Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Expanding the Galaxy's reference data.

Summary: Properly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie's remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy. Availability and implementation: The ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

Training Infrastructure as a Service.

BACKGROUND: Hands-on training, whether in bioinformatics or other domains, often requires significant technical resources and knowledge to set up and run. Instructors must have access to powerful compute infrastructure that can support resource-intensive jobs running efficiently. Often this is achieved using a private server where there is no contention for the queue. However, this places a significant prerequisite knowledge or labor barrier for instructors, who must spend time coordinating deployment and management of compute resources. Furthermore, with the increase of virtual and hybrid teaching, where learners are located in separate physical locations, it is difficult to track student progress as efficiently as during in-person courses. FINDINGS: Originally developed by Galaxy Europe and the Gallantries project, together with the Galaxy community, we have created Training Infrastructure-as-a-Service (TIaaS), aimed at providing user-friendly training infrastructure to the global training community. TIaaS provides dedicated training resources for Galaxy-based courses and events. Event organizers register their course, after which trainees are transparently placed in a private queue on the compute infrastructure, which ensures jobs complete quickly, even when the main queue is experiencing high wait times. A built-in dashboard allows instructors to monitor student progress. CONCLUSIONS: TIaaS provides a significant improvement for instructors and learners, as well as infrastructure administrators. The instructor dashboard makes remote events not only possible but also easy. Students experience continuity of learning, as all training happens on Galaxy, which they can continue to use after the event. In the past 60 months, 504 training events with over 24,000 learners have used this infrastructure for Galaxy training.

Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring.

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data.Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org .

No more business as usual: Agile and effective responses to emerging pathogen threats require open data and open analytics.

The current state of much of the Wuhan pneumonia virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies and requires unimpeded access to data, analysis tools, and computational infrastructure. Here, we show that community efforts in developing open analytical software tools over the past 10 years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all SARS-CoV-2 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.

Establishing a distributed national research infrastructure providing bioinformatics support to life science researchers in Australia.

EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australia's capacity in bioinformatics and data sciences; (2) contribute to the development of training in bioinformatics skills; (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas-Tools, Data, Standards, Platforms, Compute and Training-are described in this article.

Community-Driven Data Analysis Training for Biology.

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.

Emergence of High-Level Colistin Resistance in an Acinetobacter baumannii Clinical Isolate Mediated by Inactivation of the Global Regulator H-NS.

Colistin is a crucial last-line drug used for the treatment of life-threatening infections caused by multidrug-resistant strains of the Gram-negative bacterium Acinetobacter baumannii However, colistin-resistant A. baumannii isolates can still be isolated following failed colistin therapy. Resistance is most often mediated by the addition of phosphoethanolamine (pEtN) to lipid A by PmrC, following missense mutations in the pmrCAB operon encoding PmrC and the two-component signal transduction system PmrA/PmrB. We recovered a pair of A. baumannii isolates from a single patient before (6009-1) and after (6009-2) failed colistin treatment. These strains displayed low and very high levels of colistin resistance (MICs, 8 to 16 µg/ml and 128 µg/ml), respectively. To understand how increased colistin resistance arose, we sequenced the genome of each isolate, which revealed that 6009-2 had an extra copy of the insertion sequence element ISAba125 within a gene encoding an H-NS family transcriptional regulator. To confirm the role of H-NS in colistin resistance, we generated an hns deletion mutant in 6009-1 and showed that colistin resistance increased upon the deletion of hns We also provided 6009-2 with an intact copy of hns and showed that the strain was no longer resistant to high concentrations of colistin. Transcriptomic analysis of the clinical isolates identified more than 150 genes as being differentially expressed in the colistin-resistant hns mutant 6009-2. Importantly, the expression of eptA, encoding a second lipid A-specific pEtN transferase but not pmrC, was increased in the hns mutant. This is the first time an H-NS family transcriptional regulator has been associated with a pEtN transferase and colistin resistance.

Four simple recommendations to encourage best practices in research software.

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.

Porphyromonas gingivalis Uses Specific Domain Rearrangements and Allelic Exchange to Generate Diversity in Surface Virulence Factors.

Porphyromonas gingivalis is a keystone pathogen of chronic periodontitis. The virulence of P. gingivalis is reported to be strain related and there are currently a number of strain typing schemes based on variation in capsular polysaccharide, the major and minor fimbriae and adhesin domains of Lys-gingipain (Kgp), amongst other surface proteins. P. gingivalis can exchange chromosomal DNA between strains by natural competence and conjugation. The aim of this study was to determine the genetic variability of P. gingivalis strains sourced from international locations over a 25-year period and to determine if variability in surface virulence factors has a phylogenetic basis. Whole genome sequencing was performed on 13 strains and comparison made to 10 previously sequenced strains. A single nucleotide polymorphism-based phylogenetic analysis demonstrated a shallow tri-lobed phylogeny. There was a high level of reticulation in the phylogenetic network, demonstrating extensive horizontal gene transfer between the strains. Two highly conserved variants of the catalytic domain of the major virulence factor the Kgp proteinase (KgpcatI and KgpcatII) were found. There were three variants of the fourth Kgp C-terminal cleaved adhesin domain. Specific variants of the cell surface proteins FimA, FimCDE, MfaI, RagAB, Tpr, and PrtT were also identified. The occurrence of all these variants in the P. gingivalis strains formed a mosaic that was not related to the SNP-based phylogeny. In conclusion P. gingivalis uses domain rearrangements and genetic exchange to generate diversity in specific surface virulence factors.

Best practice data life cycle approaches for the life sciences.

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

BACKGROUND: Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. RESULTS: We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. CONCLUSIONS: This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

Comparative Genomic Analysis of Asian Haemorrhagic Septicaemia-Associated Strains of Pasteurella multocida Identifies More than 90 Haemorrhagic Septicaemia-Specific Genes.

Pasteurella multocida is the primary causative agent of a range of economically important diseases in animals, including haemorrhagic septicaemia (HS), a rapidly fatal disease of ungulates. There is limited information available on the diversity of P. multocida strains that cause HS. Therefore, we determined draft genome sequences of ten disease-causing isolates and two vaccine strains and compared these genomes using a range of bioinformatic analyses. The draft genomes of the 12 HS strains were between 2,298,035 and 2,410,300 bp in length. Comparison of these genomes with the North American HS strain, M1404, and other available P. multocida genomes (Pm70, 3480, 36950 and HN06) identified a core set of 1,824 genes. A set of 96 genes was present in all HS isolates and vaccine strains examined in this study, but absent from Pm70, 3480, 36950 and HN06. Moreover, 59 genes were shared only by the Asian B:2 strains. In two Pakistani isolates, genes with high similarity to genes in the integrative and conjugative element, ICEPmu1 from strain 36950 were identified along with a range of other antimicrobial resistance genes. Phylogenetic analysis indicated that the HS strains formed clades based on their country of isolation. Future analysis of the 96 genes unique to the HS isolates will aid the identification of HS-specific virulence attributes and facilitate the development of disease-specific diagnostic tests.

Using the local immune response from the natural buffalo host to generate an antibody fragment library that binds the early larval stages of Schistosoma japonicum.

Antibodies isolated from the local draining inguinal lymph node of field exposed-water buffaloes following challenge with Schistosoma japonicum cercariae showed high reactivity towards S. japonicum antigen preparations and bound specifically to formaldehyde-fixed S. japonicum schistosomules. Using this specific local immune response we produced a series of single-chain antibody Fv domain libraries from the same lymph nodes. Removal of phage that cross reacted with epitopes on adult parasites yielded a single-chain antibody Fv domain-phage library that specifically bound to whole formaldehyde-fixed and live S. japonicum schistosomules. DNA sequencing indicated clear enrichment of the single-chain antibody Fv domain library for buffalo B-cell complementarity determining regions post-selection for schistosomule binding. This study also revealed that long heavy chain complementarity determining regions appear to be an important factor when selecting for antibody binding fragments against schistosomule proteins. The selected single-chain antibody Fv domain-phage were used to probe a schistosome-specific protein microarray, which resulted in the recognition of many proteins expressed across all schistosome life-cycle stages. Following absorption to adult worms, the single-chain antibody Fv domain-phage library showed significantly reduced binding to most proteins, whilst two proteins (NCBI GenBank accession numbers AY915878 and AY815196) showed increased binding. We have thus developed a unique set of host derived single-chain antibody Fv domains comprising buffalo B-cell variable regions that specifically bind to early S. japonicum life-stages.

Large tandem chromosome expansions facilitate niche adaptation during persistent infection with drug-resistant Staphylococcus aureus.

We used genomics to study the evolution of meticillin-resistant Staphylococcus aureus (MRSA) during a complex, protracted clinical infection. Preparing closed MRSA genomes from days 0 and 115 allowed us to precisely reconstruct all genetic changes that occurred. Twenty-three MRSA blood cultures were also obtained during treatment, yielding 44 colony morphotypes that varied in size, haemolysis and antibiotic susceptibility. A subset of 15 isolates was sequenced and shown to harbour a total of 37 sequence polymorphisms. Eighty per cent of all mutations occurred from day 45 onwards, which coincided with the appearance of discrete chromosome expansions, and concluded in the day 115 isolate with a 98 kb tandem DNA duplication. In all heterogeneous vancomycin-intermediate Staphylococcus aureus isolates, the chromosomal amplification spanned at least a 20 kb region that notably included mprF, a gene involved in resistance to antimicrobial peptides, and parC, an essential DNA replication gene with an unusual V463 codon insertion. Restoration of the chromosome after serial passage under non-selective growth was accompanied by increased susceptibility to antimicrobial peptide killing and reduced vancomycin resistance, two signature phenotypes that help explain the clinical persistence of this strain. Elevated expression of the V463 parC was deleterious to the cell and reduced colony size, but did not alter ciprofloxacin susceptibility. In this study, we identified large DNA expansions as a clinically relevant mechanism of S. aureus resistance and persistence, demonstrating the extent to which bacterial chromosomes remodel in the face of antibiotic and host immune pressures.

A Genomic Island in Salmonella enterica ssp. salamae provides new insights on the genealogy of the locus of enterocyte effacement.

The genomic island encoding the locus of enterocyte effacement (LEE) is an important virulence factor of the human pathogenic Escherichia coli. LEE typically encodes a type III secretion system (T3SS) and secreted effectors capable of forming attaching and effacing lesions. Although prominent in the pathogenic E. coli such as serotype O157:H7, LEE has also been detected in Citrobacter rodentium, E. albertii, and although not confirmed, it is likely to also be in Shigella boydii. Previous phylogenetic analysis of LEE indicated the genomic island was evolving through stepwise acquisition of various components. This study describes a new LEE region from two strains of Salmonella enterica subspecies salamae serovar Sofia along with a phylogenetic analysis of LEE that provides new insights into the likely evolution of this genomic island. The Salmonella LEE contains 36 of the 41 genes typically observed in LEE within a genomic island of 49, 371 bp that encodes a total of 54 genes. A phylogenetic analysis was performed on the entire T3SS and four T3SS genes (escF, escJ, escN, and escV) to elucidate the genealogy of LEE. Phylogenetic analysis inferred that the previously known LEE islands are members of a single lineage distinct from the new Salmonella LEE lineage. The previously known lineage of LEE diverged between islands found in Citrobacter and those in Escherichia and Shigella. Although recombination and horizontal gene transfer are important factors in the genealogy of most genomic islands, the phylogeny of the T3SS of LEE can be interpreted with a bifurcating tree. It seems likely that the LEE island entered the Enterobacteriaceae through horizontal gene transfer as a single unit, rather than as separate subsections, which was then subjected to the forces of both mutational change and recombination.

Comparative analysis of the first complete Enterococcus faecium genome.

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 Tn1549 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.

Population genomics and phylogeography of an Australian dairy factory derived lytic bacteriophage.

In this study, we present the full genomic sequences and evolutionary analyses of a serially sampled population of 28 Lactococcus lactis-infecting phage belonging to the 936-like group in Australia. Genome sizes were consistent with previously available genomes ranging in length from 30.9 to 32.1 Kbp and consisted of 55-65 open reading frames. We analyzed their genetic diversity and found that regions of high diversity are correlated with high recombination rate regions (P value = 0.01). Phylogenetic inference showed two major clades that correlate well with known host range. Using the extended Bayesian Skyline model, we found that population size has remained mostly constant through time. Moreover, the dispersion pattern of these genomes is in agreement with human-driven dispersion as suggested by phylogeographic analysis. In addition, selection analysis found evidence of positive selection on codon positions of the Receptor Binding Protein (RBP). Likewise, positively selected sites in the RBP were located within the neck and head region in the crystal structure, both known determinants of host range. Our study demonstrates the utility of phylogenetic methods applied to whole genome data collected from populations of phage for providing insights into applied microbiology.