Publication [Open access]

Download SomaticSeq

• The source code and README's are hosted at https://github.com/bioinform/somaticseq
• The packaged releases can be found at https://github.com/bioinform/somaticseq/releases
• Docker image can be downloaded at https://hub.docker.com/r/lethalfang/somaticseq/

Software Dependencies

• Python3, plus numpy, scipy, pysam, pandas, and xgboost packages
• BEDtools (bedtools should be in the PATH)
• Optional: dbSNP and COSMIC VCF file (if you want to use those information as a part of your training set)

Running SomaticSeq

• All VCF, BAM, and FASTA files need to be sorted identically. For VCF files, both .vcf and .vcf.gz are acceptable.
• The following command can be run when those individual mutation callers have completed running. To use our entire somatic mutation pipelines, read the README in our source code. The pipeline is available in both Docker and Singularity.

Example command to merge caller results and extract SomaticSeq features

Tumor-normal paired mode

somaticseq_parallel.py --output-directory $OUTPUT_DIR --genome-reference GRCh38.fa --inclusion-region genome.bed --exclusion-region blacklist.bed paired --tumor-bam-file tumor.bam --normal-bam-file matched_normal.bam --mutect2-vcf MuTect2/variants.vcf --varscan-snv VarScan2/variants.snp.vcf --varscan-indel VarScan2/variants.indel.vcf --jsm-vcf JointSNVMix2/variants.snp.vcf --somaticsniper-vcf SomaticSniper/variants.snp.vcf --vardict-vcf VarDict/variants.vcf --muse-vcf MuSE/variants.snp.vcf --lofreq-snv LoFreq/variants.snp.vcf --lofreq-indel LoFreq/variants.indel.vcf --scalpel-vcf Scalpel/variants.indel.vcf --strelka-snv Strelka/variants.snv.vcf --strelka-indel Strelka/variants.indel.vcf

Tumor-only single mode

somaticseq_parallel.py --output-directory $OUTPUT_DIR --genome-reference GRCh38.fa --inclusion-region genome.bed --exclusion-region blacklist.bed single --bam-file tumor.bam --mutect2-vcf MuTect2/variants.vcf --varscan-vcf VarScan2/variants.vcf --vardict-vcf VarDict/variants.vcf --lofreq-vcf LoFreq/variants.vcf --scalpel-vcf Scalpel/variants.indel.vcf --strelka-vcf Strelka/variants.vcf

Additional parameters to be specified before paired or single option to invoke training mode.

• --somaticseq-train: FLAG to invoke training mode with no argument, which also requires the following inputs, R and ada package in R
• --truth-snv: if you have ground truth VCF file for SNV
• --truth-indel: if you have a ground truth VCF file for INDEL

Additional input files to be specified before paired or single option invoke prediction mode (to use classifiers to score variants)

• --classifier-snv: classifier (.RData file) previously built for SNV
• --classifier-indel: classifier (.RData file) previously built for INDEL

Notes

• Without the paramters above to invoke training or prediction mode, SomaticSeq will default to majority-vote consensus mode.
• --inclusion-region and/or --exclusion-region will require BEDTools in your path.
• To split the job into multiple threads, place --threads X before the paired option to indicate X threads. It simply creates multiple BED file (each consisting of 1/X of total base pairs) for SomaticSeq to run on each of those sub-BED files in parallel. It then merges the results. This requires bedtools in your path.
• For all input VCF files, either .vcf or .vcf.gz are acceptable.

Contact Us

Please report bugs and suggestions to the report issues page. The developers will be notified immediately. Alternatively, you may email ltfang@gmail.com.

References/Tools

• MuTect: Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213-9.
• VarScan2: Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22(3):568-76.
• JointSNVMix2: Roth A, Ding J, Morin R, et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics. 2012;28(7):907-13.
• SomaticSniper: Larson DE, Harris CC, Chen K, et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics. 2012;28(3):311-7.
• VarDict: Lai Z, Markovets A, Ahdesmaki M, Chapman B, et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Research. 2016.
• Indelocator: Banerji S, Cibulskis K, Rangel-escareno C, et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012;486(7403):405-9.
• MuSE: Fan Y, Xi L, Hughes DS, et al. MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biol. 2016;17(1):178.
• LoFreq: Wilm A, Aw PP, Bertrand D, et al. LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Res. 2012;40(22):11189-201.
• Scalpel: Narzisi G, O'rawe JA, Iossifov I, et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat Methods. 2014;11(10):1033-6.
• Strelka2: Kim S, Scheffler K, Halpern AL, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods. 2018;15(8):591-594.