MutaGene is a set of methods for the analysis of mutations and mutational processes in cancer. The MutaGene Python package consists of command-line tools that provide direct access to some of the functions available on MutaGene's website https://www.ncbi.nlm.nih.gov/research/mutagene/.
The MutaGene software package includes 5 subpackages: fetch, profile, rank, motif, and signature. Each subpackage has a unique functionality.
Installation: MutaGene package is accessible via standard python repository PyPi. The package requires Python3.7 or higher.
It is recommended to use venv or conda to create an isolated environment for mutagene package:
python3 -m venv env_mutagene
source env_mutagene/bin/activate
Then install from pypi
pip install mutagene
Usage: The MutaGene package can be ran on the command line.
usage: mutagene [-v] [-V] [-h] {fetch, profile, rank, motif, signature} ...
MutaGene version 0.9.1.0 - Analysis of mutational processes and driver mutations
Global optional arguments:
-v, --verbose Print additional messages (-v, -vv)
-V, --version Show version and exit
-h, --help Show this help message and exit
Choose MutaGene subpackage:
fetch - Load data such as genomes and cancer datasets from demote sources (alias: download)
profile - Create a mutational profile given a sample with mutations
rank - Predict driver mutations by ranking observed mutations with respect to their expected mutability
motif - Test samples for presence of mutational motifs
signature - Identify activity of existing mutational signatures in samples or derive new signatures (aliases: identify, decompose)
{fetch, profile, rank, motif, signature}
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MutaGene Fetch allows you to download sample data, cohorts, and human genome reference sequences.
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MutaGene Profile allows you to analyze any set of mutations from one or several cancer samples to identify cancers of unknown primary tumor site, to detect the most likely mutational process and to distinguish tumorigenic from normal or benign mutation sets
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MutaGene Rank allows you to rank mutations with respect to their driver status in a given sample or cohort in a batch mode using pre-calculated or user-provided mutational profiles
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MutaGene Motif allows you to analyze the presence of mutational motifs in genomic data.
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MutaGene Signature allows you to analyze the presence of mutational signatures in genomic data.
usage: mutagene fetch [-h] {examples,cohorts,genome} ...
Download data from remote repositories and API
optional arguments:
-h, --help show this help message and exit
subcommands:
Choose data source
{examples,cohorts,genome}
additional help available for subcommands
cohorts cohorts help
mutagene profile --help
usage: mutagene profile [-h] [--infile [INFILE ...]] [--outfile [OUTFILE]] [--genome GENOME] [--input-format INPUT_FORMAT]
positional arguments:
optional arguments:
-h, --help show this help message and exit
--infile [INFILE ...], -i [INFILE ...]
Input file format
--outfile [OUTFILE], -o [OUTFILE]
Name of output file, will be generated in TSV format
--genome GENOME, -g GENOME
Location of genome assembly file
--input-format INPUT_FORMAT, -f INPUT_FORMAT
Input format: auto, MAF, VCF
usage: mutagene rank [-h] [--infile INFILE] [--genome GENOME] [--outfile [OUTFILE]] [--cohorts-file [COHORTS_FILE]] [--cohort [COHORT]] [--profile PROFILE] [--nsamples NSAMPLES]
[--threshold-driver THRESHOLD_DRIVER] [--threshold-passenger THRESHOLD_PASSENGER]
optional arguments:
-h, --help show this help message and exit
Required arguments:
--infile INFILE, -i INFILE
Input file in MAF format
--genome GENOME, -g GENOME
Location of genome assembly file in 2bit format
Optional arguments:
--outfile [OUTFILE], -o [OUTFILE]
--cohorts-file [COHORTS_FILE]
Location of tar.gz container or directory for cohorts
--cohort [COHORT], -c [COHORT]
Name of cohort with observed mutations
Advanced arguments:
--profile PROFILE, -p PROFILE
Override profile to calculate mutability, may also describe cohort size
--nsamples NSAMPLES, -n NSAMPLES
Override cohort size
--threshold-driver THRESHOLD_DRIVER, -td THRESHOLD_DRIVER
BScore threshold between Driver and Pontential Driver mutations
--threshold-passenger THRESHOLD_PASSENGER, -tp THRESHOLD_PASSENGER
BScore threshold between Pontential Driver and Passenger mutations
usage: mutagene motif [-h] [--infile INFILE] [--genome GENOME] [--input-format {MAF,VCF}] [--motif MOTIF] [--outfile [OUTFILE]] [--window-size WINDOW_SIZE] [--strand {T,N,A,TNA}]
[--threshold THRESHOLD] [--save-motif-matches SAVE_MOTIF_MATCHES] [--test {Fisher,Chi2}]
optional arguments:
-h, --help show this help message and exit
Required arguments:
--infile INFILE, -i INFILE
Input file in MAF or VCF format with one or multiple samples
--genome GENOME, -g GENOME
Location of genome assembly file in 2bit format
Optional arguments:
--input-format {MAF,VCF}, -f {MAF,VCF}
Input format: MAF, VCF
--motif MOTIF, -m MOTIF
Motif to search for, use the 'R[C>T]GY' syntax for the motif. Use quotes
--outfile [OUTFILE], -o [OUTFILE]
Name of output file, will be generated in TSV format
Advanced arguments:
--window-size WINDOW_SIZE, -w WINDOW_SIZE
Context window size for motif search, default setting is 50
--strand {T,N,A,TNA}, -s {T,N,A,TNA}
Transcribed strand (T), non-transcribed (N), any (A), or all (TNA default)
--threshold THRESHOLD, -t THRESHOLD
Significance threshold for qvalues, default value=0.05
--save-motif-matches SAVE_MOTIF_MATCHES
Save mutations in matching motifs to a BED file
--test {Fisher,Chi2} Statistical test to use
Examples:
# search in sample2.vcf for all preidentified motifs in mutagene using hg19
mutagene motif --infile sample2.vcf --input-format VCF --genome hg19
# search for the presence of the C[A>T] motif in sample1.maf using hg19 not checking for strand-specificity
mutagene motif --infile sample1.maf --input-format MAF --genome hg19 --motif 'C[A>T]' --strand A
usage: mutagene signature [-h] [--infile INFILE] [--genome GENOME] [--signatures {5,10,30,49,53,MGA,MGB,COSMICv2,COSMICv3,KUCAB}] [--input-format {MAF,VCF,TCGI}] [--outfile [OUTFILE]]
[--method [METHOD]] [--no-unexplained-variance] [--mutations-threshold MUTATIONS_THRESHOLD] [--keep-only KEEP_ONLY] [--bootstrap]
[--bootstrap-replicates BOOTSTRAP_REPLICATES] [--bootstrap-confidence-level BOOTSTRAP_CONFIDENCE_LEVEL] [--bootstrap-method {t,p}]
optional arguments:
-h, --help show this help message and exit
Required arguments:
--infile INFILE, -i INFILE
Input file in VCF or MAF format
--genome GENOME, -g GENOME
Location of genome assembly file in 2bit format
--signatures {5,10,30,49,53,MGA,MGB,COSMICv2,COSMICv3,KUCAB}, -s {5,10,30,49,53,MGA,MGB,COSMICv2,COSMICv3,KUCAB}
Collection of signatures to use
Optional arguments:
--input-format {MAF,VCF,TCGI}, -f {MAF,VCF,TCGI}
Input format: MAF, VCF
--outfile [OUTFILE], -o [OUTFILE]
Name of output file, will be generated in TSV format
Advanced arguments:
--method [METHOD], -m [METHOD]
Method defines the function minimized in the optimization procedure
--no-unexplained-variance, -U
Do not account for unexplained variance (non-context dependent mutational processes and unknown signatures)
--mutations-threshold MUTATIONS_THRESHOLD, -t MUTATIONS_THRESHOLD
Only report signatures with mutations above the threshold
--keep-only KEEP_ONLY, -k KEEP_ONLY
Keep only the signatures in the list, separated by commas e.g. 1,3,5
Bootstrap-specific arguments:
--bootstrap, -b Use the bootstrap to calculate confidence intervals
--bootstrap-replicates BOOTSTRAP_REPLICATES, -br BOOTSTRAP_REPLICATES
Number of bootstrap replicates
--bootstrap-confidence-level BOOTSTRAP_CONFIDENCE_LEVEL, -bcl BOOTSTRAP_CONFIDENCE_LEVEL
Confidence level
--bootstrap-method {t,p}, -bm {t,p}
Bootstrap method (t: t-distribution, p: percentile)
If you use MutaGene, you should cite:
Goncearenco A, Rager SL, Li M, Sang Q, Rogozin IB, Panchenko AR Exploring background mutational processes to decipher cancer genetic heterogeneity. Nucleic Acids Res. 2017; 45(W1):W514–W522. https://doi.org/10.1093/nar/gkx367
Additionally, if you use the MutaGene rank subpackage, you should cite the driver prediction method as:
Brown AL, Li M, Goncearenco A, Panchenko AR Finding driver mutations in cancer: Elucidating the role of background mutational processes. PLOS Computational Biology 2019; 15(4): e1006981. https://doi.org/10.1371/journal.pcbi.1006981
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