Graduate Group in Microbiology, University of California Davis, Davis, California, United States of America.
PLoS One. 2010 Jul 8;5(7):e11471. doi: 10.1371/journal.pone.0011471.
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.
下一代 DNA 测序与染色质免疫沉淀（ChIP-seq）相结合正在彻底改变我们全面研究蛋白质-DNA 相互作用的能力。从 ChIP-seq 数据中鉴定蛋白质结合位点需要新的计算工具，与用于分析 ChIP-Chip 实验的工具不同。ChIP-seq 的日益普及促使许多不同的分析程序（到目前为止，我们注意到有 31 个开源方法）得以发展，每个程序都有一些据称的优势。鉴于文献资料丰富，经验基准测试具有挑战性，因此选择适当的 ChIP-seq 分析方法已成为一项艰巨的任务。在这里，我们比较了十一种不同的峰调用程序在常见的经验、转录因子数据集上的性能，并衡量了它们的灵敏度、准确性和可用性。我们的分析提供了对现有技术的公正评价，应该有助于研究人员选择合适的工具来处理 ChIP-seq 数据。
