title: EPSILON: an eQTL prioritization framework using similarity measures derived from local networks.
abstract: BACKGROUND
  When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene.
  
  
  RESULTS
  We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5)).
  
  
  BACKGROUND
  The physical interaction network and the source code (Matlab/C++) of our implementation can be downloaded from http://bioinformatics.intec.ugent.be/epsilon.
  
  
  BACKGROUND
  lieven.verbeke@intec.ugent.be, kamar@psb.ugent.be, jan.fostier@intec.ugent.be
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium. lieven.verbeke@intec.ugent.be
authors.forename: Lieven P C
authors.initials: LP
authors.lastname: Verbeke
authors.forename: Lore
authors.initials: L
authors.lastname: Cloots
authors.forename: Piet
authors.initials: P
authors.lastname: Demeester
authors.forename: Jan
authors.initials: J
authors.lastname: Fostier
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
mesh_terms: D000465:Algorithms; D015870:Gene Expression; D055786:Gene Knockout Techniques; D009154:Mutation; D040641:Quantitative Trait Loci; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D012984:Software