Statistical assessment of the global regulatory role of histone acetylation in Saccharomyces cerevisiae

Guo-Cheng Yuan^*¹^,2 , Ping Ma^*³^,4 , Wenxuan Zhong¹ and Jun S Liu¹

¹Department of Statistics, Harvard University, Cambridge, MA 02138, USA

²Bauer Center for Genomics Research, Harvard University, Cambridge, MA 02138, USA

³Department of Statistics, University of Illinois, Champaign, IL 61820, USA

⁴Institute for Genomic Biology, University of Illinois, Champaign, IL 61820, USA

author email corresponding author email* Contributed equally

Genome Biology 2006, 7:R70doi:10.1186/gb-2006-7-8-r70


Published:	2 August 2006

Subject areas: Bioinformatics, Genetics

Abstract

Background

Histone acetylation plays important but incompletely understood roles in gene regulation. A comprehensive understanding of the regulatory role of histone acetylation is difficult because many different histone acetylation patterns exist and their effects are confounded by other factors, such as the transcription factor binding sequence motif information and nucleosome occupancy.

Results

We analyzed recent genomewide histone acetylation data using a few complementary statistical models and tested the validity of a cumulative model in approximating the global regulatory effect of histone acetylation. Confounding effects due to transcription factor binding sequence information were estimated by using two independent motif-based algorithms followed by a variable selection method. We found that the sequence information has a significant role in regulating transcription, and we also found a clear additional histone acetylation effect. Our model fits well with observed genome-wide data. Strikingly, including more complicated combinatorial effects does not improve the model's performance. Through a statistical analysis of conditional independence, we found that H4 acetylation may not have significant direct impact on global gene expression.

Conclusion

Decoding the combinatorial complexity of histone modification requires not only new data but also new methods to analyze the data. Our statistical analysis confirms that histone acetylation has a significant effect on gene transcription rates in addition to that attributable to upstream sequence motifs. Our analysis also suggests that a cumulative effect model for global histone acetylation is justified, although a more complex histone code may be important at specific gene loci. We also found that the regulatory roles among different histone acetylation sites have important differences.