Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

Vancomycin-resistant enterococci (VRE) are one of the leading causes of nosocomial infections in health care facilities around the globe. In particular, infections caused by vancomycin-resistant Enterococcus faecium are becoming increasingly common. Comparative and functional genomic studies of E. faecium isolates have so far been limited owing to the lack of a fully assembled E. faecium genome sequence. Here we address this issue and report the complete 3.0-Mb genome sequence of the multilocus sequence type 17 vancomycin-resistant Enterococcus faecium strain Aus0004, isolated from the bloodstream of a patient in Melbourne, Australia, in 1998. The genome comprises a 2.9-Mb circular chromosome and three circular plasmids. The chromosome harbors putative E. faecium virulence factors such as enterococcal surface protein, hemolysin, and collagen-binding adhesin. Aus0004 has a very large accessory genome (38%) that includes three prophage and two genomic islands absent among 22 other E. faecium genomes. One of the prophage was present as inverted 50-kb repeats that appear to have facilitated a 683-kb chromosomal inversion across the replication terminus, resulting in a striking replichore imbalance. Other distinctive features include 76 insertion sequence elements and a single chromosomal copy of Tn 1549 containing the vanB vancomycin resistance element. A complete E. faecium genome will be a useful resource to assist our understanding of this emerging nosocomial pathogen.

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.