Identifying regulatory subnetworks for a set of genes.
High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons. Unfortunately it is often difficult to discern meaningful biological relationships from such lists. This study presents a new bioinformatic approach that can be used to identify regulatory subnetworks for lists of significant genes or proteins. We demonstrate the utility of this approach using an interaction network for yeast constructed from BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip data bases and lists of genes from well known metabolic pathways or differential expression experiments. The approach accurately rediscovers known regulatory elements of the heat shock response as well as the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast. We also find evidence supporting a previous conjecture that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated. Finally we demonstrate a previously unknown connection between GAL80 and the diauxic shift in yeast.
Scott
Michelle S
MS
McGill Centre for Bioinformatics, 3775 University Street, Montreal H3A 2B4, Canada.
Perkins
Theodore
T
Bunnell
Scott
S
Pepin
François
F
Thomas
David Y
DY
Hallett
Michael
M
D000465:Algorithms; D019295:Computational Biology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000502:physiology*; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000502:physiology; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins