Abstract
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.
Original language | English |
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Pages (from-to) | 1280-1291 |
Number of pages | 12 |
Journal | Human Mutation |
Volume | 40 |
Issue number | 9 |
Early online date | 20 May 2019 |
DOIs | |
State | Published - 1 Sep 2019 |
Bibliographical note
Publisher Copyright:© 2019 Wiley Periodicals, Inc.
Funding
M.B., D.S., and A.P. are supported by NIH R01 HG007348 and NIH U01 HG009380. I.V.K. is supported by RFBR 18-34-20024. The CAGI experiment coordination is supported by NIH U41 HG007346 and the CAGI conference by NIH R13 HG006650. M.B., D.S., and A.P. are supported by NIH R01 HG007348 and NIH U01 HG009380. I.V.K. is supported by RFBR 18‐34‐20024. The CAGI experiment coordination is supported by NIH U41 HG007346 and the CAGI conference by NIH R13 HG006650.
Funders | Funder number |
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CAGI | |
NIH R13 HG006650 | R13 HG006650 |
NIH U01 HG009380 | U01 HG009380 |
NIH U41 HG007346 | U41 HG007346 |
RFBR 18-34-20024 | |
National Institutes of Health | |
National Human Genome Research Institute | R01HG007348 |
Russian Foundation for Basic Research | 18‐34‐20024 |
Keywords
- MPRA
- enhancers
- gene regulation
- machine learning
- promoters
- regulatory variation