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Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast

  • Irit Gat-Viks
  • Renana Meller
  • Martin Kupiec
  • Ron Shamir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6577)

Abstract

The availability of expression quantitative trait loci (eQTL) data can help understanding the genetic basis of variation in gene expression. However, it has proven difficult to accurately predict functional genetic changes due to low statistical power. To address this challenge, we developed a novel computational approach for combining eQTL data with complementary regulatory network to identify modules of genes, their underlying genetic polymorphism and their shared regulatory proteins activity. The resulting eQTL model implicates novel central protein complexes that share not only a regulatory protein but also an underlying genetic variation. Our method manifests higher sensitivity than prior computational efforts.

Keywords

Cellular Respiration Genetic Basis Regulatory Protein Computational Approach Weak Linkage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Irit Gat-Viks
    • 1
  • Renana Meller
    • 2
  • Martin Kupiec
    • 2
  • Ron Shamir
    • 2
  1. 1.Broad Institute of MIT and HarvardUSA
  2. 2.Tel-Aviv UniversityIsrael

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