DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models

Cenny Taslim, Tim Huang, Shili Lin

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Differential Identification using Mixtures Ensemble (DIME) is a package for identification of biologically significant differential binding sites between two conditions using ChIP-seq data. It considers a collection of finite mixture models combined with a false discovery rate (FDR) criterion to find statistically significant regions. This leads to a more reliable assessment of differential binding sites based on a statistical approach. In addition to ChIP-seq, DIME is also applicable to data from other high-throughput platforms.

Original languageEnglish (US)
Article numberbtr165
Pages (from-to)1569-1570
Number of pages2
JournalBioinformatics
Volume27
Issue number11
DOIs
StatePublished - Jun 1 2011
Externally publishedYes

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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