Your browser doesn't support javascript.
loading
Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data.
Gallant, Andrew; Leiserson, Mark D M; Kachalov, Maxim; Cowen, Lenore J; Hescott, Benjamin J.
Afiliación
  • Gallant A; Department of Computer Science, Tufts University, Medford, MA 02155, USA.
BMC Bioinformatics ; 14: 23, 2013 Jan 18.
Article en En | MEDLINE | ID: mdl-23331614
ABSTRACT

BACKGROUND:

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 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.

RESULTS:

Genecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 181399-1409, 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 253043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation.

CONCLUSION:

We 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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Epistasis Genética Tipo de estudio: Prognostic_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2013 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Epistasis Genética Tipo de estudio: Prognostic_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2013 Tipo del documento: Article País de afiliación: Estados Unidos
...