Application of biostatistics and bioinformatics tools to identify putative transcription factor-gene regulatory network of ankylosing spondylitis and sarcoidosis

Dongseok Choi, Srilakshmi M. Sharma, Sirichai Pasadhika, Zhixin Kang, Christina A. Harrington, Justine R. Smith, Stephen R. Planck, James T. Rosenbaum

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation. We combined biostatistics and bioinformatics tools to streamline identification of putative transcription factor-gene regulatory networks unique for two immune-mediated diseases, ankylosing spondylitis and sarcoidosis. After identifying differentially expressed genes from microarrays, we employed tightCluster to find tight clusters of potentially co-regulated genes. By subsequently applying bioinformatics tools to search for common cis-regulatory elements, putative transcription factor-gene regulatory networks were found. Recognition of these networks by applying this methodology could pave the way for new insights into disease pathogenesis.

Original languageEnglish (US)
Pages (from-to)3326-3338
Number of pages13
JournalCommunications in Statistics - Theory and Methods
Volume38
Issue number18
DOIs
StatePublished - 2009

Keywords

  • Affymetrix microarrays
  • Ankylosing spondylitis
  • Cluster analysis
  • Geneexpression
  • Sarcoidosis
  • TightClust
  • Transcription factor

ASJC Scopus subject areas

  • Statistics and Probability

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