RESUMEN
Telomere-to-telomere (T2T) assemblies reveal new insights into the structure and function of the previously 'invisible' parts of the genome and allow comparative analyses of complete genomes across entire clades. We present here an open collaborative effort, termed the 'Ruminant T2T Consortium' (RT2T), that aims to generate complete diploid assemblies for numerous species of the Artiodactyla suborder Ruminantia to examine chromosomal evolution in the context of natural selection and domestication of species used as livestock.
Asunto(s)
Rumiantes , Telómero , Telómero/genética , Animales , Rumiantes/genética , Evolución Molecular , Genoma/genética , Selección Genética , Filogenia , DiploidiaRESUMEN
Variant calling across diverse species remains challenging as most bioinformatics tools default to assumptions based on human genomes. DeepVariant (DV) excels without joint genotyping while offering fewer implementation barriers. However, the growing appeal of a "universal" algorithm has magnified the unknown impacts when used with non-human genomes. Here, we use bovine genomes to assess the limits of human-genome-trained models in other species. We introduce the first multi-species DV model that achieves a lower Mendelian Inheritance Error (MIE) rate during single-sample genotyping. Our novel approach, TrioTrain, automates extending DV for species without Genome In A Bottle (GIAB) resources and uses region shuffling to mitigate barriers for SLURM-based clusters. To offset imperfect truth labels for animal genomes, we remove Mendelian discordant variants before training, where models are tuned to genotype the offspring correctly. With TrioTrain, we use cattle, yak, and bison trios to build 30 model iterations across five phases. We observe remarkable performance across phases when testing the GIAB human trios with a mean SNP F1 score >0.990. In HG002, our phase 4 bovine model identifies more variants at a lower MIE rate than DeepTrio. In bovine F1-hybrid genomes, our model substantially reduces inheritance errors with a mean MIE rate of 0.03 percent. Although constrained by imperfect labels, we find that multi-species, trio-based training produces a robust variant calling model. Our research demonstrates that exclusively training with human genomes restricts the application of deep-learning approaches for comparative genomics.