PICS: Probabilistic Inference for ChIP-seq

TL;DR

PICS is a probabilistic method for analyzing ChIP-seq data that improves detection of transcription factor binding sites by modeling read distributions and incorporating prior information, leading to more accurate and confident binding site identification.

Contribution

The paper introduces PICS, a novel Bayesian hierarchical model that enhances ChIP-seq analysis by accurately identifying binding regions and estimating uncertainties, outperforming existing methods.

Findings

PICS predicts binding sites more consistent with known motifs.

It provides confidence estimates for binding event locations.

PICS outperforms MACS, QuEST, and CisGenome in benchmark tests.

Abstract

ChIP-seq, which combines chromatin immunoprecipitation with massively parallel short-read sequencing, can profile in vivo genome-wide transcription factor-DNA association with higher sensitivity, specificity and spatial resolution than ChIP-chip. While it presents new opportunities for research, ChIP-seq poses new challenges for statistical analysis that derive from the complexity of the biological systems characterized and the variability and biases in its digital sequence data. We propose a method called PICS (Probabilistic Inference for ChIP-seq) for extracting information from ChIP-seq aligned-read data in order to identify regions bound by transcription factors. PICS identifies enriched regions 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. Its per-event fragment length estimates also allow it to remove from analysis regions that have atypical lengths. PICS uses pre-calculated, 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. We compared PICS to the alternative methods MACS, QuEST, and CisGenome, using published GABP and FOXA1 data sets from human cell lines, and found that PICS' predicted binding sites were more consistent with computationally predicted binding motifs.