Dataset Information

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.

ABSTRACT: BACKGROUND:Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. RESULTS:To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types. CONCLUSIONS:Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

SUBMITTER: Moore JE

PROVIDER: S-EPMC6977301 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.

Moore Jill E JE Pratt Henry E HE Purcaro Michael J MJ Weng Zhiping Z

Genome biology 20200122 1

<h4>Background</h4>Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes.<h4>Results</h4>To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactio ...[more]

PMID: 31969180

Similar Datasets

Project description:The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.

Dataset Information

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.

Publications

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets