Background:Gene transcription in eukaryotic cells is collectively controlled by a large panel of chromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome.Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest,yet it is still a computationally challenging task.Results:Here,we briefly review the computational tools developed in recent years for differential binding analysis with ChIP-seq data.The methods are extensively classified by their strategy of statistical modeling and scope of application.Finally,a decision tree is presented for choosing proper tools based on the specific dataset.Conclusions:Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance.This review can serve as a practical guide for readers to select appropriate tools for their own datasets.