Annotating 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.

Disturbances in brain copper result in rare and severe neurological disorders and may play a role in the pathogenesis and progression of multiple neur

Inferring historical population sizes is key to identifying drivers of ecological and evolutionary change and crucial to predicting the future of species on our rapidly changing planet. The pairwise sequentially Markovian coalescent (PSMC) method provided a revolutionary framework to reconstruct species' demographic histories over millions of years based on the genome sequence of a single individual. Here, we detected and solved a common artifact in PSMC and related methods: recent population peaks followed by population collapses. Combining real and simulated genomes, we show that these peaks do not represent true population dynamics. Instead, ill-set default parameters cause false peaks in our own and published data, which can be avoided by adjusting parameter settings. Furthermore, we show that certain changes in population structure can cause similar patterns. Newer methods, like Beta-PSMC, perform better but do not always avoid this artifact. Our results suggest testing multiple parameters that split the first time window before interpreting recent population peaks followed by collapses and call for the development of robust methods.

Zoonoses are infectious diseases transmitted from animals to humans. Bats have been suggested to harbour more zoonotic viruses than any other mammalian order¹. Infections in bats are largely asymptomatic^2,3, indicating limited tissue-damaging inflammation and immunopathology. To investigate the genomic basis of disease resistance, the Bat1K project generated reference-quality genomes of ten bat species, including potential viral reservoirs. Here we describe a systematic analysis covering 115 mammalian genomes that revealed that signatures of selection in immune genes are more prevalent in bats than in other mammalian orders. We found an excess of immune gene adaptations in the ancestral chiropteran branch and in many descending bat lineages, highlighting viral entry and detection factors, and regulators of antiviral and inflammatory responses. ISG15, which is an antiviral gene contributing to hyperinflammation during COVID-19 (refs. ^4,5), exhibits key residue changes in rhinolophid and hipposiderid bats. Cellular infection experiments show species-specific antiviral differences and an essential role of protein conjugation in antiviral function of bat ISG15, separate from its role in secretion and inflammation in humans. Furthermore, in contrast to humans, ISG15 in most rhinolophid and hipposiderid bats has strong anti-SARS-CoV-2 activity. Our work reveals molecular mechanisms that contribute to viral tolerance and disease resistance in bats.

The medaka is a fish that has served as a model organism for over a century, yet there is still much to learn about its life in the wild. Here we summarize the current knowledge, highlight recent progress and outline remaining gaps in our understanding of the natural history of medaka. It has also become clear over time that rather than being a single species, medaka comprises an entire species complex, so disentangling the species boundaries is an important goal for future research. Moreover, medaka and other ricefishes exhibit striking functional diversity, little of which has been investigated to date. As such, there are opportunities to use the resources developed for medaka to study other ricefishes, and to learn more about medaka itself in an evolutionary context.

Summary: Inferring historical population sizes is key to identify drivers of ecological and evolutionary change, and crucial to predict the future of species on our rapidly changing planet. The pairwise sequentially Markovian coalescent (PSMC) method provided a revolutionary framework to reconstruct species’ demographic histories over millions of years based on the genome sequence of a single individual ¹ . Here, we detected and solved a common artifact in PSMC and related methods: recent population peaks followed by population collapses. Combining real and simulated genomes, we show that these peaks do not represent true population dynamics. Instead, ill-set default parameters cause false peaks in our own and published data, which can be avoided by adjusted parameter settings. Furthermore, we show that certain population structure changes can cause similar patterns. Newer methods like Beta-PSMC perform better, but do not always avoid this artifact. Our results suggest testing multiple parameters before interpreting recent population peaks followed by collapses, and call for the development of robust methods.