DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes | Zendy

Mariano J. Alvarez | Zendy; James C. Chen | Zendy; Andrea Califano | Zendy

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DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes

Author(s) -

Mariano J. Alvarez,

James C. Chen,

Andrea Califano

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv499

Subject(s) - bioconductor , r package , identification (biology) , computational biology , inference , biology , gene , computer science , phenotype , genetics , artificial intelligence , botany , computational science

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package.

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