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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
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author Denisko, Danielle
Viner, Coby
Hoffman, Michael M
author_facet Denisko, Danielle
Viner, Coby
Hoffman, Michael M
author_sort Denisko, Danielle
collection PubMed
description 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
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spelling pubmed-100740352023-04-06 Motif elucidation in ChIP-seq datasets with a knockout control Denisko, Danielle Viner, Coby Hoffman, Michael M Bioinform Adv Original Paper 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 Oxford University Press 2023-03-16 /pmc/articles/PMC10074035/ /pubmed/37033469 http://dx.doi.org/10.1093/bioadv/vbad031 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Denisko, Danielle
Viner, Coby
Hoffman, Michael M
Motif elucidation in ChIP-seq datasets with a knockout control
title Motif elucidation in ChIP-seq datasets with a knockout control
title_full Motif elucidation in ChIP-seq datasets with a knockout control
title_fullStr Motif elucidation in ChIP-seq datasets with a knockout control
title_full_unstemmed Motif elucidation in ChIP-seq datasets with a knockout control
title_short Motif elucidation in ChIP-seq datasets with a knockout control
title_sort motif elucidation in chip-seq datasets with a knockout control
topic Original Paper
url 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
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