|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ИНХС РАН |
||
Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assayстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: 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.
- Журнал: Human Mutation
- Номер: ja
- Год издания: 2019
- Издательство: John Wiley & Sons Inc.
- Местоположение издательства: United States
- DOI: 10.1002/humu.23797
- Аннотация: Abstract 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. Here we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five disease associated human enhancers and nine disease associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cell-types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of disease associated genetic variation. This article is protected by copyright. All rights reserved.
- Добавил в систему: Пензар Дмитрий Дмитриевич