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

Cenny Taslim, Tim Huang, Shili Lin

Producción científica: Articlerevisión exhaustiva

22 Citas (Scopus)

Resumen

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.

Idioma originalEnglish (US)
Número de artículobtr165
Páginas (desde-hasta)1569-1570
Número de páginas2
PublicaciónBioinformatics
Volumen27
N.º11
DOI
EstadoPublished - jun 2011
Publicado de forma externa

ASJC Scopus subject areas

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

Huella

Profundice en los temas de investigación de 'DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models'. En conjunto forman una huella única.

Citar esto