Literature DB >> 11262934

BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes.

X Liu1, D L Brutlag, J S Liu.   

Abstract

The development of genome sequencing and DNA microarray analysis of gene expression gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses zero to third-order Markov background models whose parameters are either given by the user or estimated from a specified sequence file. The significance of each motif found is judged based on a motif score distribution estimated by a Monte Carlo method. In addition, BioProspector modifies the motif model used in the earlier Gibbs samplers to allow for the modeling of gapped motifs and motifs with palindromic patterns. All these modifications greatly improve the performance of the program. Although testing and development are still in progress, the program has shown preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP. We are currently working on combining BioProspector with a clustering program to explore gene expression networks and regulatory mechanisms.

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Year:  2001        PMID: 11262934

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  304 in total

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Authors:  Natalie E Simpson; Jason Gertz; Keren Imberg; Richard M Myers; Michael J Garabedian
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2.  Statistical significance of clusters of motifs represented by position specific scoring matrices in nucleotide sequences.

Authors:  Martin C Frith; John L Spouge; Ulla Hansen; Zhiping Weng
Journal:  Nucleic Acids Res       Date:  2002-07-15       Impact factor: 16.971

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Authors:  Martha L Bulyk; Abigail M McGuire; Nobuhisa Masuda; George M Church
Journal:  Genome Res       Date:  2004-02       Impact factor: 9.043

4.  Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.

Authors:  Peter M Haverty; Ulla Hansen; Zhiping Weng
Journal:  Nucleic Acids Res       Date:  2004-01-02       Impact factor: 16.971

5.  Finding functional sequence elements by multiple local alignment.

Authors:  Martin C Frith; Ulla Hansen; John L Spouge; Zhiping Weng
Journal:  Nucleic Acids Res       Date:  2004-01-02       Impact factor: 16.971

6.  Gibbs Recursive Sampler: finding transcription factor binding sites.

Authors:  William Thompson; Eric C Rouchka; Charles E Lawrence
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

Review 7.  Computational approaches to identify promoters and cis-regulatory elements in plant genomes.

Authors:  Stephane Rombauts; Kobe Florquin; Magali Lescot; Kathleen Marchal; Pierre Rouzé; Yves van de Peer
Journal:  Plant Physiol       Date:  2003-07       Impact factor: 8.340

8.  A non-parametric model for transcription factor binding sites.

Authors:  Oliver D King; Frederick P Roth
Journal:  Nucleic Acids Res       Date:  2003-10-01       Impact factor: 16.971

9.  SeqVISTA: a new module of integrated computational tools for studying transcriptional regulation.

Authors:  Zhenjun Hu; Yutao Fu; Anason S Halees; Szymon M Kielbasa; Zhiping Weng
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

10.  The Rok protein of Bacillus subtilis represses genes for cell surface and extracellular functions.

Authors:  Mark Albano; Wiep Klaas Smits; Linh T Y Ho; Barbara Kraigher; Ines Mandic-Mulec; Oscar P Kuipers; David Dubnau
Journal:  J Bacteriol       Date:  2005-03       Impact factor: 3.490

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