A Bayesian system for modeling promoter structure: A case study of histone promoters

Abstract

Computational promoter modeling (recognition and annotation) helps unravel genes' features that play crucial role in their regulation. However, due to numerous complexities involved promoter modeling remains a challenging task today, though many approaches have been introduced in the past. My PhD research is an attempt in this direction and aims at modeling and recognition of specific (histone) promoter structures, which has till date received only partial success. I have proposed a novel methodology based on Bayesian networks to model histone promoter structures by exploiting properties of regulatory signals present in these promoters. The methodology attempts to discover regions in the human genome that have structures similar to histone promoter model; such regions may in part represent promoters of the genes that may potentially be coregulated with histone genes. My methodology provides a general-purpose framework to model promoter structures of any class of genes. The methodology has been shown to perform better than several other similar well-known programs. The results obtained in this study are statistically significant and are validated with experimental data.