Reconstructing the Phylogeny of Corynebacteriales while Accounting for Horizontal Gene Transfer.

Horizontal gene transfer is a common mechanism in Bacteria that has contributed to the genomic content of existing organisms. Traditional methods for estimating bacterial phylogeny, however, assume only vertical inheritance in the evolution of homologous genes, which may result in errors in the estimated phylogenies. We present a new method for estimating bacterial phylogeny that accounts for the presence of genes acquired by horizontal gene transfer between genomes. The method identifies and corrects putative transferred genes in gene families, before applying a gene tree-based summary method to estimate bacterial species trees. The method was applied to estimate the phylogeny of the order Corynebacteriales, which is the largest clade in the phylum Actinobacteria. We report a collection of 14 phylogenetic trees on 360 Corynebacteriales genomes. All estimated trees display each genus as a monophyletic clade. The trees also display several relationships proposed by past studies, as well as new relevant relationships between and within the main genera of Corynebacteriales: Corynebacterium, Mycobacterium, Nocardia, Rhodococcus, and Gordonia. An implementation of the method in Python is available on GitHub at https://github.com/UdeS-CoBIUS/EXECT (last accessed April 2, 2020).

Global network of computational biology communities: ISCB's Regional Student Groups breaking barriers.

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: "Nurture the new generation of computational biologists".