Genecentric: uncovering graph-theoretic structure in high-throughput epistasis data
Speaker: Lenore Cowen, Tufts
Date: Wednesday, March 20 2013
Time: 11:30AM to 1:00PM
Refreshments: 11:00AM
Location: Stata Center 32-G575
Host: Bonnie Berger, MIT
Contact: Patrice Macaluso, macaluso@csail.mit.edu
Relevant URL: 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. Our 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. We introduce Genecentric, an easy-to-use software package that can find BPMs in epistasis data.
This is joint work with Andrew Gallant, Max Leiserson, Maxim Kachalov,
and Ben Hescott
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