Imputing and Predicting Quantitative Genetic Interactions in Epistatic MAPs

  • Colm Ryan
  • Gerard Cagney
  • Nevan Krogan
  • Pádraig Cunningham
  • Derek Greene
Part of the Methods in Molecular Biology book series (MIMB, volume 781)

Abstract

Mapping epistatic (or genetic) interactions has emerged as an important network biology approach for establishing functional relationships among genes and proteins. Epistasis networks are complementary to physical protein interaction networks, providing valuable insight into both the function of individual genes and the overall wiring of the cell. A high-throughput method termed “epistatic mini array profiles” (E-MAPs) was recently developed in yeast to quantify alleviating or aggravating interactions between gene pairs. The typical output of an E-MAP experiment is a large symmetric matrix of interaction scores. One problem with this data is the large amount of missing values – interactions that cannot be measured during the high-throughput process or whose measurements were discarded due to quality filtering steps. These missing values can reduce the effectiveness of some data analysis techniques and prevent the use of others. Here, we discuss one solution to this problem, imputation using nearest neighbors, and give practical examples of the use of a freely available implementation of this method.

Key words

Protein interactions Genetic interactions Epistasis Imputation Biological networks 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Colm Ryan
    • 1
  • Gerard Cagney
    • 2
  • Nevan Krogan
    • 3
  • Pádraig Cunningham
    • 1
  • Derek Greene
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublinIreland
  2. 2.Conway Institute of Biomolecular and Biomedical ResearchUniversity College DublinDublinIreland
  3. 3.Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical ResearchUniversity of CaliforniaSan FranciscoUSA

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