peaKO | Danielle Denisko

PeaKO is a computational tool to identify motifs relevant to ChIP-seq experiments by combining two differential analysis approaches. It often improves elucidation of the target motif over other methods and highlights the benefits of knockout controls. Click here for a detailed description page.

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Denisko D, Viner C, Hoffman MM. 
Motif elucidation in ChIP-seq datasets with a knockout control. 
Bioinformatics Advances, 2023. 
Available from: https://doi.org/10.1093/bioadv/vbad031.
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Quick links:

I developed peaKO during my MSc at the University of Toronto. I used Snakemake to write the main workflow that ties together a series of Python scripts. It works with Slurm scheduling or just locally. The name “peaKO” (pee-koh) comes from “peak” and “KO” (knockout), but it’s also a reference to the name my grandma would have chosen for a hedgehog, as in “orange pekoe” :)

Overview of peaKO (left) and the similarity of de novo motifs to canonical known motifs (right).

Longer description: PeaKO is a computational method for identifying transcription factor binding motifs from wild-type/knockout paired ChIP-seq datasets. PeaKO optimizes motif analyses by implementing a dual-pipeline approach. The first pipeline incorporates differential motif analysis, while the second incorporates differential peak calling. I combined these pipelines to select for motifs that both have consistent matches within peaks and fall within regions of significant read pileup. PeaKO computes a new metric based on the proportion of overlapping peaks between both pipelines, with overlaps interpreted as genuine binding events. PeaKO uses this metric to rank a collection of known or de novo motifs, where top-ranked motifs are thought to be relevant to the ChIP-seq experiment.

Comparing motif rankings across methods (left), highlighting an example (GATA3 transcription factor) in which peaKO outperforms other methods (center), and using input controls instead of knockout controls, which give rise to worse rankings (right).

To install peaKO, follow the steps on GitHub:

conda create --name peako
conda activate peako # or source activate peako
conda install python=3.7
conda install -c anaconda beautifulsoup4=4.7 pandas
conda install -c bioconda -c conda-forge -c anaconda snakemake-minimal flake8 pathlib2 ipython twine
conda install -c bioconda pybedtools

python3 -m pip install peako

peako --help

# Usage: peako <outdir> <wt-bam> <ko-bam> <organism> <chr-sizes> <trf-masked-genome> <motif-database> [options]

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.PeaKO is freely available at https://peako.hoffmanlab.org.michael.hoffman@utoronto.ca

References

2023