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Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assay

Integrative analysis of high-throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of non-coding regulatory element function and its contribution to human disease. He...

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הוצא לאור ב:	Hum Mutat
Main Authors:	Shigaki, Dustin, Adato, Orit, Adhikar, Aashish N., Dong, Shengcheng, Hawkins-Hooker, Alex, Inoue, Fumitaka, Juven-Gershon, Tamar, Kenlay, Henry, Martin, Beth, Patra, Ayoti, Penzar, Dmitry P., Schubach, Max, Xiong, Chenling, Yan, Zhongxia, Boyle, Alan P., Kreimer, Anat, Kulakovskiy, Ivan V., Reid, John, Unger, Ron, Yosef, Nir, Shendure, Jay, Ahituv, Nadav, Kircher, Martin, Beer, Michael A.
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	2019
נושאים:	Article
גישה מקוונת:	https://ncbi.nlm.nih.gov/pmc/articles/PMC6879779/ https://ncbi.nlm.nih.gov/pubmed/31106481 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/humu.23797
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Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assay

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