PICS: Probabilistic Inference for ChIP-seq
Article first published online: 1 JUN 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 151–163, March 2011
How to Cite
Zhang, X., Robertson, G., Krzywinski, M., Ning, K., Droit, A., Jones, S. and Gottardo, R. (2011), PICS: Probabilistic Inference for ChIP-seq. Biometrics, 67: 151–163. doi: 10.1111/j.1541-0420.2010.01441.x
- Issue published online: 14 MAR 2011
- Article first published online: 1 JUN 2010
- Received March 2009. Revised March 2010. Accepted March 2010.
- Bayesian hierarchical model;
- EM algorithm;
- Missing values;
- Mixture model;
- Transcription factor;
- Truncated data;
Summary 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.