This is the Final Version of VarDict. No longer maintained.

VarDictJava

Introduction

VarDictJava is a variant discovery program written in Java and Perl. It is a Java port of VarDict variant caller.

The original Perl VarDict is a sensitive variant caller for both single and paired sample variant calling from BAM files. VarDict implements several novel features such as amplicon bias aware variant calling from targeted sequencing experiments, rescue of long indels by realigning bwa soft clipped reads and better scalability than many other Java based variant callers. The Java port is around 10x faster than the original Perl implementation.

Please cite VarDict:

Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, McEwen R, Johnson J, Dougherty B, Barrett JC, and Dry JR. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 2016, pii: gkw227.

The link to is article can be accessed through: https://academic.oup.com/nar/article/44/11/e108/2468301?searchresult=1

Original coded by Zhongwu Lai 2014.

VarDictJava can run in single sample (see Single sample mode section), paired sample (see Paired variant calling section), or amplicon bias aware modes. As input, VarDictJava takes reference genomes in FASTA format, aligned reads in BAM format, and target regions in BED format.

Requirements

1. JDK 1.8 or later
2. R language (uses /usr/bin/env R)
3. Perl (uses /usr/bin/env perl)
4. Internet connection to download dependencies using gradle.

To see the help page for the program, run

 <path_to_vardict_folder>/build/install/VarDict/bin/VarDict -H.

Getting started

Getting source code

The VarDictJava source code is located at https://github.com/AstraZeneca-NGS/VarDictJava.

To load the project, execute the following command:

git clone --recursive https://github.com/AstraZeneca-NGS/VarDictJava.git

Note that the original VarDict project is placed in this repository as a submodule and its contents can be found in the sub-directory VarDict in VarDictJava working folder. So when you use teststrandbias.R and var2vcf_valid.pl. (see details and examples below), you have to add prefix VarDict: VarDict/teststrandbias.R and VarDict/var2vcf_valid.pl.

Compiling

The project uses Gradle and already includes a gradlew script.

To build the project, in the root folder of the project, run the following command:

./gradlew clean installDist

Clean will remove all old files from build folder.

To generate Javadoc, in the build/docs/javadoc folder, run the following command. If you want to save content of build folder as it is (for example after building the project), run it without clean option:

./gradlew clean javadoc

To generate release version in the build/distributions folder as tar or zip archive, run the following command:

./gradlew distZip

or

./gradlew distTar

Distribution Package Structure

When the build command completes successfully, the build/install/VarDict folder contains the distribution package.

The distribution package has the following structure:

• bin/ - contains the launch scripts
• lib/ - has the jar file that contains the compiled project code and the jar files of the third-party libraries that the project uses.

You can move the distribution package (the content of the build/install/VarDict folder) to any convenient location.

Generated zip and tar releases will also contain scripts from VarDict Perl repository in bin/ directory (teststrandbias.R, testsomatic.R, var2vcf_valid.pl, var2vcf_paired.pl).

You can add VarDictJava on PATH by adding this line to .bashrc:

export PATH=/path/to/VarDict/bin:$PATH

After that you can run VarDict by Vardict command instead of full path to <path_to_vardict_folder>/build/install/VarDict/bin/VarDict.

Third-Party Libraries

Currently, the project uses the following third-party libraries:

• JRegex (http://jregex.sourceforge.net, BSD license) is a regular expressions library that is used instead of the standard Java library because its performance is much higher than that of the standard library.
• Commons CLI (http://commons.apache.org/proper/commons-cli, Apache License) – a library for parsing the command line.
• HTSJDK (http://samtools.github.io/htsjdk/) is an implementation of a unified Java library for accessing common file formats, such as SAM and VCF.
• Mockito and TestNG are the testing frameworks (not included in distribution, used only in tests).

Single sample mode

To run VarDictJava in single sample mode, use a BAM file specified without the | symbol and perform Steps 3 and 4 (see the Program workflow section) using teststrandbias.R and var2vcf_valid.pl. The following is an example command to run in single sample mode with BED file.
You have to set options -c, -S, -E, -g using number of columns in your BED file for chromosome, start, end and gene of region respectively:

AF_THR="0.01" # minimum allele frequency
<path_to_vardict_folder>/build/install/VarDict/bin/VarDict -G /path/to/hg19.fa -f $AF_THR -N sample_name -b /path/to/my.bam -c 1 -S 2 -E 3 -g 4 /path/to/my.bed | VarDict/teststrandbias.R | VarDict/var2vcf_valid.pl -N sample_name -E -f $AF_THR > vars.vcf

VarDictJava can also be invoked without a BED file if the region is specified in the command line with -R option. The following is an example command to run VarDictJava for a region (chromosome 7, position from 55270300 to 55270348, EGFR gene) with -R option:

<path_to_vardict_folder>/build/install/VarDict/bin/VarDict  -G /path/to/hg19.fa -f 0.001 -N sample_name -b /path/to/sample.bam -R  chr7:55270300-55270348:EGFR | VarDict/teststrandbias.R | VarDict/var2vcf_valid.pl -N sample_name -E -f 0.001 > vars.vcf

In single sample mode, output columns contain a description and statistical info for variants in the single sample. See section Output Columns for list of columns in the output.

Paired variant calling

To run paired variant calling, use BAM files specified as BAM1|BAM2 and perform Steps 3 and 4 (see the Program Workflow section) using testsomatic.R and var2vcf_paired.pl.

In this mode, the number of statistics columns in the output is doubled: one set of columns is for the first sample, the other - for second sample.

The following is an example command to run in paired mode.
You have to set options -c, -S, -E, -g using number of columns in your bed file for chromosome, start, end and gene of region respectively:

AF_THR="0.01" # minimum allele frequency
<path_to_vardict_folder>/build/install/VarDict/bin/VarDict -G /path/to/hg19.fa -f $AF_THR -N tumor_sample_name -b "/path/to/tumor.bam|/path/to/normal.bam" -c 1 -S 2 -E 3 -g 4 /path/to/my.bed | VarDict/testsomatic.R | VarDict/var2vcf_paired.pl -N "tumor_sample_name|normal_sample_name" -f $AF_THR

Amplicon based calling

This mode is active if the BED file uses 8-column format and the -R option is not specified.

In this mode, only the first list of BAM files is used even if the files are specified as BAM1|BAM2 - like for paired variant calling.

For each segment, the BED file specifies the list of positions as start and end positions (columns 7 and 8 of the BED file). The Amplicon based calling mode outputs a record for every position between start and end that has any variant other than the reference one (all positions with the -p option). For any of these positions, VarDict in amplicon based calling mode outputs the following:

• Same columns as in the single sample mode for the most frequent variant
• Good variants for this position with the prefixes GOOD1, GOOD2, etc.
• Bad variants for this position with the prefixes BAD1, BAD2, etc.

For this running mode, the -a option (default: 10:0.95) specifies the criteria of discarding reads that are too far away from segments. A read is skipped if its start and end are more than 10 positions away from the segment ends and the overlap fraction between the read and the segment is less than 0.95.

Running Tests

Integration testing

The list of integration test cases is stored in files in testdata/intergationtestcases directory. To run all integration tests, the command is:

./gradlew test --tests com.astrazeneca.vardict.integrationtests.IntegrationTest 

The results of the tests can be viewed in the build/reports/tests/index.html file.

User extension of testcases

Each file in testdata/intergationtestcases directory represents a test case with input data and expected output

1. Create a txt file in testdata/intergationtestcases folder.

The file contains testcase input (of format described in Test cases file format) in the first line and expected output in the remaining file part.

2. Extend or create thin-FASTA in testdata/fastas folder.

3. Run tests.

Test cases file format

Each input file represents one test case input description. In the input file the first line consists of the following fields separated by , symbol:

Required fields:

• test case name (Amplicon/Somatic/Simple mode)
• reference name
• bam file name
• chromosome name
• start of region
• end of region

Optional fields:

• start of region with amplicon case
• end of region with amplicon case

Parameters field:

• the last field can be any other command line parameters string

Example of first line of input file:

Amplicon,hg19.fa,Colo829-18_S3-sort.bam,chr1,933866,934466,933866,934466,-a 10:0.95 -D
Somatic,hg19.fa,Colo829-18_S3-sort.bam|Colo829-19_S4-sort.bam,chr1,755917,756517
Simple,hg19.fa,Colo829-18_S3-sort.bam,chr1,9922,10122,-p

Test coverage Report

To build test coverage report run the following command:

./gradlew test jacocoTestReport 

Then HTML report could be found in build/reports/jacoco/test/html/index.html

Thin-FASTA Format

Thin fasta is needed to store only needed for tests regions of real references to decrease disk usage. Each thin-FASTA file is .csv file, each line of which represent part of reference data with information of:

• chromosome name
• start position of contig
• end position of contig
• and nucleotide sequence that corresponds to region

thin-FASTA example:

chr1,1,15,ATGCCCCCCCCCAAA
chr1,200,205,GCCGA
chr2,10,12,AC

Note: VarDict expands given regions by 1200bp to left and right (plus given value by -x option).

Program Workflow

The main workflow

The VarDictJava program follows the workflow:

1. Get regions of interest from a BED file or the command line.

2. For each segment:

   1. Find all variants for this segment in mapped reads:
      1. Optionally skip duplicated reads, low mapping-quality reads, and reads having a large number of mismatches.
      2. Skip unmapped reads.
      3. Skip a read if it does not overlap with the segment.
      4. Preprocess the CIGAR string for each read (section CIGAR Preprocessing).
      5. For each position, create a variant. If a variant is already present, adjust its count using the adjCnt function.
      6. Combine SNVs into MNV or SNV with indel to complex if variants are located closer than -X option (default <=2 bases) and have good base qualities.
   2. Find structural variants (optionally can be disabled by option -U).
   3. Realign some of the variants using realignment of insertions, deletions, large insertions, and large deletions using unaligned parts of reads (soft-clipped ends). This step is optional and can be disabled using the -k 0 switch.
   4. Apply variant filtering rules (hard filters) defined in Variant filtering.
   5. Assign a type to each variant.
   6. Output variants in an intermediate internal format (tabular). Columns of the table are described in the Output Columns section.

   Note: To perform Steps 1 and 2, use Java VarDict.

3. Perform a statistical test for strand bias using an R script.
   Note: Use R scripts teststrandbias.R or testsomatic.R for this step.

4. Transform the intermediate tabular format to VCF. Output the variants with filtering and statistical data.
   Note: Use the Perl scripts var2vcf_valid.pl or var2vcf_paired.pl for this step. Be aware that var2vcf_valid.pl or var2vcf_paired.pl by default will output the variant with the highest AF on position: if few variants start at one position, only the highest will be added in VCF. To output all variants use -A option with these perl scripts.

CIGAR Preprocessing (Initial Realignment)

Read alignment is specified in a BAM file as a CIGAR string. VarDict modifies this string (and alignment) in the following special cases:

• Soft clipping next to insertion/deletion is replaced with longer soft-clipping. The same takes place if insertion/deletion is separated from soft clipping by no more than 10 matched bases.
• Short matched sequence and insertion/deletion at the beginning/end are replaced by soft-clipping.
• Two close deletions and insertions are combined into one deletion and one insertion
• Three close deletions are combined in one
• Three close insertions/deletions are combined in one deletion or in insertion+deletion
• Two close deletions are combined into one
• Two close insertions/deletions are combined into one
• Mis-clipping at the start/end are changed to matched sequences

Variants

Simple variants (SNV, simple insertions, and deletions) are constructed in the following way:

• Single-nucleotide variation (SNV). VarDict inserts an SNV into the variants structure for every matched or mismatched base in the reads. If an SNV is already present in variants, VarDict adjusts its counts and statistics.
• Simple insertion variant. If read alignment shows an insertion at the position, VarDict inserts +BASES string into the variants structure. If the variant is already present, VarDict adjusts its count and statistics.
• Simple Deletion variant. If read alignment shows a deletion at the position, VarDict inserts -NUMBER into the variants structure. If the variant is already present, VarDict adjusts its count and statistics.
• Complex variants: VarDict also handles complex variants (for example, an insertion that is close to SNV or to deletion) using specialized ad-hoc methods.

Structural Variants are looked for after simple variants. VarDict supported DUP, INV and DEL structural variants.

Variant Description String

The description string encodes a variant for VarDict internal use.

The following table describes Variant description string encoding:

String	Description
[ATGC]	for SNPs
+[ATGC]+	for insertions
-[0-9]+	for deletions
...#[ATGC]+	for insertion/deletion variants followed by a short matched sequence
...^[ATGC]+	something followed by an insertion
...^[0-9]+	something followed by a deletion
...&[ATGC]+	for insertion/deletion variants followed by a matched sequence

Variant Filtering

A variant appears in the output if it satisfies the following criteria (in this order). If variant doesn't fit criteria on the step, it will be filtered out and the next steps won't be checked (except for the step 8, read the explanation below):

1. Frequency of the variant exceeds the threshold set by the -f option (default = 1%).
2. The minimum number of high-quality reads supporting variant is larger than the threshold set by the -r option (default = 2).
3. The mean position of the variant in reads is larger than the value set by the -P option (default = 5).
4. The mean base quality (phred score) for the variant is larger than the threshold set by the -q option (default = 22.5).
5. Variant frequency is more than 25% or reference allele does not have much better mapping quality than the variant.
6. Deletion variants are not located in the regions where the reference genome is missing.
7. The ratio of high-quality reads to low-quality reads is larger than the threshold specified by -o option (default=1.5).
8. Variant frequency exceeds 30%. If so, next steps won't be checked and variant considered as "good". Otherwise, other steps will be also checked.
9. Mean mapping quality exceeds the threshold set by the -O option (default: no filtering)
10. In the case of an MSI region, the variant size is less than 12 nucleotides for the non-monomer MSI or 15 for the monomer MSI. Variant frequency is more than 10% for the non-monomer MSI (or set by --nmfreq option) and 25% for the monomer MSI (or set by --mfreq option).
11. Variant has not "2;1" bias or variant frequency more than 20%. If both conditions aren't met, then variant mustn't be SNV and any of variants refallele or varallele lengths must be more than 3 nucleotides.

Bias flag explanation

Bias flag can take values [0-2];[0-2] (i.e. "0;2", "2;1" and separator can be another in paired and single VCF). The first value refers to reads that support the reference allele, and the second to reads that support the variant allele.

0 - small total count of reads (less than 12 for the sum of forward and reverse reads) 1 - strand bias 2 - no strand bias

Variant classification in paired(somatic) analysis

In paired analysis, VarDict will classify each variant into the following types that are propagated into STATUS info tag after var2vcf_paired.pl script.

When both samples have coverage for the variant:

• Germline: detected in germline sample (pass all quality parameters)
• StrongSomatic: detected in tumor sample only
• LikelySomatic: the variant has at least one read support OR allele frequency < 5% (defined by –V option with default 0.05)
• StrongLOH: detected in germline sample only, opposite of StrongSomaitc
• LikelyLOH: detected in germline but either lost in tumor OR 20-80% in germline, but increased to 1-opt_V (95%).
• AFDiff: detected in tumor (pass quality parameters) and present in germline but didn’t pass quality parameters.

When only one sample has coverage for the variant:

• SampleSpecific: detected in tumor sample, but no coverage in germline sample (it’s more technical than biological, as it’s unlikely a tumor sample can gain a piece of sequence in reference that germline sample lacks).
• Deletion: detected in germline sample, but no coverage in tumor sample

These are only rough classification. You need to examine the p-value (after testsomatic.R script) to determine whether or not it's significant.

Program Options

VarDictJava options

• -H|-?
  Print help page
• -h|--header
  Print a header row describing columns
• -i|--splice Output splicing read counts
• -p
  Do pileup regardless of the frequency
• -C
  Indicate the chromosome names are just numbers, such as 1, 2, not chr1, chr2 (deprecated)
• -D|--debug
  Debug mode. Will print some error messages and append full genotype at the end.
• -y|--verbose
  Verbose mode. Will output variant calling process.
• -t|--dedup
  Indicate to remove duplicated reads. Only one pair with identical start positions will be kept
• -3
  Indicate to move indels to 3-prime if alternative alignment can be achieved.
• -K Include Ns in the total depth calculation.
• -F bit
  The hexical to filter reads. Default: 0x504 (filter unmapped reads, 2nd alignments and duplicates). Use -F 0 to turn it off.
• -z 0/1
  Indicate whether the BED file contains zero-based coordinates, the same way as the Genome browser IGV does. -z 1 indicates that coordinates in a BED file start from 0. -z 0 indicates that the coordinates start from 1. Default: 1 for a BED file or amplicon BED file (0-based). Use 0 to turn it off. When using -R option, it is set to 0
• -a|--amplicon int:float
  Indicate it is amplicon based calling. Reads that do not map to the amplicon will be skipped. A read pair is considered to belong to the amplicon if the edges are less than int bp to the amplicon, and overlap fraction is at least float. Default: 10:0.95
• -k 0/1
  Indicate whether to perform local realignment. Default: 1 or yes. Set to 0 to disable it.
• -G Genome fasta
  The reference fasta. Should be indexed (.fai). Defaults to: /ngs/reference_data/genomes/Hsapiens/hg19/seq/hg19.fa
  Also short commands can be used to set path to:
  hg19 - /ngs/reference_data/genomes/Hsapiens/hg19/seq/hg19.fa
  hg38 - /ngs/reference_data/genomes/Hsapiens/hg38/seq/hg38.fa
  mm10 - /ngs/reference_data/genomes/Mmusculus/mm10/seq/mm10.fa
• -R Region
  The region of interest. In the format of chr:start-end. If chr is not start-end but start (end is omitted), then it is a single position. No BED is needed.
• -d delimiter
  The delimiter for splitting region_info, defaults to tab "\t"
• -n regular_expression
  The regular expression to extract sample names from bam filenames. Defaults to: /([^\/\._]+?)_[^\/]*.bam/
• -N string
  The sample name to be used directly. Will overwrite -n option
• -b string
  The indexed BAM file. Multiple BAM files can be specified with the “:” delimiter.
• -c INT
  The column for chromosome
• -S INT
  The column for the region start, e.g. gene start
• -E INT
  The column for the region end, e.g. gene end
• -s INT
  The column for a segment starts in the region, e.g. exon starts
• -e INT
  The column for a segment ends in the region, e.g. exon ends
• -g INT
  The column for a gene name, or segment annotation
• -x INT
  The number of nucleotides to extend for each segment, default: 0
• -f double
  The threshold for allele frequency, default: 0.01 or 1%
• -r minimum reads
  The minimum # of variance reads, default: 2
• -B INT
  The minimum # of reads to determine strand bias, default: 2
• -Q INT
  If set, reads with mapping quality less than INT will be filtered and ignored
• -q double
  The phred score for a base to be considered a good call. Default: 22.5 (for Illumina). For PGM, set it to ~15, as PGM tends to underestimate base quality.
• -m INT
  If set, reads with mismatches more than INT will be filtered and ignored. Gaps are not counted as mismatches. Valid only for bowtie2/TopHat or BWA aln followed by sampe. BWA mem is calculated as NM - Indels. For STAR you have to increase default if nM tag (for "paired" alignment) is presented in reads.Default: 8, or reads with more than 8 mismatches will not be used.
• -T|--trim INT
  Trim bases after [INT] bases in the reads
• -X INT
  Extension of bp to look for mismatches after insersion or deletion. Default to 2 bp, or only calls when they are within 2 bp.
• -P number
  The read position filter. If the mean variants position is less that specified, it is considered false positive. Default: 5
• -Z|--downsample double
  For downsampling fraction, e.g. 0.7 means roughly 70% downsampling. Default: No downsampling. Use with caution. The downsampling will be random and non-reproducible.
• -o Qratio
  The Qratio of (good_quality_reads)/(bad_quality_reads+0.5). The quality is defined by -q option. Default: 1.5
• -O MapQ
  The variant should has at least mean MapQ to be considered a valid variant. Default: no filtering
• -V freq
  The lowest frequency in a normal sample allowed for a putative somatic mutations. Used only in paired mode. Defaults to 0.05
• -I INT
  The indel size. Default: 50bp. Be cautious with -I option, especially in the amplicon mode, as amplicon sequencing is not a way to find large indels. Increasing the search size might be slow and false positives may appear in low complexity regions. Increasing it to 200-300 bp is only recommend for hybrid capture sequencing.
• -M INT
  The minimum matches for a read to be considered. If, after soft-clipping, the matched bp is less than INT, then the read is discarded. It's meant for PCR based targeted sequencing where there's no insert and the matching is only the primers. Default: 0, or no filtering
• -th [threads]
  If this parameter is missing, then the mode is one-thread. If you add the -th parameter, the number of threads equals to the number of processor cores. The parameter -th threads sets the number of threads explicitly.
• -VS STRICT | LENIENT | SILENT
  How strict to be when reading a SAM or BAM. STRICT - throw an exception if something looks wrong. LENIENT - Emit warnings but keep going if possible. SILENT - Like LENIENT, only don't emit warning messages. Default: LENIENT
• -u
  Indicate unique mode, which when mate pairs overlap, the overlapping part will be counted only once using forward read only. Default: unique mode disabled, all reads are counted.
• -UN
  Indicate unique mode, which when mate pairs overlap, the overlapping part will be counted only once using first read only. Default: unique mode disabled, all reads are counted.
• --chimeric
  Indicate to turn off chimeric reads filtering. Chimeric reads are artifacts from library construction, where a read can be split into two segments, each will be aligned within 1-2 read length distance, but in opposite direction. Default: filtering enabled
• -U|--nosv
  Turn off structural variant calling
• -L INT
  The minimum structural variant length to be presented using <DEL> <DUP> <INV> <INS>, etc. Default: 1000. Any indel, complex variants less than this will be spelled out with exact nucleotides
• -w|--insert-size INT INSERT_SIZE
  The insert size. Used for SV calling. Default: 300
• -W|--insert-std INT INSERT_STD
  The insert size STD. Used for SV calling. Default: 100
• -A INT INSERT_STD_AMT
  The number of STD. A pair will be considered for DEL if INSERT > INSERT_SIZE + INSERT_STD_AMT * INSERT_STD. Default: 4
• -Y|--ref-extension INT
  Extension of bp of reference to build lookup table. Default to 1200 bp. Increasing the number will slow down the program. The main purpose is to call large indels with 1000 bp that can be missed by discordant mate pairs.
• --deldupvar
  Turn on deleting of duplicate variants in output that can appear due to VarDict linear work on regions. Variants in this mode are considered and outputted only if start position of variant is inside the region interest.
• -DP|--default-printer
  The printer type used for different outputs. Default: OUT (i.e. System.out).
• --adaptor
  Filter adaptor sequences so that they are not used in realignment. Multiple adaptors can be supplied by setting them with comma, like:
  --adaptor ACGTTGCTC,ACGGGGTCTC,ACGCGGCTAG
• -J|--crispr CRISPR_cutting_site
  The genomic position that CRISPR/Cas9 suppose to cut, typically 3bp from the PAM NGG site and within the guide. For CRISPR mode only. It will adjust the variants (mostly In-Del) start and end sites to as close to this location as possible, if there are alternatives. The option should only be used for CRISPR mode.
• -j CRISPR_filtering_bp
  In CRISPR mode, the minimum amount in bp that a read needs to overlap with cutting site. If a read does not meet the criteria, it will not be used for variant calling, since it is likely just a partially amplified PCR. Default: not set, or no filtering
• --nmfreq
  The variant frequency threshold to determine variant as good in case of non-monomer MSI. Default: 0.1
• --mfreq
  The variant frequency threshold to determine variant as good in case of monomer MSI. Default: 0.25
• --fisher
  EXPERIMENTAL FEATURE: to exclude R script from the VarDict pipeline we added this option to calculate pvalue and oddratio from Fisher Test. It will decrease time processing on big samples because R script uses slow textConnection function. If you use this, do NOT run teststrandbias.R or testsomatic.R after Vardict, but use var2vcf_valid.pl or var2vcf_paired.pl after VarDictJava as usual.

Important var2vcf_valid.pl options

The full list of options in VarDictPerl var2vcf_valid.pl -h

• -A
  Indicate to output all variants at the same position. By default, only the variant with the highest allele frequency is converted to VCF.
• -S
  If set, variants that didn't pass filters will not be present in VCF file.
• -N string
  The sample name to be used directly.
• -f float
  The minimum allele frequency. Default to 0.02

Important var2vcf_paired.pl options

The full list of options in VarDictPerl var2vcf_paired.pl -h

• -M
  If set, will increase stringency for candidate somatic: flag P0.01Likely and InDelLikely, and add filter P0.05
• -A
  Indicate to output all variants at the same position. By default, only the variant with the highest allele frequency is converted to VCF.
• -S
  If set, variants that didn't pass filters will not be present in VCF file.
• -N string
  The sample name(s). If only one name is given, the matched will be simply names as "name-match". Two names are given separated by "|", such as "tumor|blood".
• -f float
  The minimum allele frequency. Default to 0.02

Output columns

Simple mode:

1. Sample - sample name
2. Gene - gene name from a BED file
3. Chr - chromosome name
4. Start - start position of the variation
5. End - end position of the variation
6. Ref - reference sequence
7. Alt - variant sequence
8. Depth (DP) - total coverage
9. AltDepth (VD) - variant coverage
10. RefFwdReads (REFBIAS) - reference forward strand coverage
11. RefRevReads (REFBIAS) - reference reverse strand coverage
12. AltFwdReads (VARBIAS) - variant forward strand coverage
13. AltRevReads (VARBIAS) - variant reverse strand coverage
14. Genotype - genotype description string
15. AF - allele frequency
16. Bias - strand bias flag
17. PMean - mean position in read
18. PStd - flag for read position standard deviation (1 if the variant is covered by at least 2 read segments with different positions, otherwise 0).
19. QMean - mean base quality
20. QStd - flag for base quality standard deviation
21. MAPQ - mapping quality
22. QRATIO (SN) - ratio of high quality reads to low-quality reads
23. HIFREQ (HIAF) - variant frequency for high-quality reads
24. EXTRAFR (ADJAF) - Adjusted AF for indels due to local realignment
25. SHIFT3 - No. of bases to be shifted to 3 prime for deletions due to alternative alignment
26. MSI - MicroSatellite. > 1 indicates MSI
27. MSILEN - MicroSatellite unit length in bp
28. NM - average number of mismatches for reads containing the variant
29. HICNT - number of high-quality reads with the variant
30. HICOV - position coverage by high quality reads
31. 5pFlankSeq (LSEQ) - neighboring reference sequence to 5' end
32. 3pFlankSeq (RSEQ) - neighboring reference sequence to 3' end
33. SEGMENT:CHR_START_END - position description
34. VARTYPE - variant type
35. DUPRATE - duplication rate in fraction
36. SV splits-pairs-clusters: Splits (SPLITREAD) - No. of split reads supporting SV, Pairs (SPANPAIR) - No. of pairs supporting SV, Clusters - No. of clusters supporting SV
37. CRISPR - only in crispr mode - how close to a CRISPR site is the variant

Amplicon mode

In amplicon mode columns from #35 are changed to:
(35) GoodVarCount (GDAMP) - number of good variants on amplicon
(36) TotalVarCount (TLAMP) - number of good and bad variants on amplicon
(37) Nocov (NCAMP) - number of variants on amplicon that has depth less than 1/50 of the max depth (they will be considered not working and thus not used).
(38) Ampflag - if there are different good variants on different amplicons, it will be 1.

Somatic mode

In somatic mode we have information from both samples:

1. Sample - sample name
2. Gene - gene name from a BED file
3. Chr - chromosome name
4. Start - start position of the variation
5. End - end position of the variation
6. Ref - reference sequence
7. Alt - variant sequence
   Fields from first sample:
8. Depth (DP) - total coverage
9. AltDepth (VD) - variant coverage
10. RefFwdReads (REFBIAS) - reference forward strand coverage
11. RefRevReads (REFBIAS) - reference reverse strand coverage
12. AltFwdReads (VARBIAS) - variant forward strand coverage
13. AltRevReads (VARBIAS) - variant reverse strand coverage
14. Genotype - genotype description string
15. AF - allele frequency
16. Bias - strand bias flag
17. PMean - mean position in read
18. PStd - flag for read position standard deviation
19. QMean - mean base quality
20. QStd - flag for base quality standard deviation
21. MAPQ - mapping quality
22. QRATIO (SN) - ratio of high quality reads to low-quality reads
23. HIFREQ (HIAF) - variant frequency for high-quality reads
24. EXTRAFR (ADJAF) - Adjusted AF for indels due to local realignment
25. NM - average number of mismatches for reads containing the variant
    Fields from second sample:
26. Depth - total coverage
27. AltDepth - variant coverage
28. RefFwdReads (REFBIAS) - reference forward strand coverage
29. RefRevReads (REFBIAS) - reference reverse strand coverage
30. AltFwdReads (VARBIAS) - variant forward strand coverage
31. AltRevReads (VARBIAS) - variant reverse strand coverage
32. Genotype - genotype description string
33. AF - allele frequency
34. Bias - strand bias flag
35. PMean - mean position in read
36. PStd - flag for read position standard deviation
37. QMean - mean base quality
38. QStd - flag for base quality standard deviation
39. MAPQ - mapping quality
40. QRATIO (SN) - ratio of high quality reads to low-quality reads
41. HIFREQ (HIAF) - variant frequency for high-quality reads
42. EXTRAFR (ADJAF) - Adjusted AF for indels due to local realignment
43. NM - average number of mismatches for reads containing the variant
    Common fields:
44. SHIFT3 - No. of bases to be shifted to 3 prime for deletions due to alternative alignment
45. MSI - MicroSatellite. > 1 indicates MSI
46. MSILEN - MicroSatellite unit length in bp
47. 5pFlankSeq (LSEQ) - neighboring reference sequence to 5' end
48. 3pFlankSeq (RSEQ) - neighboring reference sequence to 3' end
49. SEGMENT:CHR_START_END - position description
50. VarLabel - variant label due to type: StrongLOH, StrongSomatic...
51. VARTYPE - variant type
52. DUPRATE1 - duplication rate in fraction from first sample
53. SV_info1 - Splits - No. of split reads supporting SV, Pairs - No. of pairs supporting SV, Clusters - No. of clusters supporting SV from first sample
54. DUPRATE2 - duplication rate in fraction from second sample
55. SV_info2: Splits - No. of split reads supporting SV, Pairs - No. of pairs supporting SV, Clusters - No. of clusters supporting SV from second sample

Input Files

BED File – Regions

VarDict uses 2 types of BED files for specifying regions of interest: 8-column and all others. The 8-column file format is used for targeted DNA deep sequencing analysis (amplicon based calling), amplicon analysis will try to start if BED with 8 columns was provided. Otherwise you can start single and paired sample analysis by providing options -c, -S, -E, -g with number of columns for chromosome, start, end, gene of the region respectively.

All lines starting with #, browser, and track in a BED file are skipped. The column delimiter can be specified as the -d option (the default value is a tab “\t“).

The 8-column amplicon BED file format involves the following data:

• Chromosome name
• Region start position
• Region end position
• Gene name
• Score - not used by VarDict
• Strand - not used by VarDict
• Start position – VarDict starts outputting variants from this position
• End position – VarDict ends outputting variants from this position

For example 4-column BED file format involves the following data and VarDict must be start with -c 1 -S 2 -E 3 -g 4 to recognize it:

• Chromosome name
• Region start position
• Region end position
• Gene name

FASTA File - Reference Genome

The reference genome in FASTA format is read using HTSJDK library. For every invocation of the VarDict pipeline (usually 1 for a region in a BED file) and for every BAM file, a part of the reference genome is extracted from the FASTA file. In some cases of Structural Variants finding the reference can be reread in other regions.

Region of FASTA extends and this extension can be regulated via the REFEXT variable (option -Y INT, default 1200 bp).

Errors and warnings

Information about some of the errors and their causes is located in wiki

License

The code is freely available under the MIT license.

Contributors

Java port of VarDict implemented based on the original Perl version (Zhongwu Lai) by:

• Viktor Kirst

• Zaal Lyanov