Publications by authors named "Dustin Shigaki"

2 Publications

  • Page 1 of 1

Enhancer Predictions and Genome-Wide Regulatory Circuits.

Annu Rev Genomics Hum Genet 2020 08 22;21:37-54. Epub 2020 May 22.

Sloan Kettering Institute, New York, NY 10065, USA; email:

Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are -regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.
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http://dx.doi.org/10.1146/annurev-genom-121719-010946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644210PMC
August 2020

Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay.

Hum Mutat 2019 09 23;40(9):1280-1291. Epub 2019 Jun 23.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland.

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.
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http://dx.doi.org/10.1002/humu.23797DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879779PMC
September 2019