OncodriveFM detects candidate cancer driver genes and pathways from catalogs of somatic mutations in a cohort of tumors by computing the bias towards the accumulation of functional mutations (FM bias).This novel approach avoids some known limitations of recurrence-based approaches, such as the dif?culty to estimate background mutation rate, and the fact that they usually fail to identify lowly recurrently mutated driver genes.

OncodriveFM depends on Python 3 and some external libraries, numpy, scipy, pandas and statsmodels./nThe easiest way to install all this software stack is using the well known Anaconda Python distribution./nThen to get OncodriveFM installed run the following command:/n(env) $ pip install oncodrivefm/nAnd that's all. The following command will allow you to check that is correctly installed by showing the command help:/n(env) $ oncodrivefm --help/nusage: oncodrivefm [-h] [-o PATH] [-n NAME] [--output-format FORMAT]/n [-N NUMBER] [-e ESTIMATOR] [--gt THRESHOLD]/n [--pt THRESHOLD] [-s SLICES] [-m PATH] [--save-data]/n [--save-analysis] [-j CORES] [-D KEY=VALUE] [-L LEVEL]/n DATA/nCompute the FM bias for genes and pathways/npositional arguments:/n DATA File containing the data matrix in TDM format/noptional arguments:/n -h, --help show this help message and exit/n -o PATH, --output-path PATH/n Directory where output files will be written/n -n NAME Analysis name/n --output-format FORMAT/n The FORMAT for the output file/n -N NUMBER, --samplings NUMBER/n Number of samplings to compute the FM bias pvalue/n -e ESTIMATOR, --estimator ESTIMATOR/n Test estimator for computation./n --gt THRESHOLD, --gene-threshold THRESHOLD/n Minimum number of mutations per gene to compute the FM/n bias/n --pt THRESHOLD, --pathway-threshold THRESHOLD/n Minimum number of mutations per pathway to compute the/n FM bias/n -s SLICES, --slices SLICES/n Slices to process separated by commas/n -m PATH, --mapping PATH/n File with mappings between genes and pathways to be/n analysed/n --save-data The input data matrix will be saved/n --save-analysis The analysis results will be saved/n -j CORES, --cores CORES/n Number of cores to use for calculations. Default is 0/n that means all the available cores/n -D KEY=VALUE Define external parameters to be saved in the results/n -L LEVEL, --log-level LEVEL/n Define log level: debug, info, warn, error, critical,/n notset