Kirilenko, Bogdan M.Munegowda, ChetanOsipova, EkaterinaJebb, DavidSharma, ViragBlumer, MoritzMorales, Ariadna E.Ahmed, Alexis-WalidKontopoulos, Dimitrios-GeorgiosHilgers, LeonLindblad-Toh, KerstinKarlsson, Elinor K.Zoonomia ConsortiumHiller, Michael2024-03-252024-03-252023Kirilenko BM, Munegowda C, Osipova E, Jebb D, Sharma V, Blumer M, et al. Integrating gene annotation with orthology inference at scale. Science. 2023 Apr 28;380(6643):eabn3107. DOI: 10.1126/science.abn31070036-8075http://hdl.handle.net/10230/59551Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.application/pdfengThis is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science. 2023 Apr 28;380(6643):eabn3107, DOI: 10.1126/science.abn3107.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1126/science.abn3107Comparative genomicsOrthology inferenceGene annotationGenome alignmentGene lossMachine learninginfo:eu-repo/semantics/openAccess