Using local gene expression similarities to discover regulatory binding site modules.
BACKGROUND
We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to relate the combinations of transcription factor binding sites (also referred to as binding site modules) identified in gene promoters to the expression of these genes. The novel aspects include local expression similarity clustering and an exact IF-THEN rule inference algorithm. We also provide a method of rule generalization to include genes with unknown expression profiles.


RESULTS
We have implemented the proposed framework and tested it on publicly available datasets from yeast S. cerevisae. The testing procedure consists of thorough statistical analyses of the groups of genes matching the rules we infer from expression data against known sets of co-regulated genes. For this purpose we have used published ChIP-Chip data and Gene Ontology annotations. In order to make these tests more objective we compare our results with recently published similar studies.


CONCLUSIONS
Results we obtain show that local expression similarity clustering greatly enhances overall quality of the derived rules, both in terms of enrichment of Gene Ontology functional annotation and coherence with ChIP-Chip binding data. Our approach thus provides reliable hypotheses on co-regulation that can be experimentally verified. An important feature of the method is its reliance only on widely accessible sequence and expression data. The same procedure can be easily applied to other microbial organisms.
Wilczyński
Bartek
B
lnstitute of Mathematics, Polish Academy of Sciences, Warsaw, Poland. bartek@impan.gov.pl <bartek@impan.gov.pl>
Hvidsten
Torgeir R
TR
Kryshtafovych
Andriy
A
Tiuryn
Jerzy
J
Komorowski
Jan
J
Fidelis
Krzysztof
K
D001665:Binding Sites; D002453:Cell Cycle; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005656:Fungal Proteins / Q000737:chemistry; D015870:Gene Expression; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012984:Software
D005656:Fungal Proteins