BWA Whole Genome Sequencing
v1.0
BaseSpace App Guide
For Research Use Only. Not for use in diagnostic procedures.
Introduction
Workflow Diagram
Set Analysis Parameters
Analysis Methods
Analysis Output
Revision History
Technical Assistance
ILLUMINA PROPRIETARY
15050952 v01
January 2016
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After BaseSpace® generates the FASTQ files containing the base calls and quality scores
of the run, you can use the Burrows-Wheeler Aligner (BWA) Whole Genome
Sequencing v1.0 App to analyze the sequencing data. The app analyzes the data in two
parts; first, it aligns to the reference genome, and then it assembles and performs
variant calls.
Compatible Libraries
See the BaseSpace support page for a list of library types that are compatible with the
BWA Whole Genome Sequencing v1.0 App.
Workflow Requirements
}
}
}
}
}
}
}
}
}
}
This app does not support mate-pair or other non-forward and -reverse styles of
paired-end sequencing.
This app does not support annotation of non-human genomes.
A minimum read length of 21 bp and a maximum read length of 150 bp.
Minimum recommended data set size is enough data to yield 10 × coverage after
alignment of the genome being sequenced. See Table 1 for details.
Maximum data set size is fewer than 200 gigabases, which equates to the following:
} Approximately 1 billion reads assuming 2 × 100.
} Approximately 665 million reads assuming 2 × 150.
Completedjobinfo.xml may not print all statistics.
Sample name length has a maximum of 32 characters.
GQX can be entered as any value, although the maximum recommended value is 99.
GATK indel realignment on chrM (at very high coverage) displays this warning:
} Reads will be written to bam out of order
} One of the Picard tools, ValidateSamFile, displays this error:
} MAPQ should be 0 for unmapped read and Mate unmapped flag does
not match read unmapped flag of mate.
Isaac App is recommended if PhiX is being sequenced at a coverage > 5,000,000 ×.
For recommended minimum number of reads for 10 × coverage for different species, see
Table 1. The number of reads listed yields 10 × coverage with an additional 5% to
account for unaligned reads.
Table 1 Recommended Minimums for 10 × Coverage
Genome
Size
Data
Size
Reads for 2 × 100
(million)
Reads for 2 × 150
(million)
Arabidopsis thaliana
63.4 Mb
666 Mb
3.33
2.22
Bos taurus
2.65 Gb
28 Gb
140.00
93.33
Escherichia coli K-12
DH10B
4.7 Mb
50 Mb
0.25
0.17
Escherichia coli K-12
MG1655
4.6 Mb
49 Mb
0.25
0.16
Drosophila melanogaster
139.5 Mb
1.5 Gb
7.33
4.88
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
3
Introduction
Introduction
Genome
Size
Data
Size
Reads for 2 × 100
(million)
Reads for 2 × 150
(million)
Human
3.3 Gb
35 Gb
175.00
116.67
Mus musculus
2.6 Gb
28 Gb
140.00
93.33
PhiX (Illumina)
5386 b
57 Kb
282.77
188.51
Rattus norvegicus
2.9 Gb
31 Gb
155.00
103.33
Rhodobacter sphaeroides
2.4.1
4.6 Mb
49 Mb
0.25
0.16
Saccharomyces cerevisiae
12.2 Mb
129 Mb
0.65
0.43
Staphylococcus aureus
NCTC 8325
12.8 Mb
135 Mb
0.68
0.45
Versions
These components are used in the BWA WGS App.
Software
Version
BWA
0.6.1-r104-tpx-isis
GATK
1.6-22-g3ec78bd
CNV Variant Caller (CNVseg)
2.2.4
Structural Variant Caller (Grouper)
1.4.2
SAMtools
0.1.18
Tabix
0.2.5 (r1005)
Reference Genomes
These genomes are available for alignment:
} Human, UCSC hg19
The human reference genome is PAR-Masked, which means that the Y chromosome
sequence has the Pseudo Autosomal Regions (PAR) masked (set to N) to avoid
mismapping of reads in the duplicate regions of sex chromosomes.
} Arabidopsis thaliana (NCBI build9.1)
} Bos taurus (Ensembl UMD3.1)
} Escherichia coli K-12 DH10B (NCBI 2008-03-17)
} Escherichia coli K-12 MG1655 (NCBI 2001-10-15 )
} Drosophila melanogaster (UCSC dm3)
} Mus musculus (UCSC MM9)
} Phi X (Illumina)
} Rattus norvegicus (UCSC RN5)
} Rhodobacter sphaeroides 2.4.1 (NCBI 2005-10-07)
} Saccharomyces cerevisiae (UCSC sacCer2)
} Staphylococcus aureus NCTC 8325 (NCBI 2006-02-13)
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Workflow Diagram
Workflow Diagram
Figure 1 BWA WGS App Workflow
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
5
Set Analysis Parameters
1
Navigate to BaseSpace, and then click the Apps tab.
2
Click BWA Whole Genome Sequencing v1.0.
3
From the drop-down list, select version 1.0.0, and then click Launch to open the app.
4
In the Analysis Name field, enter the analysis name.
By default, the analysis name includes the app name, followed by the date and time
that the analysis session starts.
5
From the Save Results To field, select the project that stores the app results.
6
From the Sample(s) field, browse to the sample you want to analyze, and select the
checkbox. You can select multiple samples.
7
From the Reference Genome field, select the reference genome you want to align.
8
From the Enable SV/CNV calling field, select the checkbox to perform Isaac Variant
Caller calls structural variation (SV) and copy number variation (CNV) calling.
This option applies to paired-end data.
For more information, see Large Indel and Structural Variant Calls on page 7 and CNV
Variant Caller on page 8).
9
From the Annotation field, select a preferred gene and transcript annotation reference
database.
10 Optional, click the Advance drop-down list for additional parameter fields.
a
b
c
From the Min GQX for Variants field, enter the quality GQX for variants. GQX is
the minimum of the GQ (genotype quality) and QUAL (low quality filter), which
makes it a conservative filter. The default value is 30. The maximum
recommended is 99.
From the Max Strand Bias for Variants field, enter the maximum allowed strand
bias for variant calling. This option filters for reads when the differences in allele
frequencies for forward- and reverse-strand reads are too high. The default value
is 10. Variants with a high strand bias are flagged as filtered. Strand bias is
higher when the evidence for a variant is confined to reads from just one strand.
From the FlagPCRDuplicates field, select the checkbox to have PCR duplicates
flagged in the BAM files and not be used for variant calling. PCR duplicates are
defined as paired-end reads generated from two clusters that have the exact
same alignment positions for each read. Optical duplicates are already filtered
out during RTA processing.
11 Click Continue.
The BWA Whole Genome Sequencing v1.0 App begins analysis of the sample.
When analysis is complete, the status of the app session is updated automatically
and an email is sent to notify you.
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Analysis Methods
Analysis Methods
The BWA Whole Genome Sequencing v1.0 App uses these methods to analyze the
sequencing data.
BWA
The BWA Whole Genome Sequencing workflow uses the Burrows-Wheeler Aligner
(BWA, which adjusts parameters based on read lengths and error rates, and then
estimates the insert size distribution.
For more information, see github.com/lh3/bwa.
After BWA alignment, GATK performs variant calling.
GATK
The Genome Analysis Toolkit (GATK) is the standard variant caller after BWA
alignment.
Developed by the Broad Institute, the Genome Analysis Toolkit (GATK) first calls raw
variants for each sample read. Then GATK analyzes the variants against known
variants, and applies a calibration procedure to compute a false discovery rate for each
variant. Variants are flagged as homozygous (1/1) or heterozygous (0/1) in the VCF file
sample column.
The GATK best practices were guidelines for the app; they are described here:
www.broadinstitute.org/gatk/guide/topic?name=best-practices.
For more information about GATK, see www.broadinstitute.org/gatk.
DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del
Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY,
Cibulskis K, Gabriel SB, Altshuler D, Daly MJ (2011) A framework for variation discovery and
genotyping using next-generation DNA sequencing data. Nat Genet. 43(5): 491-8.
Large Indel and Structural Variant Calls
The large indel and structural variant caller uses the series of modules described here,
and then generates output files in VCF 4.1 format.
Before ReadBroker
}
}
}
StatsGenerator—Computes summary statistics on insert sizes, read orientation, and
alignment scores for each input BAM file.
AnomalousReadFinder—Grouper processes chromosomes in chunks. This method
enables parallel execution and, therefore, faster performance. AnomalousReadFinder
examines all alignments in a block and classifies reads and read pairs as follows:
} Classifies reads as either shadow (unaligned) or semialigned partial or clipped
alignment).
} Classifies read pairs as either InsertionPair, DeletionPair, InversionPair,
TandemDuplicationPair, or ChimericPair, according to which type of structural
variant an anomalously mapped read pair is associated.
ClusterFinder—Clusters reads based on their type and the position of their
alignment. Only reads of the same type are clustered together at this stage, except
shadow and semialigned reads, which can be clustered together.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
7
}
ClusterMerger—Associates clusters of various anomalous read types with
shadow/semi-aligned read clusters, which breakpoints can cause. A breakpoint is a
pair of bases that are adjacent in the sample genome but not in the reference. Two
clusters are merged if they share the read or if they agree on the position and length
of the structural variant. This information is inferred from read alignment orientation
and distance.
ReadBroker
}
Interchromosomal translocations yield chimeric read pairs where 1 read aligns to
one chromosome and its partner aligns to another. Because Grouper examines each
chromosome individually, the ReadBroker step is performed to join the information
from chimeric read pairs across chromosomes.
After ReadBroker
}
}
}
}
}
}
}
SmallAssembler—Assembles reads in clusters into contigs using a de Bruijn method
and iteratively assembles reads into contigs until all reads in the cluster are
assembled. It also produces a file containing the reads that were used to assemble
the contig, with a realignment to the contig sequence.
SpanContigs—Uses the presence of nearby anomalous read pairs to determine
whether to extend the search range used by the subsequent AlignContig step from its
default.
AlignContig—Computes a dynamic programming alignment of a contig to a region
of the reference genome; merges full or partial duplicate calls of the same event into
a single call.
VariantFilter—Removes all structural variants that overlap with gaps identified in
UCSC gaps. The UCSC gaps file defines regions of the genome that have not been
sequenced.
DeletionGenotyper—Assigns a genotype to all deletions.
SomaticGenotyper—Assigns a quality score (Q-score) to all structural variants.
Higher Q-scores indicate a higher probability that this structural variant is somatic.
DeletionGenotyper—Assigns a genotype to all deletions.
CNV Variant Caller
The CNV variant caller is designed to identify copy number variants (CNVs) in diploid
genomes using Hidden Markov Models (HMM) or unbalanced Haar wavelets. The
method adopts a count-based approach for CNV calling, which comprises the following
steps:
1
Pre-processing step, during which read depth is computed at each position and
then filtered based on CpG islands, assembly gaps, telomeric/centromeric
regions. Either alignability tracks or coverage tracks obtained from a pool of
reference sample are used to normalize the data. Counts or count ratios are
produced as an output.
2
Segmentation of read counts/ratios using fixed or variable bin size and a copy
number assignment.
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A single sample or a pool of reference samples is used for normalization, by deriving a
ratio between a test and the reference. Window size is fixed (by default to 100 bp). The
HMM model with Gaussian emission distribution is used for segmentation. A bin
exclusion criterion (less than 10% of build coverage in both samples) is applied.
The reference for CNV normalization is an alignability measure that is meant to gauge
the probability of a position aligning to a single unique region of the genome. In detail,
the notion of alignability for reads of length k is as follows: given a map M that, for a
fixed read length k and any position P in a genome G, stores at M(P) the number of
occurrences in G of the k-mer that starts at P for a given position P in G, define the
overlap set of P as the k-mers that overlap P. The alignability of P is the proportion of
this overlap set that is unique.
Variant Scoring
After copy number assignment, each CNV call is assigned a quality score based on a
two-sample t-test. Each counts/ratio in a 1 kb window on each size of a breakpoint (or
half the length of a variant call, whichever is smaller) is compared using t-test. This test
is based on the null hypothesis that there is no difference in coverage on each size of the
breakpoint. Obtained p-values are then reported as Q-scores on a Phred scale as -10
log10.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
9
Analysis Methods
Normalization
Analysis Output
To view the results, click the Projects tab, then the project name, and then the analysis.
Figure 2 Output Navigation Bar
After analysis is complete, access the output through the left navigation bar.
} Analysis Info—Information about the analysis session, including log files.
} Inputs—Overview of input settings.
} Output Files—Output files for the sample.
} Sample Analysis Reports—Analysis reports for each sample.
Analysis Info
The Analysis Info page displays the analysis settings and execution details.
Row Heading
Definition
Name
Name of the analysis session.
Application
App that generated this analysis.
Date Started
Date and time the analysis session started.
Date Completed
Date and time the analysis session completed.
Duration
Duration of the analysis.
Session Type
Number of nodes used.
Status
Status of the analysis session. The status shows either Running
or Complete.
Log Files
Click the Log Files link to access the app log files. You can locate some log files in
Output Files.
10
File Name
Description
CompletedJobInfo.xml
Contains information about the completed analysis session.
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Description
Logging.zip
Contains all detailed log files for each step of the workflow.
WorkflowError.txt
Workflow standard error output (contains error messages
created when running the workflow).
WorkflowLog.txt
Workflow standard output (contains details about workflow
steps, command line calls with parameters, timing, and
progress).
monoOut.txt
Wrapper mono call standard output that contains the
command calling the workflow and anything that
WorkflowLog.txt does not catch. Available when the mono
component does not work as expected.
Analysis Output
File Name
NOTE
For explanation about mono, see www.mono-project.com.
Output Files
The Output Files page provides access to the output files for each sample analysis.
} BAM Files
} VCF Files
} Genome VCF Files
} Resequencing File
} Summary File
BAM File Format
A BAM file (*.bam) is the compressed binary version of a SAM file that is used to
represent aligned sequences up to 128 Mb. SAM and BAM formats are described in
detail at https://samtools.github.io/hts-specs/SAMv1.pdf.
BAM files use the file naming format of SampleName_S#.bam, where # is the sample
number determined by the order that samples are listed for the run.
BAM files contain a header section and an alignments section:
} Header—Contains information about the entire file, such as sample name, sample
length, and alignment method. Alignments in the alignments section are associated
with specific information in the header section.
} Alignments—Contains read name, read sequence, read quality, alignment
information, and custom tags. The read name includes the chromosome, start
coordinate, alignment quality, and the match descriptor string.
The alignments section includes the following information for each or read pair:
} RG: Read group, which indicates the number of reads for a specific sample.
} BC: Barcode tag, which indicates the demultiplexed sample ID associated with the
read.
} SM: Single-end alignment quality.
} AS: Paired-end alignment quality.
} NM: Edit distance tag, which records the Levenshtein distance between the read and
the reference.
} XN: Amplicon name tag, which records the amplicon tile ID associated with the
read.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
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BAM index files (*.bam.bai) provide an index of the corresponding BAM file.
VCF File Format
VCF is a widely used file format developed by the genomics scientific community that
contains information about variants found at specific positions in a reference genome.
VCF files use the file naming format SampleName_S#.vcf, where # is the sample number
determined by the order that samples are listed for the run.
VCF File Header—Includes the VCF file format version and the variant caller version.
The header lists the annotations used in the remainder of the file. If MARS is listed, the
Illumina internal annotation algorithm annotated the VCF file. The VCF header includes
the reference genome file and BAM file. The last line in the header contains the column
headings for the data lines.
VCF File Data Lines—Each data line contains information about a single variant.
VCF File Headings
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Heading
Description
CHROM
The chromosome of the reference genome. Chromosomes appear in
the same order as the reference FASTA file.
POS
The single-base position of the variant in the reference chromosome.
For SNPs, this position is the reference base with the variant; for indels
or deletions, this position is the reference base immediately before the
variant.
ID
The rs number for the SNP obtained from dbSNP.txt, if applicable.
If there are multiple rs numbers at this location, the list is semicolon
delimited. If no dbSNP entry exists at this position, a missing value
marker ('.') is used.
REF
The reference genotype. For example, a deletion of a single T is
represented as reference TT and alternate T. An A to T single nucleotide
variant is represented as reference A and alternate T.
ALT
The alleles that differ from the reference read.
For example, an insertion of a single T is represented as reference A and
alternate AT. An A to T single nucleotide variant is represented as
reference A and alternate T.
QUAL
A Phred-scaled quality score assigned by the variant caller.
Higher scores indicate higher confidence in the variant and lower
probability of errors. For a quality score of Q, the estimated probability
of an error is 10-(Q/10). For example, the set of Q30 calls has a 0.1% error
rate. Many variant callers assign quality scores based on their statistical
models, which are high in relation to the error rate observed.
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Analysis Output
VCF File Annotations
Heading
Description
FILTER
If all filters are passed, PASS is written in the filter column.
• LowDP—Applied to sites with depth of coverage below a cutoff.
• LowGQ—The genotyping quality (GQ) is below a cutoff.
• LowQual—The variant quality (QUAL) is below a cutoff.
• LowVariantFreq—The variant frequency is less than the given
threshold.
• R8—For an indel, the number of adjacent repeats (1-base or 2-base)
in the reference is greater than 8.
• SB—The strand bias is more than the given threshold. Used with the
Somatic Variant Caller and GATK.
INFO
Possible entries in the INFO column include:
• AC—Allele count in genotypes for each ALT allele, in the same order
as listed.
• AF—Allele Frequency for each ALT allele, in the same order as listed.
• AN—The total number of alleles in called genotypes.
• CD—A flag indicating that the SNP occurs within the coding region
of at least 1 RefGene entry.
• DP—The depth (number of base calls aligned to a position and used
in variant calling).
• Exon—A comma-separated list of exon regions read from RefGene.
• FC—Functional Consequence.
• GI—A comma-separated list of gene IDs read from RefGene.
• QD—Variant Confidence/Quality by Depth.
• TI—A comma-separated list of transcript IDs read from RefGene.
FORMAT
The format column lists fields separated by colons. For example,
GT:GQ. The list of fields provided depends on the variant caller used.
Available fields include:
• AD—Entry of the form X,Y, where X is the number of reference calls,
and Y is the number of alternate calls.
• DP—Approximate read depth; reads with MQ=255 or with bad mates
are filtered.
• GQ—Genotype quality.
• GQX—Genotype quality. GQX is the minimum of the GQ value and
the QUAL column. In general, these values are similar; taking the
minimum makes GQX the more conservative measure of genotype
quality.
• GT—Genotype. 0 corresponds to the reference base, 1 corresponds
to the first entry in the ALT column, and so on. The forward slash (/)
indicates that no phasing information is available.
• NL—Noise level; an estimate of base calling noise at this position.
• PL—Normalized, Phred-scaled likelihoods for genotypes.
• SB—Strand bias at this position. Larger negative values indicate less
bias; values near 0 indicate more bias. Used with the Somatic Variant
Caller and GATK.
• VF—Variant frequency; the percentage of reads supporting the
alternate allele.
SAMPLE
The sample column gives the values specified in the FORMAT column.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
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Genome VCF Files
Genome VCF (gVCF) files are VCF v4.1 files that follow a set of conventions for
representing all sites within the genome in a reasonably compact format. The gVCF files
include all sites within the region of interest in a single file for each sample.
The gVCF file shows no-calls at positions with low coverage, or where a low-frequency
variant (< 3%) occurs often enough (> 1%) that the position cannot be called to the
reference. A genotype (GT) tag of ./. indicates a no-call.
For more information, see sites.google.com/site/gvcftools/home/about-gvcf.
Summary File
The BWA WGS App produces an overview of statistics for each sample and the
aggregate results in a comma-separated values (CSV) format. The *resquencing_
summary.csv file contains the same data as the Sample Summary Report but is
formatted for easier analysis. These files are located in the results folder for each sample
and the aggregate results.
Statistic
14
Definition
Sample ID
IDs of samples reported on in the file.
Run Folder
Run folders for samples reported on in the file.
Fragment length median
Median length of the sequenced fragment. The fragment
length is calculated based on the locations at which a read
pair aligns to the reference. The read mapping information
is parsed from the BAM files.
Fragment length min
Minimum length of the sequenced fragment.
Fragment length max
Maximum length of the sequenced fragment.
Fragment length SD
Standard deviation of the sequenced fragment length.
Number of Reads
Total number of reads passing filter for this sample.
Percent Aligned (per
read)
Percentage of reads passing filter that aligned.
Percent Q30 (per read)
The percentage of bases with a quality score of 30 or
higher.
MismatchRate (per read)
The average percentage of mismatches across both reads 1
and 2 over all cycles.
SNVs All
Total number of Single Nucleotide Variants present in the
data set passing the quality filters.
SNVs Passing Filters
SNVs passing variants filter.
SNVs (Percent found in
dbSNP)
100*(Number of SNVs in dbSNP/Number of SNVs).
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Analysis Output
Statistic
Definition
SNV Ts/Tv ratio
Transition rate of SNVs that pass the quality filters divided
by transversion rate of SNVs that pass the quality filters.
Transitions are interchanges of purines (A, G) or of
pyrimidines (C, T). Transversions are interchanges of
purine and pyrimidine bases (for example, A to T).
SNV Het/Hom ratio
Number of heterozygous SNVs/Number of homozygous
SNVs.
Indels
Total number of indels present in the data set passing the
quality filters.
Insertions Passing
Variants
Insertions passing variant filters.
Deletions Passing
Variants
Deletions passing variant filters.
Indels (Percent found in
dbSNP)
100*(Number of Indels in dbSNP/Number of Indels).
Insertions (Percent found
in dbSNP)
100*(Number of insertions in dbSNP/ Number of
insertions)
Deletions(Percent found
in dbSNP)
100*(Number of deletions in dbSNP/ Number of deletions)
Indel Het/Hom ratio
Number of heterozygous indels/Number of homozygous
indels.
Insertion Het/Hom ratio
Ratio of the number of heterozygous to homozygous
insertions.
Deletion Het/Hom ratio
Ratio of the number of heterozygous to homozygous
deletions.
SmallVariantStatisticsFlag
Flags whether SmallVariantStatistics was run (1 means that
it was run)
SVStatisticsFlag
Flags whether SVStatistics was run (1 means that it was
run)
CNVStatisticsFlag
Flags whether CNVStatistics was run (1 means that it was
run)
Sample Analysis Reports
The BWA WGS App provides an overview of statistics per sample in the Analysis
Reports sample pages. To download the statistics, click PDF Summary Report.
} Alignment Summary
} Small Variants
} Structural Variants
} Coverage Histogram Summary
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
15
Alignment Summary
Table 2 Alignment Summary
Statistic
Definition
Number of Reads
Total number of reads passing filter for this sample.
Coverage
Total number of aligned bases divided by the genome size.
Percent Duplicate
Paired Reads
Percentage of paired reads that have duplicates.
Fragment Length
Median
Median length of the sequenced fragment. The fragment
length is calculated based on the locations at which a read pair
aligns to the reference. The read mapping information is
parsed from the BAM files.
Fragment Length
Standard Deviation
Standard deviation of the sequenced fragment length.
Table 3 Read Level Statistics
Statistic
Definition
Percent Aligned
The percentage of reads passing filter that aligned to the
reference genome.
Percent Q30
The percentage of bases with a quality score of 30 or higher.
Mismatch Rate
The average percentage of mismatches across both reads 1
and 2 over all cycles.
Small Variants Summary
Table 4 Small Variants Summary
16
Statistic
Definition
Total Passing
The total number of variants present in the data set that
passed the variant quality filters.
Percent Found in
dbSNP
100*(Number of variants in dbSNP/Number of variants).
Het/Hom Ratio
Number of heterozygous variants/Number of homozygous
variants.
Ts/Tv Ratio
Transition rate of SNVs that pass the quality filters divided by
transversion rate of SNVs that pass the quality filters.
Transitions are interchanges of purines (A, G) or of
pyrimidines (C, T). Transversions are interchanges between
purine and pyrimidine bases (for example, A to T).
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Analysis Output
Table 5 Variants by Sequence Context
Statistic
Definition
Number in Genes
The number of variants that fall into a gene.
Number in Exons
The number of variants that fall into an exon.
Number in Coding
Regions
The number of variants that fall into a coding region.
Number in UTR
Regions
The number of variants that fall into an untranslated region
(UTR).
Number in Mature
microRNA
The number of variants that fall into a mature microRNA.
Number in Splice Site
Regions
The number of variants that fall into a splice site region.
Table 6 Variants by Consequence
Statistic
Definition
Frameshifts
The number of variants that cause a frameshift.
Non-synonymous
The number of variants that cause an amino acid change in a
coding region.
Synonymous
The number of variants that are within a coding region, but
do not cause an amino acid change.
Stop Gained
The number of variants that cause an additional stop codon.
Stop Lost
The number of variants that cause the loss of a stop codon.
Structural Variants Summary
This table breaks structural variant output into the classes of variants called, and reports
the total number and their overlap with annotated genes. All counts are based on PASS
filter variants.
Variant Class
Definition
CNV
A copy-number variation (CNV) is a large category of
structural variation, which includes insertions, deletions and
duplications. CNVs are generally greater than 10 kb. CNVs
below 10 kb are filtered but still present in the VCF file.
Insertion
In an insertion, nucleotides are added between 2 adjacent
nucleotides in the reference sequence. The insertions in the
structural variants category are 51 bp or greater.
Tandem Duplication
In a tandem duplication, a segment of a chromosome is
duplicated front to end, with both segments in the same
orientation. The segments are 51 bp or greater.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
17
Variant Class
Definition
Deletion
In a deletion, contiguous nucleotides are absent compared to
the reference sequence. The deletions in the structural variants
category are 51 bp or greater.
Inversion
An inversion is a chromosome rearrangement in which a
segment of a chromosome is reversed end to end. An
inversion occurs when a single chromosome undergoes
breakage and rearrangement within itself. The segments are
51 bp or greater.
Coverage Histogram
The coverage histogram shows the number of reference bases plotted against the depth of
coverage (read depth). The features include the following:
} The drop-down menu lets you look at the overall picture, or highlight a particular
chromosome.
} The Fix Y Scale checkbox lets you keep the Y Scale the same when comparing
multiple chromosomes.
} The Export TSV link lets you to export the coverage data in a tab-separated txt. file.
18
15050952 v01
Revision History
Revision History
Document
Document #
15050952 v01
Date
January
2016
Description of Change
Reorganized topics, updated writing style.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
19
Notes
For technical assistance, contact Illumina Technical Support.
Table 7 Illumina General Contact Information
Website
Email
www.illumina.com
[email protected]
Table 8 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.
BWA Whole Genome Sequencing v1.0 BaseSpace App Guide
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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