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Motif elucidation in ChIP-seq datasets with a knockout control

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...

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Detalles Bibliográficos
Autores principales: Denisko, Danielle, Viner, Coby, Hoffman, Michael M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074035/
https://www.ncbi.nlm.nih.gov/pubmed/37033469
http://dx.doi.org/10.1093/bioadv/vbad031
Descripción
Sumario: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