BayesPeak

**BayesPeak** is a software tool designed to work effectively and quickly analyze ChIP-seq data. ChIP-seq, which stands for Chromatin immunoprecipitation followed by next-generation sequencing, is a powerful technique widely used in biological studies for genome-wide measurements of protein-DNA interactions, DNA methylation, and histone modifications. However, the vast amount of data and biases introduced by sequencing and/or genome mapping present new challenges and call for effective methods and fast computer programs for statistical analysis.

To systematically model ChIP-seq data, BayesPeak builds a dynamic signal profile for each chromosome, then modeled using a fully Bayesian hidden Ising model. The proposed model naturally considers spatial dependency and global and local distributions of sequence tags, which can be used for both one-sample and two-sample analyses. Through model diagnosis, the proposed method can detect falsely enriched regions caused by sequencing and/or mapping errors, which the existing hypothesis-testing-based methods usually do not offer.

The proposed method has been illustrated using three transcription factor (TF) ChIP-seq data sets and two mixed ChIP-seq data sets. It has been compared with four popular and/or well-documented methods: MACS, CisGenome, BayesPeak, and SISSRs. The results indicate that the proposed method achieves equivalent or higher sensitivity and spatial resolution in detecting TF binding sites with a false discovery rate at a much lower level than the other methods.