利用基因敲除对照对ChIP-seq数据集中的基序进行阐释。

Motif elucidation in ChIP-seq datasets with a knockout control.

作者信息

Denisko Danielle, Viner Coby, Hoffman Michael M

机构信息

Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.

Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada.

出版信息

Bioinform Adv. 2023 Mar 16;3(1):vbad031. doi: 10.1093/bioadv/vbad031. eCollection 2023.

DOI:10.1093/bioadv/vbad031

PMID:37033469

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10074035/

Abstract

SUMMARY

Chromatin immunoprecipitation-sequencing is widely used to find transcription factor binding sites, but suffers from various sources of noise. Knocking out the target factor mitigates noise by acting as a negative control. Paired wild-type and knockout (KO) experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO-a computational method to automatically optimize motif analyses with KO controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of KO controls, which far outperform input controls.

AVAILABILITY AND IMPLEMENTATION

PeaKO is freely available at https://peako.hoffmanlab.org.

CONTACT

michael.hoffman@utoronto.ca.

摘要

染色质免疫沉淀测序被广泛用于寻找转录因子结合位点，但存在各种噪声来源。敲除目标因子作为阴性对照可减轻噪声。配对的野生型和敲除（KO）实验可以生成改进的基序，但需要优化差异分析。我们引入了peaKO——一种利用KO对照自动优化基序分析的计算方法，并将其与其他两种方法进行了比较。PeaKO通常能改善对目标因子的阐释，并突出KO对照的优势，KO对照的性能远优于输入对照。