Research article

An improved map of conserved regulatory sites for Saccharomyces cerevisiae

Kenzie D MacIsaac^1,2* , Ting Wang^3* , D Benjamin Gordon^1,4 , David K Gifford^1,2 , Gary D Stormo³ and Ernest Fraenkel^1,2,5

¹  Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA

²  MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar St., Cambridge, MA 02139, USA

³  Department of Genetics, Washington University Medical School, St. Louis, MO 63110, USA

⁴  Agilent Technologies, 245 1st Street, Cambridge, MA 02142, USA

⁵  Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2006, 7:113doi:10.1186/1471-2105-7-113

Published:	7 March 2006

Abstract

Background

The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data.

Results

Applying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators.

Conclusion

Combining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.