Stand-alone life science training events and e-learning solutions are among the most sought-after modes of training because they address both point-of-need learning and the limited timeframes available for "upskilling." Yet, finding relevant life sciences training courses and materials is challenging because such resources are not marked up for internet searches in a consistent way. This absence of markup standards to facilitate discovery, re-use, and aggregation of training resources limits their usefulness and knowledge translation potential. Through a joint effort between the Global Organisation for Bioinformatics Learning, Education and Training (GOBLET), the Bioschemas Training community, and the ELIXIR FAIR Training Focus Group, a set of Bioschemas Training profiles has been developed, published, and implemented for life sciences training courses and materials. Here, we describe our development approach and methods, which were based on the Bioschemas model, and present the results for the 3 Bioschemas Training profiles: TrainingMaterial, Course, and CourseInstance. Several implementation challenges were encountered, which we discuss alongside potential solutions. Over time, continued implementation of these Bioschemas Training profiles by training providers will obviate the barriers to skill development, facilitating both the discovery of relevant training events to meet individuals' learning needs, and the discovery and re-use of training and instructional materials.

The completion of the human genome sequence triggered worldwide efforts to unravel the secrets hidden in its deceptively simple code. Numerous bioinformatics projects were undertaken to hunt for genes, predict their protein products, function and post-translational modifications, analyse protein-protein interactions, etc. Many novel analytic and predictive computer programmes fully optimised for manipulating human genome sequence data have been developed, whereas considerably less effort has been invested in exploring the many thousands of other available genomes, from unicellular organisms to plants and non-human animals. Nevertheless, a detailed understanding of these organisms can have a significant impact on human health and well-being.New advances in genome sequencing technologies, bioinformatics, automation, artificial intelligence, etc., enable us to extend the reach of genomic research to all organisms. To this aim gather, develop and implement new bioinformatics solutions (usually in the form of software) is pivotal. A helpful model, often used by the bioinformatics community, is the so-called hackathon. These are events when all stakeholders beyond their disciplines work together creatively to solve a problem. During its runtime, the consortium of the EU-funded project AllBio - Broadening the Bioinformatics Infrastructure to cellular, animal and plant science - conducted many successful hackathons with researchers from different Life Science areas. Based on this experience, in the following, the authors present a step-by-step and standardised workflow explaining how to organise a bioinformatics hackathon to develop software solutions to biological problems.

Author summary Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it’s sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They’re often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

SUMMARY:Dispersed across the Internet is an abundance of disparate, disconnected training information, making it hard for researchers to find training opportunities that are relevant to them. To address this issue, we have developed a new platform-TeSS-which aggregates geographically distributed information and presents it in a central, feature-rich portal. Data are gathered automatically from content providers via bespoke scripts. These resources are cross-linked with related data and tools registries, and made available via a search interface, a data API and through widgets. AVAILABILITY AND IMPLEMENTATION:https://tess.elixir-europe.org.

Sharing, reusing and reproducing data, software and other digital objects forms the cornerstone of open science practices. Such practices are guided by the Findable, Accessible, Interoperable and Reusable (FAIR) principles. Implementing FAIR principles is not always straightforward, especially in the context of Bioinformatics training. If we are to meet worldwide demand for such training, adhering to the FAIR principles is vital not only for data and software, but also for training materials (any digital object used in a training context, such as slide presentations, exercises, datasets). To facilitate this process, and break the principles down into practical steps, the ELIXIR Training Platform community has put together ten simple rules for making training materials FAIR. Above all is the need to plan to share your training materials online from the outset. Amongst the remaining rules is the importance of describing materials properly, assigning unique identifiers, registering them online, defining access criteria, and using interoperable formats. The full set of ten rules is presented here.

This Professional Guide in the Resources for Training Trainers series introduces a structured approach to course design, highlighting the importance of articulating learning outcomes commensurate with the cognitive complexity of the target learning, prior to devising learning experiences and course content. The specific focus here is on face-to-face activities, but the guidance is also relevant for those designing online courses. Specifically, this Guide outlines a series of steps that can help trainers to devise and deploy effective courses. On reading this Guide, and engaging with the reflective exercises, you will be able to: list five key phases of curriculum & course development; explain the primary role of learning outcomes; write learning outcomes for a course; identify the Bloom’s-level accomplishments that different types of learning experience are likely to support; describe the role of learning outcomes in selecting relevant content; distinguish different types of assessment & their role in supporting learner progression towards learning outcomes; and summarise the benefits of course evaluation.

This Practical Guide in the Bringing Bioinformatics into the Classroom series introduces the idea of computers as tools to help understand aspects of biology. In particular, it looks at how DNA sequences can be used to identify specific organisms, why this is important in the food industry, and how this can be used to help detect food fraud. Analyses are run online using sequence data from the 4273pi project website: 4273pi.org. Specifically, this Guide introduces a popular Web-based tool for searching biological sequence databases, and shows how to identify different species based on their specific DNA sequences – their ‘barcodes’. On reading the Guide and completing the exercises, you will be able to: explain what is meant by DNA barcoding; search biological sequence databases using the online program BLAST; judge the reliability of database-search results in terms of their statistical significance; and evaluate the biological implications of search results with reference to food safety.

This Practical Guide in the Bringing Bioinformatics into the Classroom series outlines basic computational approaches used in drug discovery. It highlights how bioinformatics can be harnessed to design drug candidates, to predict their affinity for their targets, their fate inside the body, their toxicity and possible side-effects. Specifically, this Guide introduces bioinformatics tools for designing candidate drug molecules, and for predicting their likely target protein(s) and their drug-like properties. On reading the Guide and completing the exercises, you will be able to: design drug-candidate molecules using the structures of known drugs as templates, and dock them to known protein targets; compare the protein target-binding strengths of drug candidates with those of known drugs; calculate properties of drug candidates and infer whether they need chemical modification to make them more drug-like; predict the protein(s) that a drug candidate is likely to target; create molecular fingerprints for known drugs, and use these to quantify their similarity.

Abstract for poster and talk to presented 25 July 2019 at ISMB/ECCB 2019 as part of the Education COSI: Background: As the life sciences have become more computational and data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of bioinformatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum design and evaluation tool – the Mastery Rubric for Bioinformatics (MR-Bi) - to support the development of specific Knowledge, Skills and Abilities (KSAs) that promote bioinformatics practice and the achievement of competencies. Methods: 12 KSAs were extracted, and stages along a developmental trajectory were identified. The KSAs and their performance level descriptors at each stage were formulated, ultimately yielding the MR-Bi. Results and Conclusions: The MR-Bi prioritizes the development of independence and scientific reasoning. It can be used by practicing scientists at all career stages to direct their (and their team’s) acquisition of new, or to deepen existing, bioinformatics KSAs. It can be used to strengthen teaching and learning and for curriculum building. It can thereby contribute to the cultivation of a next generation of bioinformaticians who can design reproducible and rigorous research, and to critically analyze results from their own, and others’, work.

Abstract for poster and talk to presented 25 July 2019 at ISMB/ECCB 2019 as part of the Education COSI: Background: As the life sciences have become more computational and data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of bioinformatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum design and evaluation tool – the Mastery Rubric for Bioinformatics (MR-Bi) - to support the development of specific Knowledge, Skills and Abilities (KSAs) that promote bioinformatics practice and the achievement of competencies. Methods: 12 KSAs were extracted, and stages along a developmental trajectory were identified. The KSAs and their performance level descriptors at each stage were formulated, ultimately yielding the MR-Bi. Results and Conclusions: The MR-Bi prioritizes the development of independence and scientific reasoning. It can be used by practicing scientists at all career stages to direct their (and their team’s) acquisition of new, or to deepen existing, bioinformatics KSAs. It can be used to strengthen teaching and learning and for curriculum building. It can thereby contribute to the cultivation of a next generation of bioinformaticians who can design reproducible and rigorous research, and to critically analyze results from their own, and others’, work.

The increasing richness and diversity of biomedical data types creates major organizational and analytical impediments to rapid translational impact in the context of training and education. As biomedical data-sets increase in size, variety and complexity, they challenge conventional methods for sharing, managing and analyzing those data. In May 2017, we convened a two-day meeting between the BD2K Training Coordinating Center (TCC), ELIXIR Training/TeSS, GOBLET, H3ABioNet, EMBL-ABR, bioCADDIE and the CSIRO, in Huntington Beach, California, to compare and contrast our respective activities, and how these might be leveraged for wider impact on an international scale. Discussions focused on the role of i) training for biomedical data science; ii) the need to promote core competencies, and the ii) development of career paths. These led to specific conversations about i) the values of standardizing and sharing data science training resources; ii) challenges in encouraging adoption of training material standards; iii) strategies and best practices for the personalization and customization of learning experiences; iv) processes of identifying stakeholders and determining how they should be accommodated; and v) discussions of joint partnerships to lead the world on data science training in ways that benefit all stakeholders. Generally, international cooperation was viewed as essential for accommodating the widest possible participation in the modern bioscience enterprise, providing skills in a truly “FAIR” manner, addressing the importance of data science understanding worldwide. Several recommendations for the exchange of educational frameworks are made, along with potential sources for support, and plans for further cooperative efforts are presented.

Bioinformatics is now intrinsic to life science research, but the past decade has witnessed a continuing deficiency in this essential expertise. Basic data stewardship is still taught relatively rarely in life science education programmes, creating a chasm between theory and practice, and fuelling demand for bioinformatics training across all educational levels and career roles. Concerned by this, surveys have been conducted in recent years to monitor bioinformatics and computational training needs worldwide. This article briefly reviews the principal findings of a number of these studies. We see that there is still a strong appetite for short courses to improve expertise and confidence in data analysis and interpretation; strikingly, however, the most urgent appeal is for bioinformatics to be woven into the fabric of life science degree programmes. Satisfying the relentless training needs of current and future generations of life scientists will require a concerted response from stakeholders across the globe, who need to deliver sustainable solutions capable of both transforming education curricula and cultivating a new cadre of trainer scientists.

During the course of our personal and professional lives, we spend a significant amount of time communicating with others.In fact, communication is one of the most important, but possibly also one of the hardest, things we do, having the power to bring individuals and communities together or create divisions.Getting it right is therefore crucial.Modern technologies have had a significant impact on the ways in which we are now able to communicate, allowing us to share our thoughts with colleagues, family, or friends at the click of a button.But communicating more quickly does not always result in better communication-the technologies we use often divorce us from the visual clues that are so crucial to understanding each other's true meaning and make it easy to misinterpret each other's real intentions.For this reason, our interactions can sometimes be unexpectedly difficult or can go unaccountably wrong.Given that communication is vital to the health and productivity of relationships, how can we best make our interactions work, and how can we resolve situations when they arise?The following are 10 simple rules based on our experience that we hope will help.Many of these rules can apply to the kinds of communication we may have with colleagues, family, or friends.However, we focus this article on the professional environment: we begin with suggestions to help avoid disagreements or to help stop them turning into serious conflicts; we then reflect on steps that might help to resolve situations that have become confrontational.Most interactions with colleagues are cordial and are working towards a common goal.Sometimes, however, because of differing views, misinterpretation of something said, or just because you're having a bad day, communications can go awry and become heated; from this point, without resolution, awkward situations can quickly escalate.Practicing effective communication skills before a confrontation arises, or during a confrontation, is the topic of this article.For more general ideas about engaging in successful collaborations, see [1].To delve further into the area of conflict management in the work environment, see [2,3].To keep this contribution manageable, we have confined ourselves to peer-to-peer communication and not considered a larger ecosystem of interactions in which conflict occurs.We feel that is a separate contribution that should be written. Rule 1: Always treat people with equality and respectWhether interacting with your peers or not, treat people courteously.Don't prejudge individuals based on their rank or perceived academic abilities, or worse, their gender, race, or sexual orientation.Be polite, and treat everyone equally and fairly.

The InterPro database (http://www.ebi.ac.uk/interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmatic interface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.

As the life sciences have become more data intensive, the pressure to incorporate the requisite training into life-science education and training programs has increased. To facilitate curriculum development, various sets of (bio)informatics competencies have been articulated; however, these have proved difficult to implement in practice. Addressing this issue, we have created a curriculum-design and -evaluation tool to support the development of specific Knowledge, Skills and Abilities (KSAs) that reflect the scientific method and promote both bioinformatics practice and the achievement of competencies. Twelve KSAs were extracted via formal analysis, and stages along a developmental trajectory, from uninitiated student to independent practitioner, were identified. Demonstration of each KSA by a performer at each stage was initially described (Performance Level Descriptors, PLDs), evaluated, and revised at an international workshop. This work was subsequently extended and further refined to yield the Mastery Rubric for Bioinformatics (MR-Bi). The MR-Bi was validated by demonstrating alignment between the KSAs and competencies, and its consistency with principles of adult learning. The MR-Bi tool provides a formal framework to support curriculum building, training, and self-directed learning. It prioritizes the development of independence and scientific reasoning, and is structured to allow individuals (regardless of career stage, disciplinary background, or skill level) to locate themselves within the framework. The KSAs and their PLDs promote scientific problem formulation and problem solving, lending the MR-Bi durability and flexibility. With its explicit developmental trajectory, the tool can be used by developing or practicing scientists to direct their (and their team's) acquisition of new, or to deepen existing, bioinformatics KSAs. The MR-Bi is a tool that can contribute to the cultivation of a next generation of bioinformaticians who are able to design reproducible and rigorous research, and to critically analyze results from their own, and others', work.

Demand for training life scientists in bioinformatics methods, tools and resources and computational approaches is urgent and growing. To meet this demand, new trainers must be prepared with effective teaching practices for delivering short hands-on training sessions—a specific type of education that is not typically part of professional preparation of life scientists in many countries. A new Train-the-Trainer (TtT) programme was created by adapting existing models, using input from experienced trainers and experts in bioinformatics, and from educational and cognitive sciences. This programme was piloted across Europe from May 2016 to January 2017. Preparation included drafting the training materials, organizing sessions to pilot them and studying this paradigm for its potential to support the development and delivery of future bioinformatics training by participants. Seven pilot TtT sessions were carried out, and this manuscript describes the results of the pilot year. Lessons learned include (i) support is required for logistics, so that new instructors can focus on their teaching; (ii) institutions must provide incentives to include training opportunities for those who want/need to become new or better instructors; (iii) formal evaluation of the TtT materials is now a priority; (iv) a strategy is needed to recruit, train and certify new instructor trainers (faculty); and (v) future evaluations must assess utility. Additionally, defining a flexible but rigorous and reliable process of TtT 'certification' may incentivize participants and will be considered in future.

This Critical Guide in the Introduction to Bioinformatics series provides a brief outline of the Protein Data Bank – the PDB – the world’s primary repository of biological macromolecular structures. The rationale for creating the resource and the kinds of information it provides are discussed, and issues relating to its evolution and growth are explored. Specifically, this Guide introduces the principal features of the PDB, the nature (and quality) of its contents and how these may be interrogated. On reading this Guide, users will be able to: explain some of the ways in which knowledge of protein structures is useful; identify the constituent databases of the wwPDB; explain key features of the RCSB PDB in terms of its data distribution, growth and redundancy statistics; search the PDB using simple and advanced keywords and full sequences, and analyse differences between them; and explain various structural quality criteria, and infer the quality of individual PDB entries.

This Critical Guide briefly presents the need for biological databases and for a standard format for storing and organising biological data. Web-based interfaces have made databases more user-friendly, but knowledge of the underlying file format offers a deeper understanding of how to navigate and mine the information they contain, so that humans and machines can get the most out of them. This Guide explores the file format that underpins one of today’s most popular protein sequence databases – UniProtKB. Specifically, this Guide introduces the concept of database ‘flat-files’, and examines features of the UniProtKB flat-file format. On reading this Guide, users will be able to: identify key fields within UniProtKB/Swiss-Prot and /TrEMBL flat-files; explain what these fields mean, what information they contain and what the information is used for; analyse the information in different fields and infer structural and functional features of a sequence; examine and investigate the provenance of annotations; and compare annotations at different time-points and evaluate the likely impact of annotation changes.

Open Science describes the ongoing transitions in the way research is performed, i.e. researchers collaborate, knowledge is shared, and science is organized. It is driven by digital technologies and by the enormous growth of data, globalization, enlargement of the scientific community and the need to address societal challenges [23]. It has now widely been recognized that making research results more accessible to all societal actors contributes to better and more efficient science, as well as to innovation in the public and private sectors [1, 17]. However, the reuse of research results can only be achieved reliably and efficiently, if these data are valorized in a specific manner. Data are to be generated, formatted and stored according to Standard Operating Procedures (SOPs) and according to sophisticated Data Management Plans [23]. Hence, to generate accurate and reproducible data sets, to allow interlaboratory comparisons as well as further and future use of research data it is mandatory to work in line with good laboratory practices and well-defined and validated methodologies. Within this article, members of the Cost Action CHARME [10] will discuss aspects of quality management and standardization in context with Open Access (OA) efforts. We will address the question: Are Standardization and Quality Management measures in life-science research crucially needed or introduce further unwanted means of regulation?

ELIXIR’s training portal, TeSS, allows users to browse and discover life-science training resources aggregated automatically from ELIXIR Nodes and 3rd-party content providers. The resources - including training events, courses, materials and workflows - are available at https://tess.elixir-uk.org.