Learning transcriptional regulation on a genome scale: A theoretical analysis based on gene expression data

Research output: Contribution to journalArticle

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Abstract

The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs' activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.

LanguageEnglish (US)
Article numberbbr029
Pages150-161
Number of pages12
JournalBriefings in Bioinformatics
Volume13
Issue number2
DOIs
StatePublished - Mar 2012

Profile

Data Mining
Gene Regulatory Networks
Gene Expression Profiling
Microarrays
MicroRNAs
Gene expression
Computer Simulation
Transcription Factors
Genes
Yeasts
Learning
Genome
Gene Expression
Transcription factors
Reverse engineering
Transcription
RNA
Yeast
Data mining
Throughput

Keywords

  • Gene expression
  • Network reconstruction
  • Transcription factors
  • Transcriptional regulation

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems

Cite this

Learning transcriptional regulation on a genome scale : A theoretical analysis based on gene expression data. / Wu, Ming; Chan, Christina.

In: Briefings in Bioinformatics, Vol. 13, No. 2, bbr029, 03.2012, p. 150-161.

Research output: Contribution to journalArticle

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