Tao Ma, Zhenqing Ye and Liguo Wang* Pages 7641 - 7654 ( 14 )
Background: Transcription factors are DNA-binding proteins that play key roles in many fundamental biological processes. Unraveling their interactions with DNA is essential to identify their target genes and understand the regulatory network. Genome-wide identification of their binding sites became feasible thanks to recent progress in experimental and computational approaches. ChIP-chip, ChIP-seq, and ChIP-exo are three widely used techniques to demarcate genome-wide transcription factor binding sites.Objective: This review aims to provide an overview of these three techniques including their experiment procedures, computational approaches, and popular analytic tools. Conclusion: ChIP-chip, ChIP-seq, and ChIP-exo have been the major techniques to study genome- wide in vivo protein-DNA interaction. Due to the rapid development of next-generation sequencing technology, array-based ChIP-chip is deprecated and ChIP-seq has become the most widely used technique to identify transcription factor binding sites in genome-wide. The newly developed ChIP-exo further improves the spatial resolution to single nucleotide. Numerous tools have been developed to analyze ChIP-chip, ChIP-seq and ChIP-exo data. However, different programs may employ different mechanisms or underlying algorithms thus each will inherently include its own set of statistical assumption and bias. So choosing the most appropriate analytic program for a given experiment needs careful considerations. Moreover, most programs only have command line interface so their installation and usage will require basic computation expertise in Unix/Linux.
Transcription factor, transcription factor binding site, chromatin immunoprecipitation, microarrays, next-generation sequencing, ChIP-chip, ChIP-seq, ChIP-exo, data analysis.
Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905