Dong C, Shen S, Keleş S. AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver.
Bioinformatics 2023;
39:btad149. [PMID:
37004197 PMCID:
PMC10085516 DOI:
10.1093/bioinformatics/btad149]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
MOTIVATION
Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues.
RESULTS
We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.
AVAILABILITY AND IMPLEMENTATION
The R package and the data for AdaLiftOver is available from https://github.com/keleslab/AdaLiftOver.
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