MutaGene Documentation¶
MutaGene is a Python package for analyzing mutations and mutational processes in cancer. It provides command-line tools that complement the MutaGene website.
How the subcommands fit together¶
flowchart LR
MAF["MAF / VCF<br/>mutations"] --> P["profile"]
G[("2bit genome<br/>assembly")] -.-> P
P --> PROF["96-channel<br/>profile"]
PROF --> S["signature"]
PROF --> R["rank"]
MAF --> R
MAF --> M["motif"]
G -.-> R
G -.-> M
S --> EXP["signature<br/>exposures"]
R --> DRV["ranked<br/>driver mutations"]
M --> ENR["motif<br/>enrichment"]
classDef out fill:#e8f4ea,stroke:#4a7c59,color:#1b3a29
classDef cmd fill:#e6eefc,stroke:#3b5ea8,color:#16264a
class P,S,R,M cmd
class PROF,EXP,DRV,ENR out
The genome assembly must match the coordinates in the input file; a mismatch is reported rather than left to produce quiet nonsense. See Profile: mutational profiles.
Subcommands¶
- Description
- Fetch Subpackage Documentation
- Profile: mutational profiles
- Rank: Identifying potential driver mutations
- Description
- 1. Rank command
- 2. Arguments
- 3. Interpretation of Rank Output
- 4. Examples
- 4.1. Use mutagene rank to analyze genes in sample1.maf using genome hg19 and cohort gcb_lymphomas
- 4.1.1. Command
- 4.1.2. Rank Output (only first 5 results are shown here)
- 4.2. Use mutagene rank to analyze genes in sample1.maf using genome hg19 and cohort gcb_lymphomas with a BScore threshold of 0.0003 between Potential Driver and Passenger mutations
- 4.2.1. Command
- 4.2.2. Rank Output (only 4 results around potential driver and passenger are shown here)
- 4.3. Use mutagene rank to analyze genes in sample1.maf using genome hg19 and cohort gcb_lymphomas with a BScore threshold of 0.000009 between Driver and Potential Driver mutations
- 4.3.1. Command
- 4.3.2. Rank Output (only 4 results around driver and potential driver are shown here)
- 4.4. Use mutagene rank to analyze genes in sample1.maf using genome hg19 and cohort gcb_lymphomas with a cohort size of 20
- 4.4.1. Command
- 4.4.2. Rank Output (only first 5 results are shown here)
- Motif: Search for the presence of mutational motifs in samples
- 1. Description
- 2. Motif search command line
- 3. Arguments
- 4. Interpretation of Motif Output
- 5. Examples
- 5.1. Search for all pre-identfied motifs in sample1.maf using genome hg19 in any strand
- 5.1.1. Command
- 5.1.2. Motif Output
- 5.1.3. Interpretation of output
- 5.2. Search for the presence of the C[A>T] motif in sample1.maf using hg19
- 5.2.1. Command
- 5.2.2. Motif Output
- 5.3. Search sample2.vcf for all preidentified motifs in mutagene using hg19, searching for each of the motifs on the transcribed strand, non-transcribed strand, plus both strands, and using a window size of plus/minus 30 bases from each mutation
- 5.3.1. Command
- 5.3.2. Motif Output
- 5.3.3. Interpretation of output
- Common arguments
- Identify: Identifying mutational profiles in samples
- Serve: the local web interface
- Arguments shared by the analysis subcommands
Installation¶
Requires Python 3.10 or higher:
pip install mutagene
For the local web interface:
pip install mutagene[web]
Citation¶
If you use MutaGene, please 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
For the driver ranking method (mutagene rank):
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