Show simple item record Tamborero Noguera, David González-Pérez, Abel López Bigas, Núria 2017-01-25T11:06:22Z 2017-01-25T11:06:22Z 2015-11
dc.description OncodriveCLUST 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 OncodriveCLUST installed run the following command:/n(env) $ pip install oncodriveclust/nAnd that's all. The following command will allow you to check that is correctly installed by showing the command help:/n(env) $ oncodriveclust --help/nusage: oncodriveclust [-h] [--version] [-o PATH] [--cgc PATH] [-m INT] [-c]/n [-p INT]/n NON-SYN-PATH SYN-PATH GENE-TRANSCRIPTS/nRun OncodriveCLUST analysis/npositional arguments:/n NON-SYN-PATH The path to the NON-Synonymous mutations file to be/n checked/n SYN-PATH The path to the Synonymous mutations file to construct/n the background model/n GENE-TRANSCRIPTS The path of a file containing transcripts length for/n genes/noptional arguments:/n -h, --help show this help message and exit/n --version show program's version number and exit/n -o PATH, --out PATH Define the output file path/n --cgc PATH The path of a file containing CGC data/n -m INT, --muts INT Minimum number of mutations of a gene to be included/n in the analysis ('5' by default)/n -c, --coord Use this argument for printing cluster coordinates in/n the output file/n --pos INT AA position column index ('-1' by default)/n -d INT, --dist INT Intra cluster maximum distance ('5' by default)/n -p FLOAT, --prob FLOAT/n Probability of the binomial model to find cluster/n seeds ('0.01' by default)/n --dom PATH The path of a file containing gene domains/n -L LEVEL, --log-level LEVEL/n Define the loggging level
dc.description.abstract OncodriveCLUST is a method aimed to identify genes whose mutations are biased towards a large spatial clustering. This method is designed to exploit the feature that mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a sign that mutations in these regions change the function of these proteins in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours, and this property can thus be used to nominate novel candidate driver genes./nThe method does not assume that the baseline mutation probability is homogeneous across all gene positions but it creates a background model using silent mutations. Coding silent mutations are supposed to be under no positive selection and may reflect the baseline clustering of somatic mutations. Given recent evidences of non-random mutation processes along the genome, the assumption of homogenous mutation probabilities is likely an oversimplication introducing bias in the detection of meaningful events.
dc.language.iso eng
dc.publisher Universitat Pompeu Fabra
dc.relation Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes. Bioinformatics. 2013; 19(18): 2238-44. DOI: 10.1093/bioinformatics/btt395
dc.relation Més informació: OncodriveCLUST (Bitbucket)
dc.rights Universitat Pompeu Fabra Free Source Code License Agreement. Consulteu les condicions d'ús específiques dins del document.
dc.title OncodriveCLUST
dc.type info:eu-repo/semantics/other
dc.type Software
dc.subject.keyword Cancer
dc.subject.keyword Genes
dc.subject.keyword Mutation
dc.subject.keyword Clustering
dc.rights.accessRights info:eu-repo/semantics/openAccess


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