Whole Genome Sequencing
Services Guide
FOR RESEARCH USE ONLY
ILLUMINA PROPRIETARY
Document # 15040892 v01
December 2015
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Whole-Genome Sequencing Services Guide
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Document # 15040892 v01
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Whole-Genome Sequencing Services Guide
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Revision History
Document
Date
Description of Change
Document # 15040892
v01
December
2015
Part # 15040892 Rev. D
June 2015
• Revised documentation to reflect changes in version 4 of the
Illumina FastTrack WGS pipeline.
• Renamed Manta and Canvas to Isaac Structural Variant Caller
and Isaac Copy Number Variant Caller, respectively.
Part # 15040892 Rev. C
July 2014
Revised documentation to reflect changes in version 3 of the
Illumina FastTrack WGS pipeline.
Part # 15040892 Rev. B
July 2013
Added Circos plot legend plus minor modifications.
Part # 15040892 Rev. A
April 2013
Initial Release.
Whole-Genome Sequencing Services Guide
• Revised documentation reflect changes in version 6 of the
Illumina FastTrack WGS pipeline.
• Renamed Isaac Structural Variant Caller, Isaac CNV Caller, and
Isaac Variant Caller to Manta, Canvas, and Starling, respectively.
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Table of Contents
Revision History
Table of Contents
Chapter 1 Getting Started
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viii
1
Introduction
Data Delivery
2
3
Chapter 2 Analysis Deliverables
4
Overview
Result Folder Structure
Assembly
Genotyping
Variations
Summary Report
Chapter 3 Analysis Overview
Overview
Genome Specific Details
Isaac Aligner
Starling (Small Variant Caller)
gVCF (Genome VCF)
Canvas (Copy Number Variations Caller)
Manta (Large Indel and Structural Variant Caller)
Appendix A Appendix
BAM File Conversion
Illumina FastTrack Services Annotation Pipeline
Technical Assistance
Whole-Genome Sequencing Services Guide
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6
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Chapter 1 Getting Started
Introduction
Data Delivery
Whole-Genome Sequencing Services Guide
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3
1
Chapter 1
Getting Started
Getting Started
Introduction
The Whole Genome Sequencing Service leverages a suite of proven algorithms to detect
genomic variants comprehensively and accurately.
The Whole Genome Sequencing Service pipeline performs the following steps:
} Isaac aligns, trims, and flags duplicates in the raw sequence.
} Canvas generates copy number (CNV) and loss of heterozygosity (LOH) analysis calls.
} Manta generates structural variant (SV) analysis calls.
} Starling generates small variant (SNV and small indels ≤ 50 bp) analysis calls.
The variants are then annotated and the resulting statistics are compiled into a summary
PDF. The callers share output, and therefore there is no double-counting of calls.
Software Packages
The Whole Genome Sequencing Service pipeline uses the following software packages. For
the software versions used, see the Software Versions table in the summary PDF report
included with each deliverable.
Software
Isis (Analysis Software)
SAMtools
Isaac (Aligner)
Starling (Small Variant
Caller)
Manta (Structural
Variant Caller)
Canvas (CNV Caller)
Pluggable Universal
Metrics Analyzer
(PUMA)
PUMA Metrics
Purpose
Illumina Sequence Integration Software. Framework internally
utilized to run the alignment, calling, annotation, and metrics.
Public toolkit for working with the SAM/BAM format.
Aligns reads to the reference and marks duplicates.
Germline SNV and indel caller.
Germline and somatic structural variant caller.
Germline and somatic copy number variant caller.
Internal use only. Framework for producing metrics from BAM and
VCF File.
Internal use only. Specific version of modules for PUMA.
Most versions of the Illumina callers are open source and available puclicly. See the
Illumina GitHub for the current releases.
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Illumina FastTrack Services currently provides data delivery through the following choices.
Illumina Hard Drive Data Delivery
Illumina FTS ships data on 1 or more hard drives. The hard drives are formatted with the
NTFS file system and can optionally be encrypted.
The data on the hard drive are organized in a folder structure with 1 top-level folder per
sample or analysis.
Illumina Cloud Data Delivery
Illumina FTS uploads data to a cloud container. Illumina currently supports uploads to the
Amazon S3 service. Upload data are organized per upload batch by date with an Illumina_
FTS prefix. For example, a sample in a batch uploaded on February 1, 2015 would be found
in the container with the prefix Illumina_FTS/20140201/SAMPLE_BARCODE. Contact your
FastTrack Services project manager to enable cloud delivery.
Whole-Genome Sequencing Services Guide
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Data Delivery
Data Delivery
Chapter 2 Analysis Deliverables
Overview
Result Folder Structure
Assembly
Genotyping
Variations
Summary Report
Whole-Genome Sequencing Services Guide
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6
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10
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4
Chapter 2
Analysis Deliverables
Analysis Deliverables
Overview
This section details the files and folder structure for the Whole Genome Sequencing Service
results. The files and folders are named based on the unique sample identifiers. Usually,
these unique identifiers are the barcodes associated with the samples in the lab, but can be
a known sample ID for reference samples.
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Under each sample folder, you can find the following file structure that contains analysis
results.
[Sample_Barcode]
[Sample_Barcode].SummaryReport.csv—Summary report in *.csv format
[Sample_Barcode].SummaryReport.pdf—Summary report in *.pdf format
Assembly
[Sample_Barcode].bam—Archival *.bam file for sample
[Sample_Barcode].bam.bai—Index for *.bam file
Genotyping
[Sample_Barcode]_idats—Folder containing genotyping intensity data files for
the sample (*.idat files) and genotyping sample sheet
[Sample_Barcode].Genotyping.vcf.gz—Genotyping SNPs mapped to reference
in *.vcf format. This VCF format contains unmapped SNPs and is not fully
compatible with the VCF specification.
[Sample_Barcode].GenotypingReport.txt—Genotyping SNPs tab delimited
report output from GenomeStudio.
Variations
[Sample_Barcode].vcf.gz—Single nucleotyide polymorphism (SNVs) and small
insertion/deletion (1 bp–50 bp) calls in *.vcf format.
[Sample_Barcode].SV.vcf.gz—Large Structural Variation calls (51 bp–10 kb) and
copy number calls (10 kb+) in *.vcf format.
[Sample_Barcode].genome.vcf.gz—Genome *.vcf file containing SNVs, indels,
and reference covered regions
md5sum.txt—Checksum file for confirming file consistency.
NOTE
All the VCF files that Illumina provides are compressed and indexed using tabix. For details
about tabix, see the tabix manual in SAMtools (at samtools.sourceforge.net/tabix.shtml).
The tabix index shows up as an additional [Sample_Barcode].TYPE.vcf.gz.tbi file. It can be
used for fast retrieval of targeted regions in the associated *.vcf.gz file
Whole-Genome Sequencing Services Guide
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Result Folder Structure
Result Folder Structure
Analysis Deliverables
Assembly
The assembly folder contains the sequence data used to assemble the sample genome.
BAM File
The included archival BAM file contains all pass filter reads input into the analysis
pipeline for a sample and includes aligned, duplicate, and unaligned reads. To reduce the
data storage footprint without compromising accuracy, Illumina has reduced quality score
resolution in BAM files. The more commonly used 40+ possible Q-scores have been
reduced to 8 bins.
For details about the reduced storage requirements, see the Reducing Whole-Genome Data
Storage Footprint white paper on the Illumina product literature page.
BAM Index
This file is index for the BAM file and can be used with SAMtools and other tools utilizing
the SAMtools specification for fast retrieval of targeted regions in the associated BAM file.
BAM File Details
The included BAM file adheres to the SAM format specification wherever possible. The
following sections cover BAM file details that are not evident in the specification:
} Singleton / Shadow Pairs
} Read Groups: RG
} Read name: RNAME
} Bitwise Flag Notes: FLAG
} Extended Tags / Optional Fields
} MAPQ
Singleton / Shadow Pairs
Singleton/shadow pairs refer to pairs for which the aligner was unable to determine the
alignment of 1 of the ends. The determined end is the singleton and the undetermined end
is the shadow. Shadows are assigned the position of the end that does align. To maintain
SAMtools format compatibility, the shadows are stored in the BAM file immediately after
their respective singletons, with CIGAR empty and corresponding flag (4) set. Shadows can
be retrieved using the following SAMtools command:
samtools view -f 4 input.bam > output.sam
Read Groups: RG
Where possible, unique flow cell-lane-index mappings split up the read groups in the
BAM. The following is an example from a BAM header:
@RG ID:0 PL:ILLUMINA SM:NA12878 PU:C0L0AACXX:1:none
@RG ID:1 PL:ILLUMINA SM:NA12878 PU:C0L54ACXX:7:none
@RG ID:2 PL:ILLUMINA SM:NA12878 PU:C0L54ACXX:8:none
In the example, the read group 0 is derived from the flow cell barcode ID C0L0AACXX,
lane 1, without a specified index for sample NA12891. In this example, read groups 1 and
2 are from a different flow cell C0L54ACXX, lanes 7 and 8.
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The read name consists of the following pattern, which details the flow cell, lane, and tile
on which the sample was run:
flowcell-id ":" lane-number ":" tile-number ":"cluster-id
":”cluster-id-alt”
ID
Description
cluster-id
Unpadded 0-based cluster id in the order in which the clusters appear
within the tile.
flowcell-id
Flow cell barcode.
cluster- id-alt
In cases where the x:y coordinates from the flow cell were preserved, this
column contains the y-coordinate, whereas the cluster-id contains the xcoordinate. Otherwise this column always contains “0”.
lane-number
Lane number 1–8.
tile-number
Unpadded tile number.
Bitwise Flag Notes: FLAG
The bitwise flags used are described in the following table.
Bit
0x1
Description
Template having multiple segments in
sequencing.
0x2
Each segment properly aligned according to
the aligner.
0x4
Segment unmapped.
0x8
Next segment in the template unmapped.
0x10 SEQ being reverse complemented.
0x20 SEQ of the next segment in the template being
reversed.
0x40 The first segment in the template.
0x80 The last segment in the template.
0x100 Secondary alignment.
0x200 Not passing quality controls.
0x400 PCR or optical duplicate.
Note
Always set on for paired reads.
Pair matches dominant template
orientation.
Set for unmapped reads.
Paired read is unmapped.
Read mapped to strand of reference.
Paired read mapped to strand of reference.
Read 1 sequence.
Read 2 sequence.
Isaac does not produce secondary
alignments.
Nonpass filter reads are not included
(always off).
Read 1 and Read 2 were marked as
duplicate reads.
Extended Tags and Optional Fields
The aligner produces the following fields in the BAM file.
Field
Description
AS
Pair alignment score.
BC
Barcode string.
NM
Edit distance (mismatches and gaps) including the soft-clipped parts of the read.
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Assembly
Read Name: RNAME
Analysis Deliverables
Field
Description
OC
Original CIGAR for the realigned reads.
RG
Isaac read groups correspond to unique flow cell-lane-barcodes.
SM
Single read alignment score.
Mapping Quality (MAPQ)
For pairs that match the dominant template orientation, the MAPQ value in the AS field is
capped. For reads that are not members of a pair matching the dominant template
orientation, the MAPQ value in the SM field is capped at 60. The MAPQ could be
downgraded to 0 or set to be unknown (255) for alignments that do not have enough
evidence to be correctly scored.
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If available, variants called using the Infinium platform are compared to sequencing calls
to confirm identity and make sure that data are of high quality. This folder contains the
results of the genotyping SNP calls and the necessary files needed to regenerate them.
For software download files and documentation, see the GenomeStudio support page on
the Illumina website.
Genotyping Intensity Data Files Folder
The [Sample_Barcode]_idats folder is contains the GRN.idat and RED.idat intensity files
and the sample sheet for a genotyping sample.
These files along with the manifest, cluster, and genotyping product files can be imported
into the Illumina GenomeStudio software genotyping module to reproduce the genotyping
calls. The product files are on the downloads tab of the array support page.
To find the version of array chip used for your project, refer to the sample sheet in each
sample intensity data files folder. If available, the *.gtc files are also included.
Genotyping VCF File
The [Sample_Barcode].Genotyping.vcf.gz file contains the genotyping SNPs in VCF format.
The genotyping SNPs were mapped to the reference using megaBLAST and filtered for
unique mappings.
The following filters are applied to the variants:
} Intensity only SNPs
} Any match not aligning to the SNP
} Any probe with a hamming distance greater than or equal to 5
} Any probe where the highest scoring mapping site is not the best matching site (ie,
there is another site or sites within an identical hamming distance)
Any genotyping probe not matching the reference or excluded from the mapping is
mapped to chromosome “NA” in the VCF file.
NOTE
Because of the additions to the Genotyping VCF file to account for unmapped probes, the
files are not completely compatible with standard VCF specifications. However, you can still
use most tools designed to work with VCF files.
The VCF file contains the following fields.
INFO Fields
Field
Description
AL
Array alleles relative to the design strand of the array probe.
ST
The strand for the array alleles relative to the reference. A dash ( - )
denotes a reverse compliment.
GC
The GenCall score from the genotyping SNP call. (0.15 cutoff applied by
default).
GT
Genotype per VCF specification.
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Genotyping
Genotyping
Analysis Deliverables
FORMAT Fields
Field
Description
GC
The GenCall score from the genotyping SNP call. (0.15 cutoff applied by
default).
GT
Genotype per VCF specification.
FILTER Fields
Field
GTEX
NOCALL
Description
The exclude genotype filter. The genotype was excluded in the mapping,
possibly because the probe failed to find a reference map, failed to map
uniquely, or was an intensity-only based probe.
Genotype value was not called on array.
Genotyping Report
The [Sample_Barcode].GenotypingReport.txt file contains the genotyping report that is
output from the GenomeStudio Genotyping Module. Illumina provides the genotyping
report as a tab-delimited text file and includes a header followed by at least the following
columns.
11
Column
Description
Allele1—Design
The A allele call that is relative to the probe.
Allele1—Forward
The A allele call that is relative to the submitted sequence.
Allele2—Design
The B allele call that is relative to the probe.
Allele2—Forward
The B allele call that is relative to the submitted sequence.
GC Score
The GenCall score. This score is a quality metric assigned to every
genotype called, and generally indicates their reliability. GC scores
have a maximum of 1, and are calculated using information from the
clustering of the samples. Each SNP is evaluated based on the angle of
the clusters, dispersion of the clusters, overlap between clusters, and
intensity. Genotypes with lower GC scores are located furthest from
the center of a cluster and have a lower reliability.
Sample Barcode
The internal process identifier.
SNP Name
The SNP identifier. An rsID for dbSNP content.
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The variations folder contains the variant call output in VCF 4.1 format for the sample.
Each variant file that Illumina provides is compressed and includes an index for fast,
range-based access. All VCF files are annotated with the FastTrack Services Annotation
Pipeline and contain additional INFO fields pertaining to the annotations. For more
information, see Illumina FastTrack Services Annotation Pipeline on page 40.
[Sample_Barcode].vcf.gz and [Sample_Barcode].genome.vcf.gz
This file contains a combined output of all single nucleotide polymorphisms and indels,
respectively, called for a sample using Starling. Small indels are limited to 50 bp. Variants
> 50 bp are passed to Manta and Canvas.
The VCF file contains the following INFO, FORMAT, and FILTER fields.
INFO Fields
Field
Description
BLOCK_AVG_
min30p3a
Nonvariant site block. All sites in a block are constrained to be
nonvariant, have the same filter value, and have all sample values in the
range [x,y], where y ≤ max(x+3,(x*1.3)). All printed site block sample
values are the minimum observed in the region spanned by the block.
CIGAR
The CIGAR alignment for each alternate indel allele.
END
The end position of the region described in this record.
IDREP
Number of times RU is repeated in an indel allele.
REFREP
Number of times RU is repeated in the reference.
RU
The smallest repeating sequence unit extended or contracted in the indel
allele relative to the reference. If RUs are longer than 20 bases, they are
not reported.
SNVHPOL
SNV contextual homopolymer length.
SNVSB
SNV site strand bias.
Unphased
Indicates a record that is within the specified phasing window of another
variant, but could not be phased because of a lack of minimum read
support.
FORMAT Fields
ID
Description
AD
Allelic depths for the ref and alt alleles in the order listed. For indels, this
value includes only reads that confidently support each allele. Specifically,
includes reads for which the posterior probability is 0.999 or higher that
the read contains an indicated allele versus all other intersecting indel
alleles.
DP
Filtered base call depth used for site genotyping.
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Variations
Variations
Analysis Deliverables
ID
Description
DPF
Base calls filtered from input before site genotyping.
DPI
Read depth associated with indel, taken from the site preceding the indel.
GQ
Genotype quality.
GQX
Empirically calibrated variant quality score for variant sites, otherwise the
minimum of {Genotype quality assuming variant position, Genotype
quality assuming nonvariant position}.
GT
Genotype.
FILTER Fields
ID
Description
HighDepth
The locus depth is greater than 3× the mean chromosome depth.
HighDPFRatio
The fraction of base calls filtered out at a site is > 0.4.
IndelConflict
The locus is in a region with conflicting indel calls.
LowGQX
Locus GQX is < 30 or not present.
LowGQXHetSNP
Locus GQX is < 14 for het SNP.
LowGQXHomSNP
Locus GQX is < 14 for hom SNP.
LowGQXHetIns
Locus GQX is < 6 for het insertion.
LowGQXHomIns
Locus GQX is < 6 for hom insertion.
LowGQXHetAltIns
Locus GQX is < 6 for het-alt insertion.
LowGQXHetDel
Locus GQX is < 6 for het deletion.
LowGQXHomDel
Locus GQX is < 6 for hom deletion.
LowGQXHetAltDel
Locus GQX is < 6 for het-alt deletion.
PhasingConflict
Locus read evidence displays unbalanced phasing patterns.
PLOIDY_
CONFLICT
Genotype call from the variant caller is not consistent with
chromosome ploidy.
SiteConflict
The site genotype conflicts with the proximal indel call, which is
typically a heterozygous SNV call made inside a heterozygous
deletion.
[Sample_Barcode].SV.vcf.gz
The SV file contains structural variants (51 bp–10 kb) called from the sample using Manta
and Canvas.
The SV VCF file contains the following INFO, FORMAT, and FILTER fields.
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Variations
INFO
ID
Description
BND_DEPTH
Read depth at local translocation break-end.
CIEND
Confidence interval around END.
CIGAR
CIGAR alignment for each alternate indel allele.
CIPOS
Confidence interval around POS.
ColocalizedCavcas
Overlapped with a 10 kb + Canvas call.
END
End position of the variant described in this record.
EVENT
ID of event associated to break-end.
HOMLEN
Length of base pair identical microhomology at event breakpoints.
HOMSEQ
Sequence of base pair identical microhomology at event
breakpoints.
IMPRECISE
Imprecise structural variation.
INV3
Inversion break-ends open 3' of reported location.
INV5
Inversion break-ends open 5' of reported location.
JUNCTION_QUAL
Provides the QUAL value for only the adjacency in question.
LEFT_SVINSSEQ
Known left side of insertion for an insertion of unknown length.
MATE_BND_DEPTH
Read depth at remote translocation mate break-end.
MATEID
ID of mate break-end.
RIGHT_SVINSSEQ
Known right side of insertion for an insertion of unknown length.
SVTYPE
Type of structural variant.
SVLEN
Difference in length between REF and ALT alleles.
SVINSLEN
Length of insertion.
SVINSSEQ
Sequence of insertion.
FORMAT Fields
Field
Description
BC
Number of bins in the region.
CN
Copy number genotype for imprecise events.
GT
Genotype.
GQ
Genotype Quality.
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Analysis Deliverables
Field
Description
PR
Spanning paired-read support for the REF and ALT alleles in the
order listed.
RC
Mean counts per bin in the region.
SR
Split reads for the REF and ALT alleles in the order listed, for reads
where P(allele|read) > 0.999.
FILTER Fields
Field
Description
CLT10kb
Canvas call with length < 10 kb.
MaxDepth
Sample site depth is > 3× the mean chromosome depth near 1 or both
variant break-ends.
MaxMQ0Frac
For a small variant (< 1000 bases), the fraction of reads with MAPQ=0
around either break-end exceeds 0.4.
MGE10kb
Manta DEL or DUP call with length ≥ 10 kb.
MinGQ
GQ score is < 20.
NoPairSupport
For variants significantly larger than the paired read fragment size, no
paired reads support the alternate allele.
Ploidy
For DEL and DUP variants, the genotypes of overlapping variants with
smaller size are inconsistent with diploid expectation.
q10
Quality < 10.
[Sample_Barcode].genome.vcf.gz
The genome VCF file contains VCF formatted output for the SNVs, indels and block
compressed nonvariant position output. You can use this file to compare variants and
covered regions between samples quickly and in a space-efficient manner. The FILTER and
INFO fields are identical to the SNV and Indel VCF file, along with the block compressed
specific flags. For more information, see gVCF (Genome VCF) on page 24.
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Document # 15040892 v01
This PDF report contains an overview of the results for the samples and contains the
following sections.
Section
Description
Sample
Information
Contains an overview of high-level sample and sequence quality
metrics:
• Sample ID—Sample identifier
• Total PF Reads—Total number of reads used in the analysis
• Percent Q30 Bases—Number of bases with a quality score ≥ 30 out
of the total number of bases. Q-binning of the BAM file does not
affect this metric.
Read level Statistics
For each of the paired-end reads, the following metrics are reported:
• Total Aligned Reads—Total number of reads mapping to a
chromosome
• Percent Aligned Reads—Percent of reads mapping to a
chromosome
Base
Level Statistics
For each of the paired-end reads, the following metrics are reported:
• Percent Q30 Bases—Number of bases with a quality score ≥ 30 out
of the total number of bases. Q-binning of the BAM file does not
affect this metric.
• Total Aligned Bases—Number of bases that aligned to the
reference.
• Percent Aligned Bases—Percent of bases aligned to the reference.
• Mismatch Rate—The percent of bases mismatching the reference
for mapped reads.
Coverage
Histogram
Details the overall mean depth and displays a graph of bases covered
for every non-N base in the reference genome.
Variant Statistics
Breaks down SNVs and indels into total counts in overlapping regions
and annotated consequences. Complex indels are split into deletions
and insertions where appropriate. Consequence types for
overlapping transcripts are counted under the most severe transcript
consequence according to the annotation.
Structural Variants
Summary
Breaks down CNV and SV output into the classes of variants called.
Their total PASS count and the number of overlapping genes are
based on the annotation pipeline. For more information, see Illumina
FastTrack Services Annotation Pipeline on page 40.
Fragment Length
Summary
Details the fragment length statistics for the reads used in the analysis.
Duplicate
Information
Details the percent of reads marked as duplicates. Duplicate reads are
marked with Read 1 or Read 2 mapped positions overlapping with
the highest quality read pair left unmarked.
Analysis Details
Details the parameters and versions used in the analysis.
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Summary Report
Summary Report
Chapter 3 Analysis Overview
Overview
Genome Specific Details
Isaac Aligner
Starling (Small Variant Caller)
gVCF (Genome VCF)
Canvas (Copy Number Variations Caller)
Manta (Large Indel and Structural Variant Caller)
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19
20
22
24
29
34
17
Chapter 3
Analysis Overview
Analysis Overview
Overview
After the sequencer generates base calls and quality scores, the resulting data are first
aligned to the reference genome. Then assembly and variant calling is performed.
Alignment and variant calling are performed with the Isaac Aligner, Starling, Canvas, and
Manta. The following output is produced:
} Realigned and duplicate marked reads in a BAM file format.
} Variants in a VCF file format.
} An additional Genome VCF (gVCF) file. This file features an entry for every base in the
reference, which differentiates reference calls and no calls, and a summary of quality.
The reference calls are block compressed and all single nucleotide polymorphisms and
indels are included. Currently Structural Variants and CNVs are kept in separate files.
Figure 1 Whole-Genome Sequencing Pipeline
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Illumina uses iGenomes as the source for the reference genomes used in alignment and
assembly. The internally used genome can differ slightly from the iGenomes version in that
the pseudoautosomal region (PAR) of the Y chromosome is hard masked with N’s. This
difference is done to avoid false mapping of reads; any mapping occurring in the PAR
regions maps to the X chromosome. The reference genome used for a particular build is
specified in both the BAM header and the summary output files.
The ncbi37/hg18/GRCh37 PAR regions are defined as follows.
Name
PAR#1
PAR#2
PAR#1
PAR#2
Chr
X
X
Y
Y
Start
60,001
154,931,044
10,001
59,034,050
Stop
2,699,520
155,260,560
2,649,520
59,363,566
The ncbi38/hg38/GRCh38 PAR regions are defined as follows.
Name
PAR#1
PAR#2
PAR#1
PAR#2
Chr
X
X
Y
Y
Whole-Genome Sequencing Services Guide
Start
10,001
155,701,383
10,001
56,887,903
Stop
2,781,479
156,030,895
2,781,479
57,217,415
19
Genome Specific Details
Genome Specific Details
Analysis Overview
Isaac Aligner
The Isaac Aligner aligns DNA sequencing data, single or paired-end, with read lengths 32–
150 bp and low error rates using the following steps:
} Candidate mapping positions—Identifies the complete set of relevant candidate
mapping positions using a 32-mer seed-based search.
} Mapping selection—Selects the best mapping among all candidates.
} Alignment score—Determines alignment scores for the selected candidates based on a
Bayesian model.
} Alignment output—Generates final output in a sorted duplicate-marked BAM file, and
summary file.
Come Raczy, Roman Petrovski, Christopher T. Saunders, Ilya Chorny, Semyon Kruglyak,
Elliott H. Margulies, Han-Yu Chuang, Morten Källberg, Swathi A. Kumar, Arnold Liao, Kristina
M. Little, Michael P. Strömberg and Stephen W. Tanner (2013) Isaac: Ultra-fast whole genome
secondary analysis on Illumina sequencing platforms. Bioinformatics 29(16):2041-3
bioinformatics.oxfordjournals.org/content/29/16/2041
Candidate Mapping
To align reads, the Isaac Aligner first identifies a small but complete set of relevant
candidate mapping positions. The Isaac Aligner begins with a seed-based search using 32mers from the extremities of the read as seeds. Isaac Aligner performs another search using
different seeds for only those reads that were not mapped unambiguously with the first
pass seeds.
Mapping Selection
Following a seed-based search, the Isaac Aligner selects the best mapping among all the
candidates. For paired-end data sets, all mappings where only one end is aligned (called
orphan mappings) trigger a local search to find additional mapping candidates. These
candidates (called shadow mappings) are defined through the expected minimum and
maximum insert size. After optional trimming of low quality 3' ends and adapter
sequences, the possible mapping positions of each fragment are compared. This step takes
into account pair-end information (when available), possible gaps using a banded SmithWaterman gap aligner, and possible shadows. The selection is based on the SmithWaterman score and on the log-probability of each mapping.
Alignment Scores
The alignment scores of each read pair are based on a Bayesian model, where the
probability of each mapping is inferred from the base qualities and the positions of the
mismatches. The final mapping quality (MAPQ) is the alignment score, truncated to 60 for
scores above 60, and corrected based on known ambiguities in the reference flagged during
candidate mapping. Following alignment, reads are sorted. Further analysis is performed to
identify duplicates and optionally to realign indels.
Alignment Output
}
20
After sorting the reads, the Isaac Aligner generates compressed binary alignment
output files, called BAM (*.bam) files, using the following process:
Document # 15040892 v01
}
}
Marking duplicates—Detection of duplicates is based on the location and observed
length of each fragment. The Isaac Aligner identifies and marks duplicates even when
they appear on oversized fragments or chimeric fragments.
Realigning indels—The Isaac Aligner tracks previously detected indels, over a window
large enough for the current read length, and applies the known indels to all reads
with mismatches.
Generating BAM files—The first step in BAM file generation is creation of the BAM
record, which contains all required information except the name of the read. The Isaac
Aligner reads data from base call (BCL) files that were written during base calling on
the sequencer to generate the read names. Data are then compressed into blocks of 64
kb or less to create the BAM file.
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Isaac Aligner
}
Analysis Overview
Starling (Small Variant Caller)
Starling identifies single nucleotide variants (SNVs) and small indels using the following
steps:
} Read filtering—Filters out reads failing quality checks.
} Indel candidate discovery and realignment—Finds possible indels present in multiple
reads and realigns all reads overlapping these candidates.
} SNV calling—Computes the probability of each possible genotype given the aligned
read data and a prior distribution of variation in the genome.
} Short range phasing—SNPs within 2 bases of each other, and therefore close enough to
be in a single codon, are combined into a single, phased block substitution when read
evidence indicates the presence of a consistent diploid solution.
} Indel calling—Analagous to SNV calling, but used for candidate indels.
} Variant rescoring—Assigns final confidence scores based on empirically-fitted models.
Read Filtering
Input reads are filtered by removing any of the following reads:
} Reads that failed base calling quality checks
} Reads marked as PCR duplicates
} Paired-end reads not marked as a proper pair
} Reads with a mapping quality < 20
Indel Candidate Discovery and Realignment
The variant caller proceeds with candidate indel discovery and generates alternate read
alignments based on the candidate indels. As part of the realignment process, the variant
caller selects a representative alignment to be used for site genotype calling and depth
summarization by the SNV caller.
SNV Calling
The variant caller runs a series of filters on the set of filtered and realigned reads for SNV
calling without affecting indel calls. First, any contiguous trailing sequence of N base calls
is trimmed from the ends of reads. Using a mismatch density filter, reads having an
unexpectedly high number of disagreements with the reference are masked, as follows:
} The variant caller treats each insertion or deletion as a single mismatch.
} Base calls with more than 2 mismatches to the reference sequence within 20 bases of
the call are ignored.
} If the call occurs within the first or last 20 bases of a read, the mismatch limit is
applied to a 41-base window at the corresponding end of the read.
} The mismatch limit is applied to the entire read when the read length is 41 or shorter.
The variant caller filters out all bases marked by the mismatch density filter and any N
base calls that remain after the end-trimming step. These filtered base calls are not used for
site-genotyping, but appear in the filtered base call counts in the variant caller output for
each site.
All remaining base calls are used for site-genotyping. The genotyping method heuristically
adjusts the joint error probability that is calculated from multiple observations of the same
allele on each strand of the genome. This correction accounts for the possibility of error
dependencies.
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Short Range Phasing
SNPs within 2 bases of each other, and therefore close enough to be in a single codon, are
subject to a postprocessing step. This step can merge them into block substitutions that
specify the phasing of the original variants.
Blocks of 2 or more heterozygous variants such that adjacent pairs are within 2 bases of
each other are identified. Reads fully spanning a given block are used to score possible
haplotype pairs, and if 1 pair of haplotypes is superior to all other alternatives, this pair is
output as a block substitution. If, instead, no single pair is clearly best, the variants are
output as the original individual calls but with the specification ‘HaplotypeConsistency' in
the FILTER field.
Indel Calling
Indel candidates are used to score possible indel genotypes similar to the process described
for SNVs. Unlike SNVs, there is no correlated error model (ie, reads are treated as fully
independent). Indel error probabilities are assigned based on the length of homopolymer
runs in the reference and the hypothesized genome implied by an indel candidate.
Variant Rescoring and Filtering
A final calibrated confidence score (GQX) is computed for most variant calls, and this score
is used to filter dubious calls. The calibrated score is based on an empirical model fitted to
a reference truth set from the Platinum Genomes project. Predictor features are determined
for each variant call (depth of coverage, strand bias, genotype likelihood, mapping, and
base qualities, and so on). Features are normalized according to average sequencing depth
per chromosome and combined in a logistic regression model to derive a final Q-score.
This Q-score is then compared against precomputed cutoffs chosen to balance precision
and recall for a separate reference truth set, to determine whether the variant is reported as
PASS or filtered.
Variant Call Output
After the SNV and indel genotyping methods and variant rescoring are complete, the
variant caller applies a final set of heuristic filters and merges invariant positions with
similar properties (depth of coverage, confidence score, and so on) into block records. Then
the variant caller reconciles certain conflicts arising when indels overlap other indels or
SNVs.
The final output is in the form of a genome variant call (gVCF) file.
Whole-Genome Sequencing Services Guide
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Starling (Small Variant Caller)
This method treats the highest-quality base call from each allele and strand as an
independent observation and leaves the associated base call quality scores unmodified.
Quality scores for subsequent base calls for each allele and strand are then adjusted. This
adjustment is done to increase the joint error probability of the given allele above the error
expected from independent base call observations.
Analysis Overview
gVCF (Genome VCF)
Human genome sequencing applications require sequencing information for both variant
and nonvariant positions, yet there is no common exchange format for such data. gVCF
addresses this issue.
gVCF is a set of conventions applied to the standard variant call format (VCF). These
conventions allow representation of genotype, annotation, and additional information
across all sites in the genome, in a reasonably compact format. Typical human wholegenome sequencing results expressed in gVCF with annotation are less than 1.7 GB, or
about 1/50 the size of the BAM file used for variant calling.
gVCF is also equally appropriate for representing and compressing targeted sequencing
results. Compression is achieved by joining contiguous nonvariant regions with similar
properties into single ‘block’ VCF records. To maximize the utility of gVCF, especially for
high stringency applications, the properties of the compressed blocks are conservative.
Block properties such as depth and genotype quality reflect the minimum of any site in the
block. The gVCF file is also a valid VCF v4.1 file, and can be indexed and used with
existing VCF tools such as tabix and IGV. This feature makes the file convenient both for
direct interpretation and as a starting point for further analysis.
gvcftools
Illumina has created a full set of utilities aimed at creating and analyzing Genome VCF
files. For information and downloads, visit the gvcftools website at
sites.google.com/site/gvcftools/home.
Examples
The following is a segment of a VCF file following the gVCF conventions for representation
of nonvariant sites and, more specifically, using gvcftools block compression and filtration
levels.
In the following gVCF example, nonvariant regions are shown in normal text and variants
are shown in bold.
NOTE
The variant lines can be extracted from a gVCF file to produce a conventional variant VCF
file.
chr20 676337 . T . 0.00 PASS END=676401;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:143:51:0
chr20 676402 . A . 0.00 PASS END=676441;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:169:57:0
chr20 676442 . T G 287.00 PASS SNVSB=-30.5;SNVHPOL=3
GT:GQ:GQX:DP:DPF:AD 0/1:316:287:66:1:33,33
chr20 676443 . T . 0.00 PASS END=676468;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:202:68:1
chr20 676469 . G . 0.00 PASS . GT:GQX:DP:DPF 0/0:199:67:5
chr20 676470 . A . 0.00 PASS END=676528;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:157:53:0
chr20 676529 . T . 0.00 PASS END=676566;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:120:41:0
chr20 676567 . C . 0.00 PASS END=676574;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:114:39:0
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gVCF (Genome VCF)
chr20 676575 . A T 555.00 PASS SNVSB=-50.0;SNVHPOL=3
GT:GQ:GQX:DP:DPF:AD 1/1:114:114:39:0:0,39
chr20 676576 . T . 0.00 PASS END=676625;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:95:36:0
chr20 676626 . T . 0.00 PASS END=676650;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:117:40:0
chr20 676651 . T . 0.00 PASS END=676698;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:90:31:0
chr20 676699 . T . 0.00 PASS END=676728;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:69:24:0
chr20 676729 . C . 0.00 PASS END=676783;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:57:20:0
chr20 676784 . C . 0.00 PASS END=676803;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:51:18:0
chr20 676804 . G A 62.00 PASS SNVSB=-7.5;SNVHPOL=2
GT:GQ:GQX:DP:DPF:AD 0/1:95:62:17:0:11,66
chr20 676805 . C . 0.00 PASS END=676818;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:48:17:0
chr20 676819 . T . 0.00 PASS END=676824;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:39:14:0
chr20 676825 . A . 0.00 PASS END=676836;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:30:11:0
chr20 676837 . T . 0.00 LowGQX END=676857;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:21:8:0
chr20 676858 . G . 0.00 PASS END=676873;BLOCKAVG_min30p3a
GT:GQX:DP:DPF 0/0:30:11:0
In addition to the nonvariant and variant regions in the example, there is also 1
nonvariant region from [676837,676857] that is filtered out due to insufficient confidence
that the region is homozygous reference.
Conventions
Any VCF file following the gVCF convention combines information on variant calls (SNVs
and small-indels) with genotype and read depth information for all nonvariant positions in
the reference. Because this information is integrated into a single file, distinguishing
variant, reference, and no-call states for any site of interest is straightforward.
The following subsections describe the general conventions followed in any gVCF file, and
provide information on the specific parameters and filters used in the Isaac workflow gVCF
output.
Note
gVCF conventions are written with the assumption that only one sample per file is being
represented.
Interpretation
gVCFs file can be interpreted as follows:
} Fast interpretation—As a discrete classification of the genome into ‘variant’, ‘reference’,
and ‘no-call’ loci. This classification is the simplest way to use the gVCF. The Filter
fields for the gVCF file have already been set to mark uncertain calls as filtered for both
variant and nonvariant positions. Simple analysis can be performed to look for all loci
with a filter value of “PASS” and treat them as called.
Whole-Genome Sequencing Services Guide
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Analysis Overview
}
Research interpretation—As a ‘statistical’ genome. Additional fields, such as genotype
quality, are provided for both variant and reference positions to allow the threshold
between called and uncalled sites to be varied. These fields can also be used to apply
more stringent criteria to a set of loci from an initial screen.
External Tools
gVCF is written to the VCF 4.1 specifications, so any tool that is compatible with the
specification (such as IGV and tabix) can use the file. However, certain tools are not
appropriate if they:
} Apply algorithms to VCF files that make sense for only variants calls (as opposed to
variant and nonvariant regions in the full gVCF);
} Are only computationally feasible for variant calls.
For these cases, extract the variant calls from the full gVCF file.
Special Handling for Indel Conflicts
Sites that are "filled in" inside deletions have additional treatment.
} Heterozygous Deletions—Sites inside heterozygous deletions have haploid genotype
entries (ie "0" instead of "0/0", "1" instead of "1/1"). Heterozygous SNVs are marked
with the SiteConflict filter and their original genotype is left unchanged. Sites inside
heterozygous deletions cannot have a genotype quality score higher than the enclosing
deletion genotype quality.
} Homozygous Deletions—Sites inside homozygous deletions have genotype set to "."
(period), and site and genotype quality are also set to "." (period).
} All Deletions—Sites inside any deletion are marked with the filters of the deletion, and
more filters can be added pertaining to the site itself. These modifications reflect the
idea that the enclosing indel confidence bounds the site confidence.
} Indel Conflicts—In any region where overlapping deletion evidence cannot be resolved
into 2 haplotypes, all indel and set records in the region are marked with the
IndelConflict filter.
Table 1 Indel Conflict Filters
ID
Type
Description
IndelConflict site/indel Locus is in region with conflicting indel calls.
SiteConflict
site
Site genotype conflicts with proximal indel call. This conflict is typically a
heterozygous genotype found inside a heterozygous deletion.
Representation of Nonvariant Segments
This section includes the following subsections:
} Block representation using END key
} Joining nonvariant sites into a single block record
} Block sample values
} Nonvariant block implementations
Block Representation Using END Key
Continuous nonvariant segments of the genome can be represented as single records in
gVCF. These records use the standard 'END" INFO key to indicate the extent of the record.
Even though the record can span multiple bases, only the first base is provided in the REF
field (to reduce file size). Following is a simplified example of a nonreference block record:
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Document # 15040892 v01
The example record spans positions [51845,51862].
Joining Nonvariant Sites Into a Single Block Record
Address the following issues when joining adjacent nonvariant sites into block records:
} The criteria that allow adjacent sites to be joined into a single block record.
} The method to summarize the distribution of SAMPLE or INFO values from each site
in the block record.
At any gVCF compression level, a set of sites can be joined into a block if...
} Each site is nonvariant with the same genotype call. Expected nonvariant genotype
calls are { "0/0", "0", "./.", "." }.
} Each site has the same coverage state, where 'coverage state' refers to whether at least 1
read maps to the site. For example, sites with 0 coverage cannot be joined into the same
block with covered sites.
} Each site has the same set of FILTER tags.
} Sites have less than a threshold fraction of nonreference allele observations compared
to all observed alleles (based on AD and DP field information). This threshold is used
to keep sites with high ratios of nonreference alleles from being compressed into
nonvariant blocks. In the Starling gVCF output, the maximum nonreference fraction is
0.2
Block Sample Values
Any field provided for a block of sites, such as read depth (using the DP key), shows the
minimum observed value among all sites encompassed by the block.
Nonvariant Block Implementations
Files conforming to the gVCF conventions delineated in this document can use different
criteria for creation of block records, depending on the desired trade-off between
compression and nonvariant site detail. Starling provides the blocking scheme 'min30p3a'
as the nonvariant block compression scheme.
Each sample value shown for the block, such as the depth (using the DP key), is restricted
to have a range where the maximum value is within 30% or 3 of the minimum. Therefore,
for sample value range [x,y], y ≤ x+max(3, x*0.3). This range restriction applies to all
sample values written in the final block record.
Genotype Quality for Variant and Nonvariant Sites
The gVCF file uses an adapted version of genotype quality for variant and nonvariant site
filtration. This value is associated with the GQX key. The GQX value is intended to
represent the minimum of Phred genotype quality {assuming the site is variant, assuming
the sites is nonvariant}.
You can use this value to allow a single value to be used as the primary quality filter for
both variant and nonvariant sites. Filtering on this value corresponds to a conservative
assumption appropriate for applications where reference genotype calls must be
determined at the same stringency as variant genotypes, for example:
} An assertion that a site is homozygous reference at GQX ≥ 30 is made assuming the
site is variant.
Whole-Genome Sequencing Services Guide
27
gVCF (Genome VCF)
##INFO=<ID=END,Number=1,Type=Integer,Description="End position of
the variant described in this record">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA19238
chr1 51845 . A . . PASS END=51862
Analysis Overview
}
An assertion that a site is a nonreference genotype at GQX ≥ 30 is made assuming the
site is nonvariant.
Filter Criteria
The gVCF FILTER description is divided into 2 sections: (1) describes filtering based on
genotype quality; (2) describes all other filters.
NOTE
These filters are default values used in the current Starling implementation. However, no set
of filters or cutoff values are required for a file to conform to gVCF conventions.
The genotype quality is the primary filter for all sites in the genome. In particular,
traditional discovery-based site quality values that convey confidence that the site is
"anything besides the homozygous reference genotype," such as SNV quality, are not used.
Instead, a site or locus is filtered based on the confidence in the reported genotype for the
current sample.
The genotype quality used in gVCF is a Phred-scaled probability that the given genotype is
correct. It is indicated with the FORMAT field tag GQX. Any locus where the genotype
quality is below the cutoff threshold is filtered with the tag LowGQX. In addition to filtering
on genotype quality, some other filters are also applied.
For more information, see the small variants and genome VCF FILTER Fields on page 13.
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Document # 15040892 v01
Canvas is an algorithm for calling copy number variants from a diploid sample. Most of a
normal DNA sample is diploid, or having 2 copies. Canvas identifies regions of the sample
genome that are not present, or present either one time or more than 2 times in the genome.
Canvas scans the genome for regions having an unexpected number of short read
alignments. Regions with fewer than the expected number of alignments are classified as
losses. Regions having more than the expected number of alignments are classified as
gains.
Canvas is appropriately applied to low-depth cytogenetics experiments, low-depth singlecell experiments, or whole-genome sequencing experiments. Canvas is not appropriate for
whole exome experiments, cancer studies, or any other experiment with the following
conditions:
} Most of the genome is not assumed to be diploid.
} Reads are not distributed randomly across the diploid genome.
Workflow
Canvas can be conceptually divided into 4 processes:
} Binning—Counting alignments in genomic bins.
} Cleaning—Removal of systematic biases and outliers from the counts.
} Partitioning—Partitioning the counts into homogenous regions.
} Calling—Assigning a copy number to each homogenous region.
These processes are explained in subsequent sections.
Binning
The binning procedure creates genomic windows, or bins, across the genome and counts
the number of observed alignments that fall into each bin. The alignments are provided in
the form of a BAM file.
Canvas binning keeps in memory a collection of BitArrays to store observed alignments,
one BitArray for each chromosome. Each BitArray length is the same as its corresponding
chromosome length. As the BAM file is read in, Canvas records the position of the left-most
base in each alignment within the chromosome-appropriate BitArray. After all alignments
in the BAM file have been read, the BitArrays have a “1” wherever an alignment was
observed and a “0” everywhere else.
After reading in the BAM file, a masked FASTA file is read in, one chromosome at a time.
This FASTA file contains the genomic sequences that were used for alignment. Each 35-mer
within this FASTA file is marked as unique or nonunique with uppercase and lowercase
letters. If a 35-mer is unique, then its first nucleotide is capitalized; otherwise, it is not
capitalized. For example, in the sequence:
acgtttaATgacgatGaacgatcagctaagaatacgacaatatcagacaa
The 35-mers marked as unique are as follows:
ATGACGATGAACGATCAGCTAAGAATACGACAATA
TGACGATGAACGATCAGCTAAGAATACGACAATAT
GAACGATCAGCTAAGAATACGACAATATCAGACAA
Canvas stores the genomic locations of unique 35-mers in another collection of BitArrays
analogous to BitArrays used to store alignment positions. Unique positions and nonunique
positions are marked with “1”s and “0”s, respectively. This marking is used as a mask to
Whole-Genome Sequencing Services Guide
29
Canvas (Copy Number Variations Caller)
Canvas (Copy Number Variations Caller)
Analysis Overview
guarantee that only alignments that start at unique 35-mer positions in the genome are
used.
Bin Sizes
Canvas is initialized with 100 alignments per bin and then proceeds to compute the bin
boundaries such that each bin contains the same bin size, or number of unique 35-mers.
The term “bin size“ refers to the number of unique genomic 35-mers per bin. Because some
regions of the human genome are more repetitive than others, physical bin sizes (in
genomic coordinates) are not identical. In the following example, each box is a position
along the genome. Each checkmark represents a unique 35-mer while each X represents a
nonunique 35-mer. The bin size in this example is 3 (3 checkmarks per bin). The physical
size of each bin is not constant. B1 and B3 have a physical size of 3 but B2 and B4 have
physical sizes of 4 and 6, respectively.
Computing Bin Size
To compute bin size, the ratio of observed alignments to unique 35-mers is calculated for
each autosome. The desired number of alignments per bin is then divided by the median of
these ratios to yield bin size. For whole-genome sequencing, bin sizes are typically in the
range of 800–1000 unique 35-mers. Correspondingly, most physical window sizes are in
the 1–1.2 kb range. The advantage of this approach relative to using fixed genomic
intervals is that the same number of reads map to each bin, regardless of “uniqueness” or
ability to be mapped.
After bin size is computed, bins are defined as consecutive genomic windows such that
each bin contains the same bin size, or number of unique 35-mers. The number of observed
alignments present within the boundary of each bin is then counted from the alignment
BitArrays. The GC content of each bin is also calculated. The chromosome, genomic start,
genomic stop, observed counts and GC content in each bin are output to disk.
Cleaning
Canvas cleaning comprises the following 3 procedures that remove outliers and systematic
biases from the count data computed in the caller.
1
Single point outlier removal.
2
Physical size outlier removal.
3
GC content correction.
These procedures are performed on the bins produced during the Canvas binning process.
Single Point Outlier Removal
This step removes individual bins that represent extreme outliers. These bins have counts
that are very different from the counts present in upstream and downstream bins. Two
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A value of χ2 greater than 6.635, which is the 99th percentile of the Chi-squared
distribution with 1 degree of freedom, is considered very different. If a bin count is very
different from the count of both upstream and downstream neighbors, then the bin is
deemed an outlier and removed.
Physical Size Outlier Removal
Bins likely do not have the same physical (genomic) size. The average for whole-genome
sequencing runs might be approximately 1 kb. If the bins cover repetitive regions of the
genome, some bins sizes might be several megabases in size. Example regions might
include centromeres and telomeres. The counts in these regions tend to be unreliable so
bins with extreme physical size are removed. Specifically, the 98th percentile of observed
physical sizes is calculated and bins with sizes larger than this threshold are removed.
GC Content Correction
The main variability in bins counts is GC content. An example of the bias is represented in
the following figure.
Figure 2 GC Bias Example
The following correction is performed:
1
Bins are first aggregated according to GC content, which is rounded to the nearest
integer.
2
Second, each bin count is divided by the median count of bins having the same GC
content.
3
Finally, this value is multiplied by the desired average count per bin (100 by default)
and rounded to the nearest integer. The effect is to flatten the midpoints of the bars in
the example box-and-whisker plot.
Some values for GC content have few bins so the estimate of its median is not robust.
Therefore, bins are discarded when the number of bins having the same GC content is
fewer than 100.
Whole-Genome Sequencing Services Guide
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Canvas (Copy Number Variations Caller)
values, a and b, are defined as to be very different when their difference is greater than
expected by chance, assuming a and b come from the same underlying distribution. These
values use the Chi-squared distribution, as follows:
} µ = 0.5a + 0.5b
} χ2 = ((a - µ)2 + (b - µ)2) µ-1
Analysis Overview
For some sample preparation schemes, GC content correction has a dramatic effect. The
following figure illustrates the effect of GC content correction for a low depth sequencing
experiment using the Nextera library preparation method. The figure on the left shows bins
counts as a function of chromosome position before normalization. The figure on the right
shows the result after GC content correction.
Figure 3 GC Content Correction
For whole-genome sequencing experiments, the typically median absolute deviations
(MADs) are 10.3, which is close to the expected value of 10. The expected value is predicted
using the Poisson model for an average count of 100 and indicates that little bias remains
following GC content correction.
It is important to note that the normalization signal does not dampen signal from CNVs as
shown in the following 2 figures. The figure on the left shows a chromosome known to
harbor a single copy gain. The figure on the right shows chromosome known to harbor a
double copy gain.
Figure 4 Chromosomal Copy Gain
Partitioning
Canvas partitioning implements an algorithm for identifying regions of the genome such
that their average counts are statistically different than average counts of neighboring
regions. The implementation is a port of the circular binary segmentation (CBS) algorithm.
The algorithm briefly considers each chromosome as a segment. The algorithm assesses
each segment and identifies the pair of bins for which the counts in the bins between them
are maximally different than the counts of the rest of the bins. The statistical significance of
the maximal difference is assessed via permutation testing. If the difference is statistically
significant, then the procedure is applied recursively to the 2 or 3 segments created by
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Because of the computational complexity of the algorithm O(N2), the problem is divided
into subchromosome problems followed by merging, in practice. Heuristics are used to
speed up the permutation testing.
Calling
The final module of the Canvas algorithm is to assign discrete copy numbers to each of the
regions identified by the Canvas partitioner.
A Gaussian model is used as the default calling method. In this case, both the mean and
standard deviation are estimated from the data for the diploid model and adjusted for the
other copy number models. For example, if the mean, µ, and standard deviation, σ, are
estimated to be 100 and 15 in the diploid model, then corresponding estimates in the
haploid model would be µ/2 and σ/2. The mean and standard deviation are estimated
using the autosomal median and MAD of counts. This model is the default as it is more
appropriate in cases where the spread of counts is higher than expected from the Poisson
model due to unaccounted sources of variability. An example of this case is single cell
sequencing experiments where whole-genome amplification is required.
Following assignment of copy number states, neighboring regions that received the same
copy number call are merged into a single region.
Phred-scaled Q-scores are assigned to each region using a simple logistic function derived
using array CGH data as the gold standard. The probability of a miscall is modeled as
p=1-(1/((1+e^(0.5532-0.147N)))
Where N is the number of bins found within the nondiploid region. This probability is
converted to a Q-score by
q=-10 log p
This estimate is likely conservative as it is derived from array CGH. Importantly, Q-scores
are a function of number of bins, not genomic size, so they are applicable to experiments of
any sequencing depth, including low-depth cytogenetics screening.
The coordinates of nondiploid regions and their Q-scores are output to a VCF file. Two
filters are applied to PASS variants. First, a variant must have a Q-score of Q10 or greater.
Second, a variant must be of size 10 kb, or greater.
Whole-Genome Sequencing Services Guide
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Canvas (Copy Number Variations Caller)
partitioning the current segment by the identified pair of points. Input to the algorithm is
the output generated by the Canvas cleaning algorithm.
Analysis Overview
Manta (Large Indel and Structural Variant Caller)
The large indel and structural variant calling method (Manta) is a structural variant caller
for short sequencing reads. It can discover structural variants of any size and score these
variants using both a diploid genotype model and a somatic model (when separate tumor
and normal samples are specified). Structural variant discovery and scoring incorporate
both paired read fragment spanning and split read evidence.
For more information, see the publication Manta: Rapid detection of structural variants and
indels for clinical sequencing applications or the Manta GitHub.
Chen,X., Schulz-Trieglaff,O., Shaw,R. et al. (2015) Manta: Rapid detection of structural variants
and indels for clinical sequencing applications. Bioinformatics. Advance online publication. doi:
10.1101/024232
Method Overview
Manta works by dividing the structural variant discovery process into 2 primary steps–
scanning the genome to find SV associated regions and analysis, scoring, and output of
SVs found in such regions.
1
Build SV association graph
Scan the entire genome to discover evidence of possible SVs and large indels. This
evidence is enumerated into a graph with edges connecting all regions of the genome
that have a possible SV association. Edges can connect 2 different regions of the
genome to represent evidence of a long-range association, or an edge can connect a
region to itself to capture a local indel/small SV association. These associations are
more general than a specific SV hypothesis, in that many SV candidates can be found
on 1 edge, although typically only 1 or 2 candidates are found per edge.
2
Analyze graph edges to find SVs
Analyze individual graph edges or groups of highly connected edges to discover and
score SVs associated with the edges. The substeps of this process include:
} Inference of SV candidates associated with the edge.
} Attempted assembly of the SVs break-ends.
} Scoring and filtration of the SV under various biological models (currently diploid
germline and somatic).
} Output to VCF.
Capabilities
Manta can detect all structural variant types that are identifiable in the absence of copy
number analysis and large scale de novo assembly. Detectable types are enumerated in this
section.
For each structural variant and indel, Manta attempts to align the break-ends to base pair
resolution and report the left-shifted break-end coordinate (per the VCF 4.1 SV reporting
guidelines). Manta also reports any break-end microhomology sequence and inserted
sequence between the break-ends. Often the assembly fails to provide a confident
explanation of the data. In such cases, the variant is reported as IMPRECISE, and scored
according to the paired-end read evidence alone.
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Document # 15040892 v01
Detected Variant Classes
Manta is able to detect all variation classes that can be explained as novel DNA
adjacencies in the genome. Simple insertion/deletion events can be detected down to a
configurable minimum size cutoff (defaulting to 51). All DNA adjacencies are classified
into the following categories based on the break-end pattern:
} Deletions
} Insertions
} Inversions
} Tandem Duplications
} Interchromosomal Translocations
Known Limitations
Manta cannot detect the following variant types:
} Nontandem repeats/amplifications
} Large insertions—The maximum detectable size corresponds to approximately the
read-pair fragment size, but note that detection power falls off to impractical levels well
before this size.
The FastTrack Whole-Genome Sequencing service reports called variants that are 50–10
kb in size.
} Small inversions—The limiting size is not tested, but in theory detection falls off below
~200 bases. So-called microinversions might be detected indirectly as combined
insertion/deletion variants.
More general repeat-based limitations exist for all variant types:
} Power to assemble variants to break-end resolution falls to 0 as break-end repeat length
approaches the read size.
} Power to detect any break-end falls to (nearly) 0 as the break-end repeat length
approaches the fragment size.
} The method cannot detect nontandem repeats.
While Manta classifies novel DNA-adjacencies, it does not infer the higher level constructs
implied by the classification. For instance, a variant marked as a deletion by Manta
indicates an intrachromosomal translocation with a deletion-like break-end pattern.
However, there is no test of depth, b-allele frequency, or intersecting adjacencies to infer the
SV type directly.
Whole-Genome Sequencing Services Guide
35
Manta (Large Indel and Structural Variant
The sequencing reads provided as input to Manta are expected to be from a paired-end
sequencing assay that results in an inwards orientation between the 2 reads of each DNA
fragment. Each read presents a read from the outer edge of the fragment insert inward.
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Document # 15040892 v01
Appendix A Appendix
Appendix
BAM File Conversion
Illumina FastTrack Services Annotation Pipeline
Whole-Genome Sequencing Services Guide
38
40
37
Appendix
BAM File Conversion
A large volume of data represents the sequence and corresponding alignments, which are
provided in BAM format. There are a few methods to convert BAM into different formats,
such as FASTQ files.
Picard Tools FASTQ Extraction
Many pipelines start from FASTQ files. To convert BAM files to FASTQ files using Picard
tools, refer to the following example.
# Convert bam into read1.fastq and read2.fastq
$java -jar /picard-tools-1.110/SamToFastq.jar INPUT=Example.bam
FASTQ=Example_R1.fastq SECOND_END_FASTQ=Example_R2.fastq
VALIDATION_STRINGENCY=SILENT
BAM Size: 79 G
Wall Clock Time: 3 hrs 54 min
Optional arguments:
} RE_REVERSE=true—Reverts the sequence to the native orientation. Otherwise, all
aligned sequence is forward orientation.
} MAX_RECORDS_IN_RAM=5000000—Decides the number of reads held memory and
controls total memory usage.
Picard requires large amounts of memory. Picard reads data sequentially line by line from
the BAM file and stores the reads in memory until both pairs of each read have been read.
Memory is reset only when the reads are printed. Every read that does not have adjacent or
near adjacent pairs requires more memory. Therefore, sort large BAM files when memory is
a limiting factor.
Download Picard Tools at sourceforge.net/projects/picard/files/picard-tools.
SAMtools Sort
SAMtools sort ensures that paired reads are next to each other. You can save a significant
amount of memory by using SAMtools to sort the BAM files by name before running
Picard.
# Sort the bam file by name and output to sorted_by_name.bam
$ samtools/samtools-0.1.19/samtools sort -n [email protected] 4 -m 1G
Example.bam Example_sorted
Bam Size: 79G
Wall Clock Time: 3 hrs 5 min
Optional Parameters:
[email protected] 4 : This option tells samtools to run 4 threads
-m 1G : This option tells samtools to use 1Gb of memory per
thread.
For additional information about SAMtools, see samtools.sourceforge.net/
Reads Extraction Using SAMtools Flags
The BAM/SAM format contains a “bitwise flag” column that contains a hexadecimal,
which defines the nature of that read. SAMtools allows you to easily filter on reads based
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Document # 15040892 v01
To convert the SAMtools flags into a human readable format, input the flag into
picard.sourceforge.net/explain-flags.html or run the following command to output the flags
in the coded string format described in the SAMtools manual.
$samtools view –X Example.bam
The following list includes a few commonly used examples of filtering:
} Extract all reads that are unmapped
# -f 4 = include reads which are unmapped
# command will output all the reads which are not mapped.
$samtools view –h –f 4 Example.bam
} Extract reads with unmapped mates
# -f 8 = include reads whose mates are not mapped
# command will output all reads whose mates are not mapped.
$samtools view –h –f 8 Example.bam
} Extract an unmapped read with a mapped mate
# -f 4 = include reads which are unmapped
# –F 8 = exclude reads whose mate is not mapped
# command outputs reads that are unmapped with the corresponding
mate mapped
$samtools view –h –f 4 -F8 Example.bam
} Extract a mapped read with an unmapped mate
# -f 8 = include reads whose mate is unmapped
# -F 8 = exclude all reads not mapped
# command outputs reads which are mapped with the mate is
unmapped
$samtools view –h –f 8 -F4 Example.bam
} Extract both reads of a pair, which are unmapped
#-f 12 = a combination of flag 4 and flag 8 (4+8) -> include only
if a read is unmapped and the mate is unmapped.
# command outputs read pairs with both pairs unmapped
$samtools view –h –f 12 Example.bam
Whole-Genome Sequencing Services Guide
39
BAM File Conversion
on this flag. There are 12 types of these flags. Using the including (-f) or the excluding (-F)
option with flags from SAMtools, you can filter or extract any kind of read from the
BAM/SAM file.
Appendix
Illumina FastTrack Services Annotation Pipeline
The FastTrack pipeline is an internal pipeline that provides the following annotations.
NOTE
These versions are specific to the time of publication of this document and can change with
later updates. To determine the versions used, see the VCF file headers.
Source
dbSNP
COSMIC
1000 Genomes Project
EVS
ClinVar
phyloP
In
}
}
}
40
Version
144
v73
Phase 3 v5a
V2
Unknown
hg19
Release Date
06/06/2015
06/06/2015
05/27/2013
11/13/2013
09/02/2015
11/10/2009
addition, the following annotations are added:
Consequence predictions on RefSeq and Ensembl transcripts (modeled from VEP)
Annotations in regulatory elements (modeled from VEP)
Gene/transcript identifiers and their relationship between RefSeq, Ensembl, HGNC, and
known synonyms (Gene Index)
Document # 15040892 v01
For technical assistance, contact Illumina Technical Support.
Table 2 Illumina General Contact Information
Website
Email
www.illumina.com
[email protected]
Table 3 Illumina Customer Support Telephone Numbers
Region
Contact Number
Region
North America
1.800.809.4566
Japan
Australia
1.800.775.688
Netherlands
Austria
0800.296575
New Zealand
Belgium
0800.81102
Norway
China
400.635.9898
Singapore
Denmark
80882346
Spain
Finland
0800.918363
Sweden
France
0800.911850
Switzerland
Germany
0800.180.8994
Taiwan
Hong Kong
800960230
United Kingdom
Ireland
1.800.812949
Other countries
Italy
800.874909
Contact Number
0800.111.5011
0800.0223859
0800.451.650
800.16836
1.800.579.2745
900.812168
020790181
0800.563118
00806651752
0800.917.0041
+44.1799.534000
Safety data sheets (SDSs)—Available on the Illumina website at
support.illumina.com/sds.html.
Product documentation—Available for download in PDF from the Illumina website. Go
to support.illumina.com, select a product, then select Documentation & Literature.
Whole-Genome Sequencing Services Guide
41
Technical Assistance
Technical Assistance
Illumina
5200 Illumina Way
San Diego, California 92122 U.S.A.
+1.800.809.ILMN (4566)
+1.858.202.4566 (outside North America)
[email protected]
www.illumina.com
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