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Author index

2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

2009 IEEE International Workshop on Genomic Signal Processing and Statistics

2008 IEEE International Workshop on Genomic Signal Processing and Statistics

2007 IEEE International Workshop on Genomic Signal Processing and Statistics

2006 IEEE International Workshop on Genomic Signal Processing and Statistics

Chromatin signature analysis and prediction of genome-wide novel promoters using finite mixture model

Content Provider	IEEE Xplore Digital Library
Author	Taslim, C. Shili Lin Kun Huang Huang, T.
Copyright Year	2011
Description	Author affiliation: Department of Molecular Virology, Immunology and Medical Genetics, Columbus, OH 43210, USA (Huang, T.) \|\| Department of Statistics, Columbus, OH 43210, USA (Taslim, C.; Shili Lin) \|\| Department of Biomedical Informatics, Columbus, OH 43210, USA (Kun Huang)
Abstract	Regulation of gene expression has been shown to involve not only binding of transcription factor in target gene promoters but also 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 be associated with gene activation. However, no clear pattern has been shown to predict human promoters. This paper proposed a novel quantitative approach to characterize chromatin signature and patterns of promoters, which are then used to predict novel (alternative) promoters. In this paper, chromatin immunoprecipitation methods followed by massive parallel sequencing (ChIP-seq) data against RNA Polymerase II (Pol II) and H3K4me2 are used to identify common patterns of promoter regions. These patterns were then used to search for similar patterns over the entire genome to find novel promoters. Common patterns of promoter regions are modeled using a mixture model involving double-exponential and uniform distributions. Regions with high correlations with the common patterns are identified as putative novel promoters. We used this proposed algorithm and RNA-seq data to identify novel promoters in the MCF7 cell line. We found 4,392 high-confidence regions that display the identified promoter patterns (referred to as putative novel promoters). Of these, 875 regions (20%) overlap with RNA transcripts. Around 70% of these putative novel promoters have overlapped with RNA transcripts, EST and/or non-coding RNA suggesting that these putative novel promoters might be promoters which are currently undiscovered.
Starting Page	13
Ending Page	16
File Size	383269
Page Count	4
File Format	PDF
ISBN	9781467304917
ISSN	21503001
e-ISBN	9781467304900
e-ISBN	9781467304894
DOI	10.1109/GENSiPS.2011.6169429
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation RNA Genomics Humans Polymers Gene expression Bioinformatics
Content Type	Text
Resource Type	Article

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