Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

This article is part of the supplement: Selected articles from the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) 2011

Open Access Research

Integrative genome-wide chromatin signature analysis using finite mixture models

Cenny Taslim12*, Shili Lin1*, Kun Huang2* and Tim Hui-Ming Huang3

Author affiliations

1 Department of Statistics, The Ohio State University, Columbus, Ohio 43210, USA

2 Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA

3 Department of Molecular Medicine/Institute of Biotechnology and Cancer Therapy and Research Center University of Texas Health Science Center, San Antonio, Texas 78229, USA

For all author emails, please log on.

Citation and License

BMC Genomics 2012, 13(Suppl 6):S3  doi:10.1186/1471-2164-13-S6-S3

Published: 26 October 2012


Regulation of gene expression has been shown to involve not only the binding of transcription factor at target gene promoters but also the characterization of histone around which DNA is wrapped around. Some histone modification, for example di-methylated histone H3 at lysine 4 (H3K4me2), has been shown to bind to promoters and activate target genes. However, no clear pattern has been shown to predict human promoters. This paper proposed a novel quantitative approach to characterize patterns of promoter regions and predict novel and alternative promoters. We utilized high-throughput data generated using chromatin immunoprecipitation methods followed by massively parallel sequencing (ChIP-seq) technology on RNA Polymerase II (Pol-II) and H3K4me2. Common patterns of promoter regions are modeled using a mixture model involving double-exponential and uniform distributions. The fitted model obtained were then used to search for regions displaying similar patterns over the entire genome to find novel and alternative promoters. Regions with high correlations with the common patterns are identified as putative novel promoters. We used this proposed algorithm, RNA-seq data and several transcripts databases to find alternative promoters in MCF7 (normal breast cancer) cell line. We found 7,235 high-confidence regions that display the identified promoter patterns. Of these, 4,167 regions (58%) can be mapped to RefSeq regions. 2,444 regions are in a gene body or overlap with transcripts (non-coding RNAs, ESTs, and transcripts that are predicted by RNA-seq data). Some of these maybe potential alternative promoters. We also found 193 regions that map to enhancer regions (represented by androgen and estrogen receptor binding sites) and other regulatory regions such as CTCF (CCCTC binding factor) and CpG island. Around 5% (431 regions) of these correlated regions do not overlap with any transcripts or regulatory regions suggesting that these might be potential new promoters or markers for other annotation which are currently undiscovered.