AdaLiftOver: High-resolution identification of orthologous regulatory elements with adaptive liftOver

Abstract

Elucidating orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from the genome-wide association studies. 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. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying 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 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. The R package AdaLiftOver is available from https://github.com/ThomasDCY/AdaLiftOver.