AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver.
Bioinformatics
; 39(4)2023 04 03.
Article
en En
| MEDLINE
| ID: mdl-37004197
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Secuencias Reguladoras de Ácidos Nucleicos
/
Estudio de Asociación del Genoma Completo
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos