Combinatorial Genetic Regulatory Network Analysis Tools for High Throughput Transcriptomic Data

: A series of genome-scale algorithms and high-performance implementations is described and shown to be useful in the genetic analysis of gene transcription. With them it is possible to address common questions such as: "are the sets of genes coexpressed under one type of conditions the same as those sets co-expressed under another?" A new noise-adaptive graph algorithm, dubbed "paraclique," is introduced and analyzed for use in biological hypotheses testing. A notion of vertex coverage is also devised, based on vertex-disjoint paths within correlation graphs, and used to determine the identity, proportion and number of transcripts connected to individual phenotypes and quantitative trait loci (QTL) regulatory models. A major goal is to identify which, among a set of candidate genes, are the most likely regulators of trait variation. These methods are applied in an effort to identify multiple-QTL regulatory models for large groups of genetically co-expressed genes, ...

• Type: Conference
• Year: 2005
• Where: RECOMB
• Authors: Elissa J. Chesler, Michael A. Langston
• Keywords: Biological Hypotheses Testing, Computational Biology, Quantitative Trait Loci, RECOMB 2005, Tasks Discrete Mathematics