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Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay
Author(s) -
Shigaki Dustin,
Adato Orit,
Adhikari Aashish N.,
Dong Shengcheng,
HawkinsHooker Alex,
Inoue Fumitaka,
JuvenGershon Tamar,
Kenlay Henry,
Martin Beth,
Patra Ayoti,
Penzar Dmitry D.,
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.
Publication year - 2019
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23797
Subject(s) - biology , epigenomics , saturated mutagenesis , enhancer , computational biology , reporter gene , chromatin , mutagenesis , genetics , regulatory sequence , transcription factor , dna , mutation , gene , dna methylation , gene expression , mutant
The 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 noncoding 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.