• Gabriela Rustici

The rapid evolution of bioinformatics and data science has created shared challenges for both academia and industry, particularly in developing essential skills among emerging scientists and upskilling the experienced workforce. Despite improved mobility and collaboration, persistent barriers remain, including questions about where and how training is delivered, and what competencies are necessary for success. These hurdles not only impact knowledge exchange but also make transitions into industry daunting for young scientists unaware of workplace expectations and skill requirements. This talk will focus on ways to foster cross-sector cooperation, identify shared opportunities, and strengthen support for young scientists preparing for careers in the industrial sector.

• Allissa Dillman, BioData Sage, United States
• David Burns, ORAU, United States
• Kristi Sadowski, ORAU, United States
• Kelli Bursey, ORAU, United States
• Diane Krause, ORAU, United States
• Jennifer Burnette, ORAU, United States
• LaFrancis Gibson, ORAU, United States

The Common Fund Data Ecosystem (CFDE) enables broad use of Common Fund data to advance scientific discovery. Five Centers integrate data, resources, and knowledge from many Common Fund Programs to empower the research community to pursue novel investigations that were previously not possible.
The CFDE Training Center (TC) serves as a central hub supporting current and potential CFDE users through a comprehensive, learner-centered approach. An in-depth landscape analysis was conducted to assess, identify, and address the training opportunities and needs of the CFDE community. Key findings were broken into training barriers, mentoring challenges, and access issues.
In response to these key findings and needs, the TC provides training in basic and advanced bioinformatics skills crucial for working with CFDE data. Meaningful engagement with CFDE data and tools is fostered with existing users and utilized to attract new users from the bioinformatics, data science, and research communities.
The TC implemented a Learning Management System to provide a singular and seamlessly accessible location for all TC-produced trainings. This includes a foundational seminar series that defines omics-related research areas in the context of available CFDE data, Decoding the Data Ecosystem: A CFDE Training Center Podcast dedicated to unraveling the complexities and exploring the depths of omics research, and a FAIR and open-source Hackathon at the Bio-IT World conference that focused on integrating CFDE tools and data.
By fostering a more knowledgeable and connected research community, the TC significantly accelerates scientific discovery and amplifies the CFDE's contribution to biomedical research.

• Ajay Mishra, EMBL-EBI, United Kingdom
• Flaminia Zane, EMBL-EBI, United Kingdom
• Anna Swan, N/A, United Kingdom
• Prakash Singh Gaur, EMBL-EBI, United Kingdom
• Adam Broadbent, EMBL-EBI, United Kingdom
• Aziz Mithani, EMBL-EBI, United Kingdom
• Cath Brooksbank, EMBL-EBI, United Kingdom

As bioinformatics continues to advance and expand across life sciences research, learners often struggle to navigate complex topics without structured learning paths. This presentation introduces our strategy to address this issue by organising thematic webinar series that focus on bioinformatics applications within specific life science domains, subsequently repurposing these, along with related tutorials, to build structured learning pathways.

Webinars in a series are organised to methodically cover bioinformatic approaches within focused areas such as microbial ecosystems, plant sciences, or fundamental bioinformatics methods, offering participants a coherent learning progression. The live sessions enhance engagement through real-time interaction while serving a dual purpose: they are recorded and made available as stand-alone lectures as well as being repurposed into curated, on-demand training collections.

By combining the recorded sessions with supplementary tutorials and relevant resources, we create comprehensive self-paced learning pathways. This hybrid approach allows learners to review training materials, enhance understanding, and access content according to their own schedule, accommodating various learning preferences and time constraints. Additionally, the resources are openly accessible under a CC-BY-4.0 Creative Commons license, allowing both learners and educators to reuse and adapt the materials to suit their individual needs and perspectives.

In this presentation, we will showcase case studies developed around this structured training approach, share practical insights and lessons learned, and highlight user feedback and engagement outcomes.

• Dusanka Nikolic, Wellcome Connecting Science, United Kingdom
• Fatma Guerfali, Institut Pasteur de Tunis, Tunisia
• Martin Aslett, Wellcome Connecting Science, United Kingdom
• Victoria Offord, Wellcome Sanger Institute, United Kingdom
• Ruth Nanjala, University of Oxford, United Kingdom
• Andries Van Tonder, University of Cambridge, United Kingdom
• Jorge Batista da Rocha, EMBL EBI, United Kingdom
• Katherine Kaldeli, Wellcome Connecting Science, United Kingdom
• Treasa Creavin, Wellcome Connecting Science, United Kingdom

Background: To meet growing demand for training in core bioinformatics skills, we designed and delivered a free, two-part Massive Open Online Course (MOOC)-style course series: Bioinformatics for Biologists (B4B1 and B4B2). These courses provide free, introductory- and advanced-level learning pathways, catering to students and professionals working in genomics research or bioinformatics, and learners aspiring to data science career. Methodology: The first iteration of each course underwent formative evaluation using a mixed methods approach to identify recurring barriers to learning, informing the subsequent runs’ improvements. Results: The main challenges identified included: time constraints, technical difficulties, familiarity with the subject matter, the level of course content, and accessibility issues, all of which could have affected overall engagement with the material. To address these challenges, several improvements were implemented. Technical challenges were mitigated through active facilitation, peer learning support and alternative setup methods, whereas course content was enhanced with downloadable resources, formative quizzes, glossaries and refresher materials, to accommodate varying levels of prior knowledge. Accessibility was improved through transcripts, fall back resources and platform features, ensuring a more inclusive learning experience. The courses were offered at different times of the year and eventually transitioned to a free on-demand format, allowing greater flexibility for learners balancing professional and personal commitments. Conclusion: These enhancements focused on providing an adaptable and supportive environment for core bioinformatics training and resulted in smoother subsequent runs, reaching a global audience, of around 45,000 learners from more than 180 countries, illustrating the potential of MOOCs to bridge bioinformatics skills gaps.

• Nelly Sélem-Mojica, Centro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Mexico
• Tiago Lubiana, Department of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, Brazil
• Toni Hermoso Pulido, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Spain
• Aarón Gallego-Crespo, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
• Tülay Karakulak, Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Switzerland
• Megha Hegde, Kingston University, London, United Kingdom
• Nicolas C Näpflin, Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
• Audra Anjum, Ohio University, United States
• Pradeep Eranti, Université Paris Cité, Inserm, T3S, F-75006 Paris, France
• Dan DeBlasio, Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, United States
• Jorge Noé García-Chávez, Laboratory of Agrogenomics Sciences, Universidad Nacional Autónoma de México, Mexico
• Cynthia Paola Rangel-Chavez, Biochemical Engineering Division, Tecnológico Nacional de México/Instituto Tecnológico Superior de Irapuato, Mexico
• Divanery Rodriguez-Gomez, Biochemical Engineering Division,Tecnológico Nacional de México/Instituto Tecnológico Superior de Irapuato, Mexico
• Varinia López-Ramírez, Biochemical Engineering Division,Tecnológico Nacional de México/Instituto Tecnológico Superior de Irapuato, Mexico
• Juan Vázquez-Martínez, Chemical Engineering Division,Tecnológico Nacional de México/Instituto Tecnológico Superior de Irapuato, Mexico
• Lonnie Welch, Ohio University, United States
• Alastair Kilpatrick, Centre for Regenerative Medicine, The University of Edinburgh, United Kingdom
• Farzana Rahman, Kingston University, London, United Kingdom

Motivation
Wikipedia is a vital open educational resource in computational biology; however, a significant knowledge gap exists between English and Non-English Wikipedias. Reducing this knowledge gap via intensive editing events, or ‘editathons’, would be beneficial in reducing language barriers that disadvantage learners whose native language is not English.
Results
We present a framework to guide educators in organizing editathons for learners to improve and create relevant Wikipedia articles. As a case study, we present the results of an editathon held at the 2024 ISCB Latin America conference, in which ten new articles were created in Spanish Wikipedia. We also present a web tool, ‘compbio-on-wiki’, which identifies relevant English Wikipedia articles missing in other languages. We demonstrate the value of editathons to expand the accessibility and visibility of computational biology content in multiple languages.
Availability and Implementation
Source code for the compbio-on-wiki Toolforge site is available at: https://github.com/lubianat/compbio-on-wiki

• Pavlin G. Poličar, University of Ljubljana, Faculty of Computer and Information Science, Slovenia
• Martin Špendl, University of Ljubljana, Faculty of Computer and Information Science, Slovenia
• Tomaž Curk, University of Ljubljana, Faculty of Computer and Information Science, Slovenia
• Blaž Zupan, University of Ljubljana, Faculty of Computer and Information Science, Slovenia

Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high-quality feedback plays a critical role in improving learning outcomes. However, providing personalized feedback on a large scale in classes with large numbers of students is often impractical due to the significant time and effort required. Recent advances in natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback. These technologies can reduce the workload of course staff while improving student satisfaction and learning outcomes. Their successful implementation, however, requires thorough evaluation and validation in real classrooms.
We present the results of a practical evaluation of LLM-based graders for written assignments in the 2024/25 iteration of the Introduction to Bioinformatics course at the University of Ljubljana. Over the course of the semester, more than 100 students answered 36 text-based questions, most of which were automatically graded using LLMs. In a blind study, students received feedback from both LLMs and human teaching assistants without knowing the source, and later rated the quality of the feedback. We conducted a systematic evaluation of six commercial and open-source LLMs and compared their grading performance with human teaching assistants. Our results show that with well-designed prompts, LLMs can achieve grading accuracy and feedback quality comparable to human graders. Our results also suggest that open-source LLMs perform as well as commercial LLMs, allowing schools to implement their own grading systems while maintaining privacy.

• Aparna Nathan, Harvard Medical School, United States
• Nils Gehlenborg, Harvard Medical School, United States

Large Language Model (LLM) tools (e.g., ChatGPT) are increasingly helping bioinformatics courses foster self-efficacy, personalize learning, and make biological data analysis accessible to students with less coding training. However, students with inadequate understanding of how LLM tools work may use them counterproductively, thus hindering their learning and problem-solving abilities. To address this, we developed and evaluated an interactive LLM Literacy curriculum to help bioinformatics students (1) learn LLM fundamentals, (2) develop best practices for using LLM tools as computational aids, and (3) explore the limitations and ethics of these technologies. At the end of the curriculum, students developed their own guidelines on how to use these tools in bioinformatic analyses. The lessons focus on debugging and statistical design, integrating literature on best practices in these fields with best practices for the use of LLMs as learning aids. The curriculum is tool-agnostic and adaptable to evolving LLM tools. We incorporated the curriculum into a graduate biological data analysis course. Based on a pre-test and post-test, students displayed significant improvements in LLM prompt-writing practices after completing the LLM Literacy curriculum. They were able to solve more coding and statistics problems correctly with fewer LLM interactions due to better-designed LLM prompts. Students also reported increased confidence in their computational skills, both in general and with LLM tools’ assistance. These findings show that LLM Literacy training promotes self-confidence, self-efficacy, and critical evaluation of computing tools. This underscores the importance of LLM literacy training as a necessary part of modern bioinformatics education.

• Inimary Toby-Ogundeji, University of Dallas, Biology Department, United States

As biology becomes increasingly data-driven, the integration of computational and quantitative skills into undergraduate life sciences education is essential for preparing students for research environments and emerging career pathways. Over a five-year period, a longitudinal survey was conducted to evaluate bioinformatics competencies among undergraduate Biology majors, with the aim of integrating computational and quantitative skills into the biology curriculum. The survey assessed students' proficiency, confidence, and awareness of the practical applications of these skills in biological research. Findings revealed a consistent trend: while many students enter with limited experience in computational methods, there is growing interest and recognition of their importance in modern biology. Key areas of deficiency included: coding literacy, data analysis, and algorithmic thinking. In response to these findings, the department introduced a tiered integration of bioinformatics across introductory biology courses and developed a summer program in bioinformatics to provide immersive, hands-on training. These initiatives, along with research-based course modules and collaborative workshops, significantly enhanced student confidence and practical skills application. Students who participated in these immersive experiences reported an increased understanding of the role of computational biology and a stronger ability to solve biological problems using quantitative tools. This educational model is not only responsive to current skill gaps but is also adaptable across diverse institutions and learning environments. These outcomes highlight the value of early, structured exposure to bioinformatics and reinforce the need for ongoing curriculum innovation to better prepare undergraduates for the interdisciplinary demands of modern biological research.

• Amanda Saravia-Butler, Amentum, NASA Ames Research Center, Moffett Field, CA 94035, USA, United States
• Lauren Sanders, BMSIS, United States
• Alexis Torres, NASA Ames, BMSIS, United States
• Crystal Han, NASA Ames, BMSIS, United States
• Samrawit Gebre, Space Biosciences Division, NASA Ames Research Center, United States

The NASA GeneLab project provides open access to space-relevant multi-omics data, hosted on the Open Science Data Repository (OSDR), which can be mined to understand the impacts of spaceflight on biological systems. To engage the scientific community with the Space Biology field and increase the number of scientists who understand and utilize GeneLab data, GeneLab created GeneLab for Colleges and Universities (GL4U). GL4U offers space biology-relevant training in bioinformatics to prospective students, educators, and citizen scientists through various approaches.

Since its inception in 2021, the GL4U program has conducted live annual bootcamps for students and educators where participants complete introductory and omics-specific module sets. The GL4U: Introduction modules include lecture-style overviews of NASA, Space Biology, and OSDR and hands-on training in basic Unix and R commands. The GL4U: Omics-specific module sets include a mix of lectures and hands-on training for a particular type of omics data using GeneLab’s standard processing pipelines. To-date GL4U has hosted 4 live bootcamps, training over 75 students and 12 educators across 8 institutions. Pre- versus post-bootcamp surveys revealed a 115% increase, on average, in both participant understanding of omics data processing and in familiarity with NASA Space Biology resources, showing the overwhelming success of these bootcamps.

GeneLab has recently expanded GL4U into a series of open-access on-demand training modules. The GL4U: Introduction and GL4U: RNAseq modules, featuring recorded lectures and hands-on Jupyter Notebooks exercises, was launched in December 2024. The authors will present an overview of the GL4U on-demand platform and discuss initial user feedback.

• Nadiia Kasianchuk, Computer Science Department, Kyiv School of Economics, Ukraine
• Vladyslav Ostash, Kyiv School of Economics; NGO ‘Genetically Modified Organisation’; NTUU “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine
• Mariia Yakovenko, Kyiv School of Economics; NGO ‘Genetically Modified Organisation’; Taras Shevchenko National University of Kyiv, Ukraine
• Daria Nishchenko, Department of Biochemistry, ESC , Ukraine
• Tetiana Povshyk, Computer Science Department, Kyiv School of Economics, Ukraine
• Kvitoslava-Olha Yarish, Computer Science Department, Kyiv School of Economics, Ukraine
• Pavlo Khomenko, Computer Science Department, Kyiv School of Economics, Ukraine
• Dmytro Hinaliuk, NGO Youth Vitryla; Economic faculty, Taras Shevchenko Kyiv National University, Ukraine
• Serhiy Kornyliuk, Department of International Relations, Donetsk National Technical University, Ukraine
• Oleksandra Konopatska, Computer Science Department, Kyiv School of Economics, Ukraine

The Reuse Science School is a scalable, peer-oriented educational program designed to empower youth from rural and displaced communities in Ukraine through open science and computational biology. In response to systemic educational disruptions caused by war, the program equips learners with data analysis skills using open datasets—an accessible alternative to lab-based research in crisis-affected regions.

The curriculum was co-developed by NGO Youth Vitryla, NGO Genetically Modified Organisation, and the Kyiv School of Economics, with support from GIZ and the EU4Youth Project. It combines soft skills, community-building, and a hands-on Python track (15+ hours) covering fundamentals, data processing (NumPy, Pandas), and visualization (Matplotlib). Students apply these skills in mini-projects analyzing publicly available biological and medical datasets. The top 40 participants are invited to a 3-day bootcamp covering statistics, research ethics, and science communication. During the bootcamp, students also begin developing original group projects, on which they will work over the following three months, culminating in presentations at a closing conference.

343 students registered from across Ukraine, the majority aged 14–19 and with no prior coding experience. Despite wartime power outages and air raid disruptions, each session had 140+ live Zoom attendees, with others following via recordings. Interim self-assessments (n=65) showed a 47% average increase in confidence with data analysis. Over 75% of respondents expressed interest in mentoring, supporting a Training-of-Trainers (ToT) module piloted in Western Ukraine. Participants described the course as “fun,” “accessible,” and “inspiring.” Interests aligned strongly with computational biology–relevant fields such as bioinformatics, life sciences, and data science. The program demonstrates how open science education can be adapted to empower marginalized learners globally.

• Nicola Mulder, University of Cape Town, South Africa
• Siddiqah George, University of Cape Town, South Africa
• Kirsty Lee Garson, University of Cape Town, South Africa
• Tony Li, University of Cape Town, South Africa
• Perceval Maturure, University of Cape Town, South Africa

The recent emergence and re-emergence of infectious diseases in Africa highlight the critical need for robust pathogen genomic surveillance systems across the continent. Effective surveillance depends on comprehensive training and capacity development in pathogen genomics and bioinformatics, as rapid public health responses to disease outbreaks rely on continuously enhancing these skills. Over the past four years, we have delivered hybrid training in pathogen genomic surveillance and bioinformatics to >290 participants from 36 African countries. These initiatives, tailored to diverse personas in national public health institutions, leveraged trainers and facilitators from across the continent to address varying competency levels. We have also developed and implemented resources to support our training initiatives, including a user-friendly helpdesk ticketing system, a robust trainer database, and intuitive websites hosting training materials. These tools work jointly to ensure that training and related resources are widely accessible, while also providing participants with support and engagement opportunities long after receiving training.
To ensure consistency in the training of public health staff in Africa, a standardised pathogen genomics surveillance training curriculum has been developed. The curriculum is designed to serve as a comprehensive resource for trainers, encompassing content that ranges from foundational courses in generic, wet-lab, and bioinformatics topics to advanced pathogen-specific courses that include tailored genomic surveillance workflows. Currently, we are exploring the integration of AI in pathogen genomics curriculum development and training. We have benchmarked AI tools for curriculum design, content generation, skills and knowledge assessment and the implementation of a chatbot for trainee support.

• Alina Frolova, The Institute of Molecular Biology and Genetics of NASU, Kyiv Academic University, NGO Genomics UA, Ukraine
• Serhiy Naumenko, NGO Genomics UA, Canada
• Anna Diamant, Université Côte d'Azur, Institut de Pharmacologie Moléculaire et Cellulaire, NGO Genomics UA, France
• Ihor Arefiev, University of Sherbrooke, NGO Genomics UA, Canada
• Nadiia Kasianchuk, Computer Science Department, Kyiv School of Economics, NGO ‘Genetically Modified Organisation’, Ukraine
• Daryna Yakymenko, Jagiellonian University in Krakow, NGO Genomics UA, Ukraine
• Taras K. Oleksyk, Department of Biological Sciences Oakland University, United States
• Walter Wolfsberger, Department of Biological Sciences, Oakland University, United States
• Viorel Munteanu, Technical University of Moldova, Moldova
• Mangul Serghei, Department of Clinical Pharmacy, University of Southern California, United States
• Valeriia Vasylieva, University of Sherbrooke, NGO Genomics UA, Canada

Competitive education in the areas of bioinformatics, computational biology, and biological data analysis has become a pass to the world of modern biotechnology for any developed country. In Ukraine, many efforts are underway to bridge the gap between the availability of highly talented and motivated students and the scarcity of high-quality educational programs. As active ambassadors of bioinformatics in Ukraine, we report on our educational projects. The non-governmental organization Genomics UA leads yearly courses in RNA-seq data analysis, multi-omics, and spatial transcriptomics data analysis, and maintains a community portal and a discussion forum. Three yearly competitive international science schools - Bioinformatics For Ukraine, Ukrainian Biological Data Science Summer School (UBDS^3), and LifeScienceCourse, are entering their third season, providing intensive, research-oriented training to early career scientists. In parallel, academic institutions are expanding their role, four Ukrainian universities are developing Master’s programs in bioinformatics, two offer Bachelor’s degrees, and five have incorporated bioinformatics courses into broader life science curricula. The growing bioinformatics community in Ukraine received overwhelming support from scientists abroad: many of them contribute their time and expertise to participate in scientific schools, while others provide hiring and educational opportunities. Finally, we describe the challenges faced by the community. While the number of qualified instructors is insufficient and the basic textbooks in Ukrainian are scarce, the students pursuing degrees in bioinformatics or reorienting into the field come from immensely diverse backgrounds, pushed by the limited amount of funding opportunities in laboratory-based research.

• Patricia Carvajal