ArticleBMC bioinformatics2013
Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A novel ILP framework to identify compensatory pathways in genetic interaction networks with GIDEON.Bioinformatics (Oxford, England) · 2026Article
- A Novel ILP Framework to Identify Compensatory Pathways in Genetic Interaction Networks with GIDEON.bioRxiv : the preprint server for biology · 2026Article
- Exploring weighting schemes for the discovery of informative generalized between pathway models to uncover pathways in genetic interaction networks.Scientific reports · 2025Article
- Review
- Assessment of network module identification across complex diseases.Nature methods · 2019Article
- Constructing module maps for integrated analysis of heterogeneous biological networks.Nucleic acids research · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundNew technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available.
resultsGenecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 18:1399-1409, 2011) for generating generalized BPMs from high-throughput genetic interaction data. Given a matrix of weighted epistasis values for a set of double knock-outs, Genecentric returns a list of generalized BPMs that may represent compensatory pathways. Genecentric also has an extension, GenecentricGO, to query FuncAssociate (Bioinformatics 25:3043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation.
conclusionWe find that Genecentric can be used to find coherent functional and perhaps compensatory gene sets from high throughput genetic interaction data. Genecentric is made freely available for download under the GPLv2 from http://bcb.cs.tufts.edu/genecentric.
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