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

• Published in: Bioinformatics Vol. 27; no. 11; pp. 1569 - 1570
• Main Authors: Taslim, Cenny, Huang, Tim, Lin, Shili
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.06.2011
• Subjects: Algorithms; Applications Note; Binding Sites; Biological and medical sciences; Cell Line, Tumor; Chromatin Immunoprecipitation - methods; DNA-Binding Proteins - analysis; Fundamental and applied biological sciences. Psychology; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Statistical; Sequence Analysis, DNA; Software; Mixture theory; Models
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btr165

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. Availability and implementation: DIME is implemented as an R-package, which is available at http://www.stat.osu.edu/~statgen/SOFTWARE/DIME. It may also be downloaded from http://cran.r-project.org/web/packages/DIME/. Contact:  shili@stat.osu.edu