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Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy

Shuye Pu¹, Karen Ronen¹, James Vlasblom¹, Jack Greenblatt² & Shoshana J. Wodak¹

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1. The Hospital for Sick Children
2. University of Toronto

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Document Type:

Manuscript

Date:

Received 13 August 2008 21:22 UTC; Posted 14 August 2008

Subjects:

Bioinformatics

Tags:

• functional module
• genetic interaction
• bicluster
• pleiotropy
• yeast

Abstract:

Epistatic or genetic interactions, representing the effects of mutations on the phenotypes caused by other mutations, can be very helpful for uncovering functional relationships between genes. Recently, the Epistasis Miniarray Profile (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. As part of this approach, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles. Here we present an original biclustering algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function, a goal that cannot be achieved with hierarchical clustering. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast S. cerevisiae. In addition to identifying the majority of the functional modules reported in these studies, our algorithm uncovers many recently documented and novel multi-functional relationships between genes and gene groups.

Collection:

AFP-Biosapiens 2008

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License:

This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Pu, Shuye, Ronen, Karen, Vlasblom, James, Greenblatt, Jack, and Wodak, Shoshana. Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2008.2182.1> (2008)

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