Background： Gene transcription in eukaryotie cells is collectively controlled by a large panel of ehromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. In...Background： Gene transcription in eukaryotie cells is collectively controlled by a large panel of ehromatin 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 computationhl 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 s＇cope 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.展开更多
文摘Background： Gene transcription in eukaryotie cells is collectively controlled by a large panel of ehromatin 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 computationhl 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 s＇cope 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.
