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Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data
Andrew Gallant1, Mark D M Leiserson, Maxim Kachalov
1Department of Computer Science, Tufts University, Medford, MA 02155, USA.
BMC Bioinformatics
|January 22, 2013
Summary
Genecentric software identifies functional gene sets from genetic interaction data. This new package implements an algorithm to find compensatory pathways, aiding biological discovery.
Area of Science:
- Systems Biology
- Computational Biology
- Genomics
Background:
- High-throughput screens generate pairwise genetic interaction data in model organisms.
- Epistasis scores quantify the combined effect of gene knockouts.
- Algorithmic approaches can identify functional modules and compensatory pathways from this data.
Purpose of the Study:
- To introduce Genecentric, a publicly available software package for analyzing genetic interaction data.
- To implement a parallelized algorithm for generating generalized bipartite pathway models (BPMs).
Main Methods:
- Genecentric utilizes a parallelized algorithm based on Leiserson et al. (2011) to process epistasis data.
- The software generates generalized BPMs representing potential compensatory pathways.
- GenecentricGO extends functionality by integrating with FuncAssociate for Gene Ontology enrichment analysis.
Main Results:
- Genecentric successfully generates generalized BPMs from weighted epistasis matrices.
- The GenecentricGO extension provides GO enrichment statistics for identified BPMs.
- The package is implemented in Python with readily available documentation and examples.
Conclusions:
- Genecentric enables the identification of coherent functional and compensatory gene sets from high-throughput genetic interaction data.
- The software is freely available, promoting wider use in biological research.
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