Accurate annotation of coding genes and inference of orthology relationships in newly sequenced genomes remain central challenges in modern genomics. We present TOGA2, the next generation of the TOGA (Tool to infer Orthologs from Genome Alignments) framework for scalable reference-based orthologous gene annotation. By introducing an exon-wise gene annotation approach, TOGA2 achieves a 513-fold memory reduction and a 6-fold runtime reduction compared to its predecessor, while improving exon-level annotation precision. We demonstrate that deep learning models for splice-site prediction trained on human data remain effective across diverse vertebrates. Incorporating these predictions further enhances exon boundary annotation precision in TOGA2 and enables detection of evolutionary changes affecting gene exon–intron structure, such as splice-site shifts or intron gains and losses. Multiple false-positive prediction filters and a new gene tree–based reconciliation step further improve ortholog inference, specifically identifying new 1:1 ortholog pairs. We further show how features integrated into TOGA2 improve annotation of immunoglobulin and T-cell receptor gene segments, processed pseudogenes, and functional retrogenes, and how synteny information from inferred orthologs can be leveraged for ancestral chromosome reconstruction and phylogenomic analyses. To demonstrate scalability across multiple genomes, we provide a comprehensive comparative genomics resource for >900 mammal and >680 bird assemblies, including gene annotations, ortholog sets, retrogene candidates, and codon alignments.

Co-authors: Bernhard Bein, Alejandro Gonzales-Irribarren, Leon Hilgers, Amy Stephen, Xueling Yi, Tim Stadager, Luca Hoppach, Lucas Koch, Evgeny Leushkin, Markus Zumpt, Michael Hiller, Niklas Himstedt, Felix Götz, Michele Albertini