, method steps to achieve the same or similar
objectives. In certain aspects, systems need not necessarily
include every disclosed part, and may include other undisclosed
parts. For example, systems may omit, repeat, or combine, as
necessary, parts to achieve the same or similar objectives.
Accordingly, the claimed subject matter is not limited to the
disclosed embodiments, but instead defined by the appended claims
in light of their full scope of equivalents.
Sequence CWU 1 SEQUENCE LISTING <160> NUMBER OF SEQ ID
NOS: 55 <210> SEQ ID NO 1 <211> LENGTH: 22 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 1 cggtggaggg
ccgcctctga gc 22 <210> SEQ ID NO 2 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 2
caggcgctca gctccgtttc ggttt 25 <210> SEQ ID NO 3 <211>
LENGTH: 25 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 3 cagtcaggcg ctcagctccg tttcg 25 <210> SEQ ID NO 4
<211> LENGTH: 24 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 4 tccggtggag ggccgcctct gagc 24 <210>
SEQ ID NO 5 <211> LENGTH: 35 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 5 ggttcggcct cagtcaggcg
ctcagctccg tttcg 35 <210> SEQ ID NO 6 <211> LENGTH: 36
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 6
gggttcggcc tcagtcaggc gctcagctcc gtttcg 36 <210> SEQ ID NO 7
<211> LENGTH: 26 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 7 gcgggccggg ggttcggcct cagtca 26 <210>
SEQ ID NO 8 <211> LENGTH: 25 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 8 cagcgggccg ggggttcggc
ctcag 25 <210> SEQ ID NO 9 <211> LENGTH: 25 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 9 gcagcgggcc
gggggttcgg cctca 25 <210> SEQ ID NO 10 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 10
gggccggggg ttcggcctca gtcag 25 <210> SEQ ID NO 11 <211>
LENGTH: 26 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 11 ggggttcggc ctcagtcagg cgctca 26 <210> SEQ ID NO
12 <211> LENGTH: 27 <212> TYPE: DNA <213>
ORGANISM: Artificial Sequence <220> FEATURE: <223>
OTHER INFORMATION: Description of Artificial Sequence: Synthetic
primer <400> SEQUENCE: 12 ggggttcggc ctcagtcagg cgctcag 27
<210> SEQ ID NO 13 <211> LENGTH: 30 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 13 ggcgctcagc tccgtttcgg
tttcacttcc 30 <210> SEQ ID NO 14 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 14
tcaggcgctc agctccgttt cggtttca 28 <210> SEQ ID NO 15
<211> LENGTH: 26 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 15 cacttccggt ggagggccgc ctctga 26
<210> SEQ ID NO 16 <211> LENGTH: 25 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 16 ttccggtgga gggccgcctc
tgagc 25 <210> SEQ ID NO 17 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 17
cgcacttcca ccaccagctc ctcca 25 <210> SEQ ID NO 18 <211>
LENGTH: 21 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 18 ggagcccgcc cccgagaggt g 21 <210> SEQ ID NO 19
<211> LENGTH: 21 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 19 gggagcccgc ccccgagagg t 21 <210> SEQ
ID NO 20 <211> LENGTH: 26 <212> TYPE: DNA <213>
ORGANISM: Artificial Sequence <220> FEATURE: <223>
OTHER INFORMATION: Description of Artificial Sequence: Synthetic
primer <400> SEQUENCE: 20 cgcacttcca ccaccagctc ctccat 26
<210> SEQ ID NO 21 <211> LENGTH: 23 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 21 cgggagcccg cccccgagag gtg
23 <210> SEQ ID NO 22 <211> LENGTH: 23 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 22 ccgggagccc
gcccccgaga ggt 23 <210> SEQ ID NO 23 <211> LENGTH: 24
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 23
ccgggagccc gcccccgaga ggtg 24 <210> SEQ ID NO 24 <211>
LENGTH: 26 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 24 cgccgggagc ccgcccccga gaggtg 26 <210> SEQ ID NO
25 <211> LENGTH: 26 <212> TYPE: DNA <213>
ORGANISM: Artificial Sequence <220> FEATURE: <223>
OTHER INFORMATION: Description of Artificial Sequence: Synthetic
primer <400> SEQUENCE: 25 gcgccgggag cccgcccccg agaggt 26
<210> SEQ ID NO 26 <211> LENGTH: 25 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 26 cgccgggagc ccgcccccga
gaggt 25 <210> SEQ ID NO 27 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 27
gcgccattgg agccccgcac ttccacca 28 <210> SEQ ID NO 28
<211> LENGTH: 25 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 28 gcgccattgg agccccgcac ttcca 25 <210>
SEQ ID NO 29 <211> LENGTH: 25 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 29 agcgccattg gagccccgca
cttcc 25 <210> SEQ ID NO 30 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 30
cgccattgga gccccgcact tccac 25 <210> SEQ ID NO 31 <211>
LENGTH: 25 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 31 ttggagcccc gcacttccac cacca 25 <210> SEQ ID NO
32 <211> LENGTH: 28 <212> TYPE: DNA <213>
ORGANISM: Artificial Sequence <220> FEATURE: <223>
OTHER INFORMATION: Description of Artificial Sequence: Synthetic
primer <400> SEQUENCE: 32 agccccgcac ttccaccacc agctcctc 28
<210> SEQ ID NO 33 <211> LENGTH: 28 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 33 gagccccgca cttccaccac
cagctcct 28 <210> SEQ ID NO 34 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 34
cattggagcc ccgcacttcc accaccag 28 <210> SEQ ID NO 35
<211> LENGTH: 30 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 35 cccgcacttc caccaccagc tcctccatct 30
<210> SEQ ID NO 36 <211> LENGTH: 30 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 36 tagaaagcgc cattggagcc
ccgcacttcc 30 <210> SEQ ID NO 37 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 37
aagcgccatt ggagccccgc acttcc 26 <210> SEQ ID NO 38
<211> LENGTH: 36 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 38 tcaggcgctc agctccgttt cggtttcact tccggt 36
<210> SEQ ID NO 39 <211> LENGTH: 36 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 39 agcgtctact gtctcggcac
ttgcccgccg ccgccg 36 <210> SEQ ID NO 40 <211> LENGTH:
25 <212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 40
ggcgctcagc tccgtttcgg tttca 25 <210> SEQ ID NO 41 <211>
LENGTH: 43 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 41 tcaggcgctc agctccgttt cggtttcacg gcggcggcgg cgg 43
<210> SEQ ID NO 42 <400> SEQUENCE: 42 000 <210>
SEQ ID NO 43 <211> LENGTH: 41 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 43 aagcgccatt ggagccccgc
acttccccgc cgccgccgcc g 41 <210> SEQ ID NO 44 <211>
LENGTH: 44 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 44 tcaggcgctc agctccgttt cggtttcacg gcggcggcgg cgga 44
<210> SEQ ID NO 45 <211> LENGTH: 41 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 45 aagcgccatt ggagccccgc
acttccccgc cgccgccgcc t 41 <210> SEQ ID NO 46 <211>
LENGTH: 39 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 46 tgcgcctccg ccgccgcggg cgcaggcacc gcaaccgca 39
<210> SEQ ID NO 47 <211> LENGTH: 33 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 47 cgcagcctgt agcaagctct
ggaactcagg agt 33 <210> SEQ ID NO 48 <211> LENGTH: 57
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 48
tgcgcctccg ccgccgcggg cgcaggcacc gcaaccgcac cccggccccg gccccgg 57
<210> SEQ ID NO 49 <211> LENGTH: 53 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 49 cgcagcctgt agcaagctct
ggaactcagg agtcgccggg gccggggccg ggg 53 <210> SEQ ID NO 50
<211> LENGTH: 22 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 50 ccaaagcatt gggattactg gc 22 <210>
SEQ ID NO 51 <211> LENGTH: 22 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 51 gattgcttga gcctaggcat tc
22 <210> SEQ ID NO 52 <211> LENGTH: 12 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic oligonucleotide <400> SEQUENCE: 52
aataataata at 12 <210> SEQ ID NO 53 <211> LENGTH: 10
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic oligonucleotide <400>
SEQUENCE: 53 aataaataat 10 <210> SEQ ID NO 54 <211>
LENGTH: 10 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic oligonucleotide
<400> SEQUENCE: 54 aaataaaaat 10 <210> SEQ ID NO 55
<211> LENGTH: 10 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic
oligonucleotide <400> SEQUENCE: 55 aataaaaaat 10
1 SEQUENCE LISTING <160> NUMBER OF SEQ ID NOS: 55 <210>
SEQ ID NO 1 <211> LENGTH: 22 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 1 cggtggaggg ccgcctctga gc
22 <210> SEQ ID NO 2 <211> LENGTH: 25 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 2 caggcgctca gctccgtttc
ggttt 25 <210> SEQ ID NO 3 <211> LENGTH: 25 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 3 cagtcaggcg
ctcagctccg tttcg 25 <210> SEQ ID NO 4 <211> LENGTH: 24
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 4
tccggtggag ggccgcctct gagc 24 <210> SEQ ID NO 5 <211>
LENGTH: 35 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 5 ggttcggcct cagtcaggcg ctcagctccg tttcg 35 <210>
SEQ ID NO 6 <211> LENGTH: 36 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 6 gggttcggcc tcagtcaggc
gctcagctcc gtttcg 36 <210> SEQ ID NO 7 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 7
gcgggccggg ggttcggcct cagtca 26 <210> SEQ ID NO 8 <211>
LENGTH: 25 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 8 cagcgggccg ggggttcggc ctcag 25 <210> SEQ ID NO 9
<211> LENGTH: 25 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 9 gcagcgggcc gggggttcgg cctca 25 <210>
SEQ ID NO 10 <211> LENGTH: 25 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 10 gggccggggg ttcggcctca
gtcag 25 <210> SEQ ID NO 11 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 11
ggggttcggc ctcagtcagg cgctca 26 <210> SEQ ID NO 12
<211> LENGTH: 27 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 12 ggggttcggc ctcagtcagg cgctcag 27
<210> SEQ ID NO 13 <211> LENGTH: 30 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 13 ggcgctcagc tccgtttcgg
tttcacttcc 30 <210> SEQ ID NO 14 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 14
tcaggcgctc agctccgttt cggtttca 28 <210> SEQ ID NO 15
<211> LENGTH: 26 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 15 cacttccggt ggagggccgc ctctga 26
<210> SEQ ID NO 16 <211> LENGTH: 25 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 16 ttccggtgga gggccgcctc
tgagc 25 <210> SEQ ID NO 17 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 17
cgcacttcca ccaccagctc ctcca 25 <210> SEQ ID NO 18 <211>
LENGTH: 21 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 18 ggagcccgcc cccgagaggt g
21 <210> SEQ ID NO 19 <211> LENGTH: 21 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 19 gggagcccgc
ccccgagagg t 21 <210> SEQ ID NO 20 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 20
cgcacttcca ccaccagctc ctccat 26 <210> SEQ ID NO 21
<211> LENGTH: 23 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 21 cgggagcccg cccccgagag gtg 23 <210>
SEQ ID NO 22 <211> LENGTH: 23 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 22 ccgggagccc gcccccgaga ggt
23 <210> SEQ ID NO 23 <211> LENGTH: 24 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic primer <400> SEQUENCE: 23 ccgggagccc
gcccccgaga ggtg 24 <210> SEQ ID NO 24 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 24
cgccgggagc ccgcccccga gaggtg 26 <210> SEQ ID NO 25
<211> LENGTH: 26 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 25 gcgccgggag cccgcccccg agaggt 26
<210> SEQ ID NO 26 <211> LENGTH: 25 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 26 cgccgggagc ccgcccccga
gaggt 25 <210> SEQ ID NO 27 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 27
gcgccattgg agccccgcac ttccacca 28 <210> SEQ ID NO 28
<211> LENGTH: 25 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 28 gcgccattgg agccccgcac ttcca 25 <210>
SEQ ID NO 29 <211> LENGTH: 25 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 29 agcgccattg gagccccgca
cttcc 25 <210> SEQ ID NO 30 <211> LENGTH: 25
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 30
cgccattgga gccccgcact tccac 25 <210> SEQ ID NO 31 <211>
LENGTH: 25 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 31 ttggagcccc gcacttccac cacca 25 <210> SEQ ID NO
32 <211> LENGTH: 28 <212> TYPE: DNA <213>
ORGANISM: Artificial Sequence <220> FEATURE: <223>
OTHER INFORMATION: Description of Artificial Sequence: Synthetic
primer <400> SEQUENCE: 32 agccccgcac ttccaccacc agctcctc 28
<210> SEQ ID NO 33 <211> LENGTH: 28 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 33 gagccccgca cttccaccac
cagctcct 28 <210> SEQ ID NO 34 <211> LENGTH: 28
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 34
cattggagcc ccgcacttcc accaccag 28 <210> SEQ ID NO 35
<211> LENGTH: 30 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 35 cccgcacttc caccaccagc tcctccatct 30
<210> SEQ ID NO 36 <211> LENGTH: 30 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 36 tagaaagcgc cattggagcc
ccgcacttcc 30 <210> SEQ ID NO 37 <211> LENGTH: 26
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 37
aagcgccatt ggagccccgc acttcc 26 <210> SEQ ID NO 38
<211> LENGTH: 36 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 38 tcaggcgctc agctccgttt cggtttcact tccggt 36
<210> SEQ ID NO 39 <211> LENGTH: 36 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 39 agcgtctact gtctcggcac
ttgcccgccg ccgccg 36 <210> SEQ ID NO 40 <211> LENGTH:
25 <212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 40
ggcgctcagc tccgtttcgg tttca 25 <210> SEQ ID NO 41 <211>
LENGTH: 43 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 41 tcaggcgctc agctccgttt cggtttcacg gcggcggcgg cgg 43
<210> SEQ ID NO 42 <400> SEQUENCE: 42 000 <210>
SEQ ID NO 43 <211> LENGTH: 41 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 43 aagcgccatt ggagccccgc
acttccccgc cgccgccgcc g 41 <210> SEQ ID NO 44 <211>
LENGTH: 44 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 44 tcaggcgctc agctccgttt cggtttcacg gcggcggcgg cgga 44
<210> SEQ ID NO 45 <211> LENGTH: 41 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 45 aagcgccatt ggagccccgc
acttccccgc cgccgccgcc t 41 <210> SEQ ID NO 46 <211>
LENGTH: 39 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic primer <400>
SEQUENCE: 46 tgcgcctccg ccgccgcggg cgcaggcacc gcaaccgca 39
<210> SEQ ID NO 47 <211> LENGTH: 33 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 47 cgcagcctgt agcaagctct
ggaactcagg agt 33 <210> SEQ ID NO 48 <211> LENGTH: 57
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic primer <400> SEQUENCE: 48
tgcgcctccg ccgccgcggg cgcaggcacc gcaaccgcac cccggccccg gccccgg 57
<210> SEQ ID NO 49 <211> LENGTH: 53 <212> TYPE:
DNA <213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 49 cgcagcctgt agcaagctct
ggaactcagg agtcgccggg gccggggccg ggg 53 <210> SEQ ID NO 50
<211> LENGTH: 22 <212> TYPE: DNA <213> ORGANISM:
Artificial Sequence <220> FEATURE: <223> OTHER
INFORMATION: Description of Artificial Sequence: Synthetic primer
<400> SEQUENCE: 50 ccaaagcatt gggattactg gc 22 <210>
SEQ ID NO 51 <211> LENGTH: 22 <212> TYPE: DNA
<213> ORGANISM: Artificial Sequence <220> FEATURE:
<223> OTHER INFORMATION: Description of Artificial Sequence:
Synthetic primer <400> SEQUENCE: 51 gattgcttga gcctaggcat tc
22 <210> SEQ ID NO 52 <211> LENGTH: 12 <212>
TYPE: DNA <213> ORGANISM: Artificial Sequence <220>
FEATURE: <223> OTHER INFORMATION: Description of Artificial
Sequence: Synthetic oligonucleotide <400> SEQUENCE: 52
aataataata at 12 <210> SEQ ID NO 53 <211> LENGTH: 10
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic oligonucleotide <400>
SEQUENCE: 53 aataaataat 10 <210> SEQ ID NO 54 <211>
LENGTH: 10 <212> TYPE: DNA <213> ORGANISM: Artificial
Sequence <220> FEATURE: <223> OTHER INFORMATION:
Description of Artificial Sequence: Synthetic oligonucleotide
<400> SEQUENCE: 54
aaataaaaat 10 <210> SEQ ID NO 55 <211> LENGTH: 10
<212> TYPE: DNA <213> ORGANISM: Artificial Sequence
<220> FEATURE: <223> OTHER INFORMATION: Description of
Artificial Sequence: Synthetic oligonucleotide <400>
SEQUENCE: 55 aataaaaaat 10
* * * * *