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Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts

机译:ChIP-seq峰调用算法的快速创新超出了基准测试的努力

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The current understanding of the regulation of transcription does not keep the pace with the spectacular advances in the determination of genomic sequences. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) promises to give better insight into transcription regulation by locating sites of protein-DNA interactions. Such loci of putative interactions can be inferred from the genome-wide distributions of ChIP-seq data by peak-calling software. The analysis of ChIP-seq data critically depends on this step and a multitude of these peak-callers have been deployed in the recent years. A recent study reported severe variation among peak-calling results.Yet, peak-calling still lacks systematic quantitative benchmarking.Here, we summarize benchmarking efforts and explain potential drawbacks of each benchmarking method.
机译:目前对转录调控的理解不能跟上基因组序列测定的惊人进展。染色质免疫沉淀后再进行大规模并行测序(ChIP-seq),有望通过定位蛋白质-DNA相互作用的位点来更好地了解转录调控。可以通过峰调用软件从ChIP-seq数据的全基因组分布中推断出这样的推定相互作用位点。 ChIP-seq数据的分析严重依赖于此步骤,并且近年来已经部署了许多此类峰值调用程序。最近的一项研究报告了峰调用结果之间的严重差异,但是,峰调用仍缺乏系统的定量基准测试。在此,我们总结了基准测试工作并解释了每种基准测试方法的潜在缺点。

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