Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning

MOTIVATION: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts...

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Detalles Bibliográficos
Autores principales: Hocking, Toby Dylan, Goerner-Potvin, Patricia, Morin, Andreanne, Shao, Xiaojian, Pastinen, Tomi, Bourque, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408812/
https://www.ncbi.nlm.nih.gov/pubmed/27797775
http://dx.doi.org/10.1093/bioinformatics/btw672

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408812/
https://www.ncbi.nlm.nih.gov/pubmed/27797775
http://dx.doi.org/10.1093/bioinformatics/btw672

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