Abstract
Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.
Highlights
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a technique that provides quantitative, genome-wide mapping of target protein binding events [1,2]
Benchmarking the performance of different peak calling programs is challenging, since there exists no comprehensive list of all genomic locations bound by the target under the experimental conditions
We make use of extensive lists of qPCR verified sites that are available for neuron-restrictive silencer factor (NRSF) (83 sites) [45] and growth-associated binding protein (GABP) (150 sites) [46]
Summary
Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a technique that provides quantitative, genome-wide mapping of target protein binding events [1,2]. Identifying putative protein binding sites from large, sequencebased datasets presents a bioinformatic challenge that has required considerable computational innovation despite the availability of numerous programs for ChIP-Chip analysis [3,4,5,6,7,8,9]. With the rising popularity of ChIP-seq, a demand for new analytical methods has led to the proliferation of available peak finding algorithms. Reviewing literature from the past three years, we noted 31 open source programs for finding peaks in ChIP-seq data (Table S1), in addition to the available commercial software. An appraisal of available analytical methods will better equip researchers to bridge the ‘‘next-generation gap’’ between sequencing and data analysis [10]
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