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A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

机译：在ChIP-seq实验中检测转录因子结合位点的方法的实用比较

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Background Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications. Results The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments. Conclusions In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.
机译：背景染色质免疫沉淀与大规模并行测序（ChIP-seq）的结合越来越多地用于研究全基因组范围内的转录调控。尽管最近提出了许多算法来分析大型ChIP-seq数据集，但在实际应用中仍不清楚它们的相对优缺点。结果本研究比较了在各种实际研究设置下检测四个不同ChIP-seq数据集中转录因子结合位点的最新算法。首先，我们演示了当应用不同的算法时，生物学结论可能会发生巨大变化。然后研究跨生物学复制品的可重复性，作为检测的内部验证。最后，将每种方法的预测结合位点与高分值的结合基序以及在独立qPCR实验中确认的结合区进行比较。结论总的来说，我们的结果表明，计算方法的最佳选择在很大程度上取决于所分析的数据集。除了向该技术的用户提供有关结合位点检测方法特性的有价值的信息外，系统的评估框架还为开发ChIP-seq数据的改进算法的开发人员提供了有用的参考。

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