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

Cenny Taslim; Tim Huang; Shili Lin

doi:10.1093/bioinformatics/btr165

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 journal › Article › peer-review

22 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 language	English (US)
Article number	btr165
Pages (from-to)	1569-1570
Number of pages	2
Journal	Bioinformatics
Volume	27
Issue number	11
DOIs	https://doi.org/10.1093/bioinformatics/btr165
State	Published - Jun 2011
Externally published	Yes

ASJC Scopus subject areas

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

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10.1093/bioinformatics/btr165

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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.",

author = "Cenny Taslim and Tim Huang and Shili Lin",

note = "Funding Information: Funding: This work was supported in part by National Cancer Institute (grant U54CA113001) and National Science Foundation (grant DMS-1042946).",

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

Abstract

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