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.

Fostering accessible online education using Galaxy as an e-learning platform.

The COVID-19 pandemic is shifting teaching to an online setting all over the world. The Galaxy framework facilitates the online learning process and makes it accessible by providing a library of high-quality community-curated training materials, enabling easy access to data and tools, and facilitates sharing achievements and progress between students and instructors. By combining Galaxy with robust communication channels, effective instruction can be designed inclusively, regardless of the students' environments.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update.

Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research. Together, this community develops the Galaxy software framework, integrates analysis tools and visualizations into the framework, runs public servers that make Galaxy available via a web browser, performs and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy, and develops interactive training materials for Galaxy. Over the last two years, all aspects of the Galaxy project have grown: code contributions, tools integrated, users, and training materials. Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis. Extensions to Galaxy's framework include support for federated identity and access management and increased ability to distribute analysis jobs to remote resources. New community resources include large public servers in Europe and Australia, an increasing number of regional and local Galaxy communities, and substantial growth in the Galaxy Training Network.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.

High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.

MetaBase--the wiki-database of biological databases.

Biology is generating more data than ever. As a result, there is an ever increasing number of publicly available databases that analyse, integrate and summarize the available data, providing an invaluable resource for the biological community. As this trend continues, there is a pressing need to organize, catalogue and rate these resources, so that the information they contain can be most effectively exploited. MetaBase (MB) (http://MetaDatabase.Org) is a community-curated database containing more than 2000 commonly used biological databases. Each entry is structured using templates and can carry various user comments and annotations. Entries can be searched, listed, browsed or queried. The database was created using the same MediaWiki technology that powers Wikipedia, allowing users to contribute on many different levels. The initial release of MB was derived from the content of the 2007 Nucleic Acids Research (NAR) Database Issue. Since then, approximately 100 databases have been manually collected from the literature, and users have added information for over 240 databases. MB is synchronized annually with the static Molecular Biology Database Collection provided by NAR. To date, there have been 19 significant contributors to the project; each one is listed as an author here to highlight the community aspect of the project.

Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.

Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq.

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework.

BACKGROUND: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. RESULTS: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. CONCLUSIONS: The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

The Zebrafish Information Network: the zebrafish model organism database.

The Zebrafish Information Network (ZFIN; http://zfin.org) is a web based community resource that implements the curation of zebrafish genetic, genomic and developmental data. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community resources such as meeting announcements and contact information. Recent enhancements to ZFIN include (i) comprehensive curation of gene expression data from the literature and from directly submitted data, (ii) increased support and annotation of the genome sequence, (iii) expanded use of ontologies to support curation and query forms, (iv) curation of morpholino data from the literature, and (v) increased versatility of gene pages, with new data types, links and analysis tools.

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.

Effective teamwork in healthcare: research and reality.

Issues affecting health workplaces range from serious concerns that could affect the immediate physical safety of workers to those that would improve productivity and efficiency, or make an organization a preferred employer. Employers and workers might consider effective teamwork an asset, but for patients it is a prerequisite. This paper reviews the evidence for effective teamwork, primarily that gathered by a research team funded by the Canadian Health Services Research Foundation (CHSRF). We also review the expert opinion provided by a group of 25 researchers and decision makers convened by CHSRF in late 2005 at a forum for discussion about issues related to effective teamwork. Included in the retreat were representatives from professional organizations and occupations as well as areas such as legal liability. Taken together, the research and expert opinion provide a comprehensive overview of the benefits of effective teamwork and the conditions needed for its implementation. In addition, we review policy and management perspectives on the most significant challenges to the implementation of effective teamwork in the Canadian context, and potential opportunities to overcome these obstacles.

The Zebrafish Information Network (ZFIN): the zebrafish model organism database.

The Zebrafish Information Network (ZFIN) is a web based community resource that serves as a centralized location for the curation and integration of zebrafish genetic, genomic and developmental data. ZFIN is publicly accessible at http://zfin.org. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community contact data. Recent enhancements to ZFIN include: (i) an anatomical dictionary that provides a controlled vocabulary of anatomical terms, grouped by developmental stages, that may be used to annotate and query gene expression data; (ii) gene expression data; (iii) expanded support for genome sequence; (iv) gene annotation using the standardized vocabulary of Gene Ontology (GO) terms that can be used to elucidate relationships between gene products in zebrafish and other organisms; and (v) collaborations with other databases (NCBI, Sanger Institute and SWISS-PROT) to provide standardization and interconnections based on shared curation.

Using Galaxy to perform large-scale interactive data analyses.

Innovations in biomedical research technologies continue to provide experimental biologists with novel and increasingly large genomic and high-throughput data resources to be analyzed. As creating and obtaining data has become easier, the key decision faced by many researchers is a practical one: where and how should an analysis be performed? Datasets are large and analysis tool set-up and use is riddled with complexities outside of the scope of core research activities. The authors believe that Galaxy provides a powerful solution that simplifies data acquisition and analysis in an intuitive Web application, granting all researchers access to key informatics tools previously only available to computational specialists working in Unix-based environments. We will demonstrate through a series of biomedically relevant protocols how Galaxy specifically brings together (1) data retrieval from public and private sources, for example, UCSC's Eukaryote and Microbial Genome Browsers, (2) custom tools (wrapped Unix functions, format standardization/conversions, interval operations), and 3rd-party analysis tools.