Liftoff: accurate mapping of gene annotations

Alaina Shumate; Steven L Salzberg

doi:10.1093/bioinformatics/btaa1016

Liftoff: accurate mapping of gene annotations

Bioinformatics. 2021 Jul 19;37(12):1639-1643. doi: 10.1093/bioinformatics/btaa1016.

Authors

Alaina Shumate^{1

2}, Steven L Salzberg^{1

2

3

4}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
² Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA.
³ Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
⁴ Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.

Abstract

Motivation: Improvements in DNA sequencing technology and computational methods have led to a substantial increase in the creation of high-quality genome assemblies of many species. To understand the biology of these genomes, annotation of gene features and other functional elements is essential; however, for most species, only the reference genome is well-annotated.

Results: One strategy to annotate new or improved genome assemblies is to map or 'lift over' the genes from a previously annotated reference genome. Here, we describe Liftoff, a new genome annotation lift-over tool capable of mapping genes between two assemblies of the same or closely related species. Liftoff aligns genes from a reference genome to a target genome and finds the mapping that maximizes sequence identity while preserving the structure of each exon, transcript and gene. We show that Liftoff can accurately map 99.9% of genes between two versions of the human reference genome with an average sequence identity >99.9%. We also show that Liftoff can map genes across species by successfully lifting over 98.3% of human protein-coding genes to a chimpanzee genome assembly with 98.2% sequence identity.

Availability and implementation: Liftoff can be installed via bioconda and PyPI. In addition, the source code for Liftoff is available at https://github.com/agshumate/Liftoff.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

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