ATARiS: Computational quantification of gene suppression phenotypes from multisample RNAi screens

Diane D. Shao, Aviad Tsherniak, Shuba Gopal, Barbara A. Weir, Pablo Tamayo, Nicolas Stransky, Steven E. Schumacher, Travis I. Zack, Rameen Beroukhim, Levi A. Garraway, Adam A. Margolin, David E. Root, William C. Hahn, Jill P. Mesirov

Research output: Contribution to journalArticlepeer-review

93 Scopus citations


Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of offtarget transcripts. Here we present a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for data sets that contain as few as 10 samples and can be used to analyze screens of any number of targeted genes. We used this analytic approach to interrogate RNAi data derived from screening more than 100 human cancer cell lines and identified HNF1B as a transforming oncogene required for the survival of cancer cells that harbor HNF1B amplifications. ATARiS is publicly available at ataris.

Original languageEnglish
Pages (from-to)665-678
Number of pages14
JournalGenome Research
Issue number4
StatePublished - Apr 2013
Externally publishedYes


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