Paper Info
Title
An effective framework for reconstructing gene regulatory networks from genetical genomics data
Abstract
Motivation: Systems Genetics approaches, in particular those relying on genetical genomics data, put forward a new paradigm of large-scale genome and network analysis. These methods use naturally occurring multi-factorial perturbations (e.g. polymorphisms) in properly controlled and screened genetic crosses to elucidate causal relationships in biological networks. However, although genetical genomics data contain rich information, a clear dissection of causes and effects as required for reconstructing gene regulatory networks is not easily possible. Results: We present a framework for reconstructing gene regulatory networks from genetical genomics data where genotype and phenotype correlation measures are used to derive an initial graph which is subsequently reduced by pruning strategies to minimize false positive predictions. Applied to realistic simulated genetic data from a recent DREAM challenge, we demonstrate that our approach is simple yet effective and outperforms more complex methods (including the best performer) with respect to (i) reconstruction quality (especially for small sample sizes) and (ii) applicability to large data sets due to relatively low computational costs. We also present reconstruction results from real genetical genomics data of yeast. Availability: A MATLAB implementation (script) of the reconstruction framework is available at www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html Contact: klamt@mpi-magdeburg.mpg.de
Year
DOI
Venue
2013
10.1093/bioinformatics/bts679
Bioinformatics
Field
DocType
Volume
Genome,Graph,Data set,Biological network,Computer science,Genomics,Bioinformatics,Network analysis,Gene regulatory network,Genetic Crosses
Journal
29
Issue
ISSN
Citations 
2
1367-4803
4
PageRank 
References 
Authors
0.40
6
4
Name
Order
Citations
PageRank
Robert J Flassig1364.76
Sandra Heise291.61
Kai Sundmacher34912.51
Steffen Klamt491577.99