Abstract
Chromatin immunoprecipitation-sequencing (ChIP-seq) 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 experiments can generate improved motifs but require optimal differential analysis. We introduce peaKO—a computational method to automatically optimize motif analyses with knockout controls, which we compare to two other methods. PeaKO often improves elucidation of the target factor and highlights the benefits of knockout controls, which far outperform input controls. It is freely available at https://peako.hoffmanlab.org.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
Added an additional potential future direction, mentioned some further recent work, and expanded upon technical considerations in our Discussion. Also, made some minor textual revisions to improve the clarity in some areas.
