IZKF Computational Biology Research Group, RWTH Aachen University Medical School, Aachen, Germany.
Department of Cell Biology, Institute of Biomedical Engineering, RWTH Aachen University Medical School, Aachen, Germany.
Nat Methods. 2016 Apr;13(4):303-9. doi: 10.1038/nmeth.3772. Epub 2016 Feb 22.
DNase-seq allows nucleotide-level identification of transcription factor binding sites on the basis of a computational search of footprint-like DNase I cleavage patterns on the DNA. Frequently in high-throughput methods, experimental artifacts such as DNase I cleavage bias affect the computational analysis of DNase-seq experiments. Here we performed a comprehensive and systematic study on the performance of computational footprinting methods. We evaluated ten footprinting methods in a panel of DNase-seq experiments for their ability to recover cell-specific transcription factor binding sites. We show that three methods--HINT, DNase2TF and PIQ--consistently outperformed the other evaluated methods and that correcting the DNase-seq signal for experimental artifacts significantly improved the accuracy of computational footprints. We also propose a score that can be used to detect footprints arising from transcription factors with potentially short residence times.
DNase-seq 允许在计算上搜索 DNA 上类似足迹的 DNase I 切割模式，从而在核苷酸水平上识别转录因子结合位点。在高通量方法中，经常会出现实验伪影（如 DNase I 切割偏倚），影响对 DNase-seq 实验的计算分析。在这里，我们对计算足迹方法的性能进行了全面系统的研究。我们在一组 DNase-seq 实验中评估了十种足迹方法，以评估它们识别细胞特异性转录因子结合位点的能力。我们表明，三种方法——HINT、DNase2TF 和 PIQ——始终优于其他评估方法，并且校正实验伪影的 DNase-seq 信号可显著提高计算足迹的准确性。我们还提出了一个评分，可以用于检测可能具有短停留时间的转录因子产生的足迹。
