Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data

  • Authors:

  • Andrew Gallant;Mark D. M. Leiserson;Maxim Kachalov;Lenore J. Cowen;Benjamin J. Hescott

  • Affiliations:

  • Tufts University, Medford, MA;Brown University, Providence, RI;Tufts University, Medford, MA;Tufts University, Medford, MA;Tufts University, Medford, MA

  • Venue:
  • Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

New 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 the 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.