Identification of Transcriptional Regulatory Elements by Functional Enrichment Analysis.

Identification of Transcriptional Regulatory Elements by Functional Enrichment Analysis.

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by Amitava Karmaker , Stephen Kwek

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@MISC{Karmaker_identificationof,
    author = {Amitava Karmaker and Stephen Kwek},
    title = {Identification of Transcriptional Regulatory Elements by Functional Enrichment Analysis.},
    year = {}
}

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Abstract

Abstract- Deciphering the complex interaction between transcriptional regulatory (both trans- and cis-) elements comprehensively and identifying these potential binding sites are fundamental problems in functional genomics. Therefore, determining the transcription factors that regulate a gene in different cell types and the cis-regulatory elements they are binding to will help lay the foundation for building gene regulatory networks. While many computational approaches have been developed for lower eukaryotes and prokaryotes, most of them often do not generalize to vertebrates. Here, we use gene ontological evidences to perform functional enrichment analysis among the TFs and genes, and group the functionally related genes to characterize their transcriptional association. We also analyze correlations between TFs and genes using their expression profiles. Thus, we search for putative transcriptional regulatory elements (transcription factor binding sites) along core promoter regions of the grouped genes. The performance of our search is highly satisfactory in term of binding site hit accuracy.

Citations

6232	Maximum likelihood from incomplete data via the em algorithm - Dempster, Laird, et al. - 1977
760	ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 2000 - Ashburner, Ball, et al.
196	Identifying DNA and protein patterns with statistically significant alignments of multiple sequences - Hertz, Stormo - 1999
181	TRANSFAC: transcriptional regulation, from patterns to profiles - Matys, Fricke, et al. - 2003
66	GD: ANN-Spec: a method for discovering transcription factor binding sites with improved specificity. Pac Symp Biocomput - CT, Stormo
64	et al. Comprehensive identification of cell cycle–regulated genes of the yeast saccharomyces cerecisiae by microarray hybridization - Spellman - 1998
53	Applied bioinformatics for the identification of regulatory elements - Wasserman, Sandelin - 2004
40	A: Intrinsic errors in genome annotation - Devos, Valencia
35	ConSite: web-based prediction of regulatory elements using cross-species comparison - Sandelin, Wasserman, et al. - 2004
34	PO: New components of a system for phosphate accumulation and polyphosphate metabolism in Saccharomyces cerevisiae revealed by genomic expression analysis - Ogawa, DeRisi, et al.
33	Discovery and modeling of transcriptional regulatory regions - Fickett, Wasserman
33	Computational detection of genomic cis-regulatory modules applied to body patterning in the early Drosophila embryo - Rajewsky, Vergassola, et al. - 2002
29	Factors influencing the identification of transcription factor binding sites by cross-species comparison - McCue, Thompson, et al. - 2002
29	I.H.: Data mining in bioinformatics using Weka - Frank, Hall, et al. - 2004
28	et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005 - Tompa, Li, et al.
23	Prediction of yeast protein-protein interaction network: insights from the gene ontology and annotations - Wu, Zhu, et al. - 2006
21	Systematic changes in gene expression patterns following adaptive evolution - Ferea, Botstein, et al. - 1999
12	Touzet H: Predicting transcription factor binding sites using local over-representation and comparative genomics - Defrance
12	SE: Environmentally induced foregut remodeling by - Ao, Gaudet, et al.
10	A Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length.” Bioinformatics - Favorov, Gelfand, et al. - 2005
9	P-Match: transcription factor binding site search by combining patterns and weight matrices - Chekmenev, Haid, et al. - 2005
6	Y: Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data - SY, Kim
6	Higgins et al., A DNA microarray survey of gene expression in normal human tissues. Genome Biology 6 - Shyamsundar, Kim, et al. - 2005
4	et al., "Global analysis of protein localization in budding yeast - Huh - 2003
4	SiteSeer: Visualisation and analysis of transcription factor binding sites in nucleotide sequences - Boardman - 2003
2	Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs - Jin - 2006
2	et al., "Exploring the metabolic and genetic control of gene expression on a genomic scale - DeRisi - 1997
2	et al., "Gaussian mixture clustering and imputation of microarray data - Ouyang - 2004
2	et al., "DNA microarray data imputation and significance analysis of differential expression - Jornsten - 2005
2	et al., "A meta-data based method for DNA microarray imputation - Jornsten - 2007
1	et al., "Ontology-based knowledge representation for bioinformatics - Stevens - 2000
1	et al., "Quantifying the relationship between co-expression, co-regulation and gene function - Allocco - 2004
1	et al., "In silico representation and discovery of transcription factor binding sites - Pavesi - 2004
1	et al., "Cluster-Buster: Finding dense clusters of motifs in DNA sequences - Frith - 2003
1	et al., "Finding functional sequence elements by multiple local alignment - Frith - 2004
1	et al., "MSCAN: identification of functional clusters of transcription factor binding sites - Alkema - 2004
1	et al., "Regulatory element detection using correlation with expression - Bussemaker - 2001
1	et al., "Gene therapy of metastatic disease: progress and prospects - Maron - 2001