- Published: Friday, 26 August 2022 07:27
Orthologs are evolutionarily related genes in different species whose separation from the same ancestral sequence was initiated by a speciation event. The identification of orthologs is fundamental for biological sequence analysis both in an evolutionary and functional context. For the bioinformatics community, the quest for orthologs poses a rich source of challenges ranging from the development of scalable algorithms for ortholog classification to the hosting of dedicated data repositories to disseminate orthology assignments into a global community. As such, the development and improvement of orthology inference tools has to keep up with the evolution in the underlying data with respect to both quantity and quality. Some of the currently most pressing challenges are the following. The Darwin Tree of Life project alone will sequence over 70,000 eukaryotic genomes over the next several years. This data is invaluable for biodiversity genomics, but renders the development of orthology inference methods that scale to this wealth of data an urgent task. In the same way as the taxonomic breadth of sequenced organisms increases, deep orthology relationships between genes in distantly related species or between rapidly evolving sequences move into focus. Here, novel algorithms that incorporate information provided by the increasing availability of accurate protein structure predictions may help. The functional diversity and relevance of non-coding RNAs in the regulatory network of a cell, which emerged in the past decade, requires dedicated tools whose standards meet those achieved for protein coding genes. Eventually, the focus beyond cellular organisms is necessary given the spread of pathogenic viruses worldwide. Viral genes diverge quickly and often their evolutionary history is reticulated due to frequent recombination and horizontal gene transfer. Overlapping genes and polyprotein further complicate the identification of orthologous groups. This workshop aims at highlighting these novel challenges in the quest for orthologs for which innovative bioinformatics solutions are urgently needed. The workshop goal is to communicate these topics to, and to discuss possible approaches with a diverse bioinformatics community. This will significantly broaden the methodological basis from which future approaches of orthology inference can be built on and fosters the formation of new collaborations that will significantly advance the field of orthology inference.