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An ensemble approach to accurately detect somatic mutations using SomaticSeq
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somaticseq v3.3.0 xgboost Sep 12, 2019
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This dockerfile reveals the dependencies

  • Python 3, plus pysam, numpy, and scipy libraries.
  • R, plus ada and/or xgboost libraries: required in machine learning training or prediction mode. XGBoost also requires the caret package.
  • BEDTools: required when parallel processing in invoked, and/or when any bed files are used as input files
  • 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 (GATK4) / MuTect / Indelocator, VarScan2, JointSNVMix2, SomaticSniper, VarDict, MuSE, LoFreq, Scalpel, Strelka2, TNscope, and/or Platypus.
  • To install SomaticSeq scripts into your PATH, cd somaticseq and then run ./ install.

To install with bioconda

SomaticSeq can also be found on bioconda. To install with conda, which also automatically installs a bunch of 3rd-party somatic mutation callers: conda install -c bioconda somaticseq

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.

  • 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 \
--output-directory  $OUTPUT_DIR \
--genome-reference  GRCh38.fa \
--inclusion-region  genome.bed \
--exclusion-region  blacklist.bed \
--algorithm         ada \
--threads           24 \
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.
  • --algorithm will default to ada (adaptive boosting), but can also be xgboost (extreme gradient boosting). We have incorporated XGBoost recently. It can be orders of magnitude faster than AdaBoost, but we have not benchmarked it as comprehensively.
  • 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.

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., SSeq.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.

SomaticSeq Video

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