New publication from the Hoffman Lab
The lab of MBP scientist Dr. Michael Hoffman has published a new Bioinformatics Advances article titled "Motif elucidation in ChIP-seq datasets with a knockout control". MBP MSc student Danielle Denisko acted as co-first-author on the paper.
Article Abstract
"Our manuscript introduces a novel software method, PeaKO, for identifying transcription factor motifs in the context of knockout control ChIP-seq experiments. Despite the potential for knockout ChIP-seq experiments to permit improvements in discerning genuine motifs from the background, they are presently rarely employed. Analysis methods specific to knockout controls remain almost completely unexplored, with currently only one other published method available. This gap results in poorer transcription factor binding site elucidation and fewer high-quality motifs for downstream regulatory analyses.
Here, we address the need for a good knockout ChIP-seq analysis method with freely available user-friendly software. PeaKO implements a dual-pipeline approach, combining two strategies for subtracting putative noise from the wild-type experiment, using a knockout control. We demonstrated PeaKO made consistent improvements across other methods in our tests on publicly available wild-type/knockout ChIP-seq datasets. We also show the vast superiority of knockout controls to input controls that until recently were the standard in the field. We anticipate that this demonstration will lead to an acceleration of the uptake of knockout controls by experimentalists.
To our knowledge, PeaKO is the first fully-automated method for wild-type/knockout ChIP-seq analyses, allowing all steps to be conducted by simply providing a reference genome, motif database, and alignments. We expect that PeaKO will become a standard for processing ChIP-seq datasets, as knockout controls continue to gain popularity."