Titre
PICS: probabilistic inference for ChIP-seq.
Type
article
Institution
Externe
Périodique
Auteur(s)
Zhang, X.
Auteure/Auteur
Robertson, G.
Auteure/Auteur
Krzywinski, M.
Auteure/Auteur
Ning, K.
Auteure/Auteur
Droit, A.
Auteure/Auteur
Jones, S.
Auteure/Auteur
Gottardo, R.
Auteure/Auteur
Liens vers les personnes
ISSN
1541-0420
Statut éditorial
Publié
Date de publication
2011-03
Volume
67
Numéro
1
Première page
151
Dernière page/numéro d’article
163
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Résumé
ChIP-seq combines chromatin immunoprecipitation with massively parallel short-read sequencing. While it can profile genome-wide in vivo transcription factor-DNA association with higher sensitivity, specificity, and spatial resolution than ChIP-chip, it poses new challenges for statistical analysis that derive from the complexity of the biological systems characterized and from variability and biases in its sequence data. We propose a method called PICS (Probabilistic Inference for ChIP-seq) for identifying regions bound by transcription factors from aligned reads. PICS identifies binding event locations by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. It uses precalculated, whole-genome read mappability profiles and a truncated t-distribution to adjust binding event models for reads that are missing due to local genome repetitiveness. It estimates uncertainties in model parameters that can be used to define confidence regions on binding event locations and to filter estimates. Finally, PICS calculates a per-event enrichment score relative to a control sample, and can use a control sample to estimate a false discovery rate. Using published GABP and FOXA1 data from human cell lines, we show that PICS' predicted binding sites were more consistent with computationally predicted binding motifs than the alternative methods MACS, QuEST, CisGenome, and USeq. We then use a simulation study to confirm that PICS compares favorably to these methods and is robust to model misspecification.
PID Serval
serval:BIB_38E05B59A639
PMID
Date de création
2022-02-28T10:45:35.111Z
Date de création dans IRIS
2025-05-20T16:54:49Z