HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data. Results: Here we introduce HPeak, a H idden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage. Conclusions: Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Alhaji, Baba B.; Dai, Hongsheng; Hayashi, Yoshiko; Vinciotti, Veronica; Harrison, Andrew; Lausen, Berthold: Bayesian analysis for mixtures of discrete distributions with a non-parametric component (2016)
- Ranciati, Saverio; Viroli, Cinzia; Wit, Ernst: Spatio-temporal model for multiple ChIP-seq experiments (2015)
- Yun, Jonghyun; Wang, Tao; Xiao, Guanghua: Bayesian hidden Markov models to identify RNA-protein interaction sites in PAR-CLIP (2014)
- Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)