- 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 firstname.lastname@example.org.
- 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.
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 somaticseqand then run
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
At minimum, given the results of the individual mutation caller(s), SomaticSeq will extract sequencing features for the combined call set. Required inputs are
--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 somaticseq_parallel.py \ --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
--exclusion-regionwill require BEDTools in your path.
--algorithmwill 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 Xbefore the
pairedoption 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
bedtoolsin 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
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.
- 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.
This 8-minute video was created for SomaticSeq v1.0. The details are slightly outdated, but the main points remain the same.