Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data.

BMC Bioinformatics
Yuji ZhangHabtom W Ressom

Abstract

Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycl...Continue Reading

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Citations

Oct 14, 2008·Tissue Engineering. Part a·Bahar BilgenGilda A Barabino
Sep 2, 2009·Microbiology and Molecular Biology Reviews : MMBR·Sacha A F T van HijumOscar P Kuipers
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Methods Mentioned

BETA
two-hybrid
immunoprecipitation

Software Mentioned

ClusterJudge
PSO
BSEA
OSU
FuncAssociate
GSEA FuncAssociate
GSEA
SVM Support Vector Machine Toolbox
GA
WebMOTIFS

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