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Ni P, Wu S, Su Z. Validated Negative Regions (VNRs) in the VISTA Database might be Truncated Forms of Bona Fide Enhancers. ADVANCED GENETICS (HOBOKEN, N.J.) 2024; 5:2300209. [PMID: 38884049 PMCID: PMC11170074 DOI: 10.1002/ggn2.202300209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/16/2024] [Indexed: 06/18/2024]
Abstract
The VISTA enhancer database is a valuable resource for evaluating predicted enhancers in humans and mice. In addition to thousands of validated positive regions (VPRs) in the human and mouse genomes, the database also contains similar numbers of validated negative regions (VNRs). It is previously shown that the VPRs are on average half as long as predicted overlapping enhancers that are highly conserved and hypothesize that the VPRs may be truncated forms of long bona fide enhancers. Here, it is shown that like the VPRs, the VNRs also are under strong evolutionary constraints and overlap predicted enhancers in the genomes. The VNRs are also on average half as long as predicted overlapping enhancers that are highly conserved. Moreover, the VNRs and the VPRs display similar cell/tissue-specific modification patterns of key epigenetic marks of active enhancers. Furthermore, the VNRs and the VPRs show similar impact score spectra of in silico mutagenesis. These highly similar properties between the VPRs and the VNRs suggest that like the VPRs, the VNRs may also be truncated forms of long bona fide enhancers.
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Affiliation(s)
- Pengyu Ni
- Department of Bioinformatics and Genomics the University of North Carolina at Charlotte Charlotte NC 28223 USA
- Present address: Department of Molecular Biophysics & Biochemistry Yale University New Haven CT 06520 USA
| | - Siwen Wu
- Department of Bioinformatics and Genomics the University of North Carolina at Charlotte Charlotte NC 28223 USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics the University of North Carolina at Charlotte Charlotte NC 28223 USA
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Liu N, Low WY, Alinejad-Rokny H, Pederson S, Sadlon T, Barry S, Breen J. Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C. Epigenetics Chromatin 2021; 14:41. [PMID: 34454581 PMCID: PMC8399707 DOI: 10.1186/s13072-021-00417-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/19/2021] [Indexed: 11/30/2022] Open
Abstract
Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.
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Affiliation(s)
- Ning Liu
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Wai Yee Low
- The Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, 5371, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, The University of New South Wales, NSW, 2052, Sydney, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
| | - Stephen Pederson
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
- Dame Roma Mitchell Cancer Research Laboratories (DRMCRL), Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia
| | - Timothy Sadlon
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - Simon Barry
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia
- Core Member of UNSW Data Science Hub, The University of New South Wales, 2052, Sydney, Australia
- Women's & Children's Health Network, SA, 5006, North Adelaide, Australia
| | - James Breen
- Computational & Systems Biology, Precision Medicine Theme, South Australian Health & Medical Research Institute, SA, 5000, Adelaide, Australia.
- Robinson Research Institute, University of Adelaide, SA, 5005, Adelaide, Australia.
- Adelaide Medical School, University of Adelaide, SA, 5005, Adelaide, Australia.
- South Australian Genomics Centre (SAGC), South Australian Health & Medical Research Institute (SAHMRI), SA, 5000, Adelaide, Australia.
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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