SomaticSeq

• SomaticSeq is an ensemble caller that has the ability to use machine learning to filter out false positives. The detailed documentation is included in the package, located in docs/Manual.pdf. A quick guide can also be found here.
• SomaticSeq's open-access paper: Fang LT, Afshar PT, Chhibber A, et al. An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome Biol. 2015;16:197.
• Feel free to report issues and/or ask questions at the Issues page. You may also email Li Tai Fang at [email protected].
• The v2 branch will continue to be supported in the foreseeable future for bug fixes, but new features will only be introduced in the current master branch.

Requirements

• Python 3, plus pysam, numpy, and scipy libraries.
• R, plus ada library
• BEDTools
• Optional: dbSNP VCF file (if you want to use dbSNP membership as a feature).
• At least one of the callers we have incorporated, i.e., MuTect2/MuTect/Indelocator, VarScan2, JointSNVMix2, SomaticSniper, VarDict, MuSE, LoFreq, Scalpel, Strelka2, and/or TNscope.

Example commands

• At minimum, given the results of the individual mutation caller(s), SomaticSeq will extract sequencing features for the combined call set. Required inputs are --output-directory, --genome-reference, paired|single, --tumor-bam-file, and --normal-bam-file. Everything else is optional, although if you don't have a single VCF file input, the command will have nothing to do.

• The following four files will be created into the output directory:

  • Consensus.sSNV.vcf, Consensus.sINDEL.vcf, Ensemble.sSNV.tsv, and Ensemble.sINDEL.tsv.

• If you're searching for pipelines to run those individual somatic mutation callers, feel free to take advantage of our dockerized somatic mutation scripts.

# Merge caller results and extract SomaticSeq features
$somaticseq/somaticseq/run_somaticseq.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

• --inclusion-region or --exclusion-region will require BEDTools in your path.
• The command can be parallelized using $somaticseq/somaticseq_parallel.py script, with identical input options except for --threads X placed before the paired option to indicate X threads. The somaticseq_parallel.py is simply a script to create multiple sub-BED files, and then invokes somaticseq/run_somaticseq.py on each of those sub-BED files in parallel, then merge the results. It also requires BEDTools in your path.
• For all those input VCF files, either .vcf or .vcf.gz are acceptable.

Additional parameters to be specified before paired option to invoke training mode. In addition to the four files specified above, two additional files (classifiers) will be created, i.e., Ensemble.sSNV.tsv.ntChange.Classifier.RData and Ensemble.sINDEL.tsv.ntChange.Classifier.RData.

• --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 option invoke prediction mode (to use classifiers to score variants). Four additional files will be created, i.e., Seq.Classified.sSNV.vcf, SSeq.Classified.sSNV.tsv, SSeq.Classified.sINDEL.vcf, and SSeq.Classified.sINDEL.tsv.

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

Without those paramters above to invoking training or prediction mode, SomaticSeq will default to majority-vote consensus mode.

Do not worry if Python throws the following warning. This occurs when SciPy attempts a statistical test with empty data, e.g., z-scores between reference- and variant-supporting reads will be NaN if there is no reference read at a position.

  RuntimeWarning: invalid value encountered in double_scalars
  z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)

Dockerized workflows and pipelines

To run somatic mutation callers and then SomaticSeq

We have created scripts that run all the dockerized somatic mutation callers and then SomaticSeq at utilities/dockered_pipelines. All you need is docker.

To create training data to create SomaticSeq classifiers

We have also dockerized pipelines for in silico mutation spike in at utilities/dockered_pipelines/bamSimulator. These pipelines are based on BAMSurgeon. We use it to create training set to build SomaticSeq classifiers.

GATK's best practices for alignment

The limited pipeline to generate BAM files based on GATK's best practices is at utilities/dockered_pipelines/alignments.

Additional workflows

• A Snakemake workflow to run the somatic mutation callers and SomaticSeq, created by Afif Elghraoui, is at utilities/snakemake.
• All the docker scripts have their corresponding singularity versions at utilities/singularities. They're created automatically with this script. They are not as extensively tested or optimized as the dockered ones. Read the pages at the dockered pipelines for descriptions and how-to's. Please let us know at Issues if any of them does not work.

Video tutorial

This 8-minute video was created for SomaticSeq v1.0. The details are slightly outdated, but the main points remain the same.