A strategy for extracting and analyzing large-scale quantitative epistatic interaction data

By Sean R. Collins, Maya Schuldiner, Nevan J. Krogan, Jonathan S. Weissman. Genome Biology, 2006

336 citations recorded by the source corpus.

Recently, approaches have been developed for high-throughput identification of synthetic sick/lethal gene pairs. However, these are only a specific example of the broader phenomenon of epistasis, wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile (E-MAP) technology, as well as several tools for higher-level analysis of the resulting data that are greatly enhanced by the quantitative score and detection of alleviating interactions.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c4637ab2ab7ae2f9f7f437a2fd813cbae3323665

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In Genetics

1. Charles Boone
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In Molecular Biology

1. E. Jeng
2. Charles Boone
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4. H. Bussey
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Citations in the corpus

• Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions (2017, positive): “…the following equation (A similarly modified t-value score was used to calculate a quantitative genetic interaction score, S score, in budding yeast 33 ): Where: U exp : the median of Norm-GIs of all double-sgRNAs targeting the gene pair N exp : the number of all double-sgRNAs targeting the gene…”
• Exploring genetic interactions and networks with yeast (2007, background): “Indeed, a quantitative analysis of an SGA interaction ma”
• Quantitative analysis of fitness and genetic interactions in yeast on a genome scale (2010, neutral): “Genetic interactions previously have been quantified from SGA experiments of functionally biased subsets of a yeast deletion mutant collection using an interaction score (S score”