Bioinformatics Education Research Articles

Retracted: Status of Bioinformatics Education in South Asia: Past and Present.

[This retracts the article DOI: 10.1155/2021/5568262.].

Conceptualizing bioinformatics education in STEM literacy development for pre-service biology teachers

This study aimed to create a basic plan for adding bioinformatics education into STEM (Science, Technology, Engineering, and Mathematics) learning, particularly for future biology teachers. By reviewing studies from 2019 to 2023 in the ERIC Database with the key terms "Bioinformatics Education" and "STEM Literacy," the research highlighted important areas in this subject. It organized the findings into four main themes related to bioinformatics education: levels of education, challenges faced, teaching methods, and methods for research or evaluation. Additionally, it identified three themes in STEM literacy: educational levels, challenges, and definitions or ways to measure it. The review pointed out a critical need for teaching materials, tools, and strategies in bioinformatics for future biology teachers at the college level. It also found a lack of research on STEM literacy at this level, with just three articles addressing it. These articles examined STEM literacy through skills such as general knowledge, career awareness, and communication skills. The suggested basic plan promotes combining bioinformatics education with real-world biological issues, career preparation, and addressing educational shortcomings. It recommends a bioinformatics module with specific curriculum content, tools, and teaching methods to improve STEM literacy, including knowledge, attitudes, and skills in STEM. This plan could guide future research in classrooms to improve STEM literacy among future biology teachers through bioinformatics education.

Revisiting barriers to implementation of bioinformatics into life sciences education

IntroductionBioinformatics is an interdisciplinary field at the intersection of computational and biological sciences that focuses on the analysis and interpretation of large biological data sets. Although recognized as essential in the life sciences, bioinformatics is not commonly integrated in undergraduate life science education programs. Based on a national survey in 2016, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) published a community-sourced set of core competencies in bioinformatics education. The survey also identified barriers that prevent incorporation of these competencies into the curriculum. In the current study, the NIBLSE group reports the findings of a new survey to 509 life science educators across the US in 2022 to identify current barriers of bioinformatics integration and to determine if the landscape of bioinformatics education has changed since the 2016 survey.ResultsSimilar to previous results, a majority of respondents who currently teach bioinformatics or plan to teach bioinformatics report barriers. The top two barriers reported are students lacking prerequisite skills/knowledge and instructors lacking time to restructure course content. As in 2016, women reported experiencing barriers to bioinformatics teaching significantly more often than men; faculty from underrepresented minority backgrounds reported barriers more often than non-URM faculty; and educators at minority-serving institutions (MSIs) reported barriers more frequently than colleagues at non-MSIs. For additional insight into the barriers facing these educators, we conducted focus groups which provided qualitative data that supported the survey findings and revealed common themes including faculty perceptions of the relevance of bioinformatics in the curriculum. Despite the perceived value of bioinformatics education, many focus group members cited lack of student preparation and interest, and technological access as barriers. Participants also discussed how professional development and community support would enhance and sustain bioinformatics teaching.DiscussionTaken all together, this study indicates that challenges remain, which vary among faculty types and settings, but that more educators are attempting to integrate bioinformatics into life sciences education. In summary, our results suggest that redoubled efforts to provide training and community support to life sciences faculty is necessary.

Grand challenges in bioinformatics education and training.

Grand challenges in bioinformatics education and training.

Bioschemas training profiles: A set of specifications for standardizing training information to facilitate the discovery of training programs and resources.

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.

Empowering beginners in bioinformatics with ChatGPT.

The impressive conversational and programming abilities of ChatGPT make it an attractive tool for facilitating the education of bioinformatics data analysis for beginners. In this study, we proposed an iterative model to fine-tune instructions for guiding a chatbot in generating code for bioinformatics data analysis tasks. We demonstrated the feasibility of the model by applying it to various bioinformatics topics. Additionally, we discussed practical considerations and limitations regarding the use of the model in chatbot-aided bioinformatics education.

A Python-based educational software tool for visualizing bioinformatics alignment algorithms.

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-bystep processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

A course-based undergraduate research experience for bioinformatics education in undergraduate students.

With rapid development of sequencing technology and the continuous accumulation of biological big data, people who are capable of using bioinformatic skills to analyze omics data and work out biological problems are urgently needed in the workforce, which highlights the importance of developing bioinformatics skills early in the undergraduate curriculum. Meanwhile, course-based undergraduate research experience (CURE) courses have been proved to be an effective teaching format that have many advantages over traditional labs and lectures. Here we introduced an implementation of CURE course of bioinformatics data analysis and visualization for undergraduate students in major of bioinformatics and evaluated the learning outcomes. We were able to address 10 out of 15 core competencies identified by Network for Integrating Bioinformatics into Life Sciences Education. Besides, results evaluated by Laboratory Course Assessment Survey demonstrated the goals of collaboration, discovery and relevance, and iteration were accomplished in our course. Meanwhile, a significant increase in scores of final examinations and a long-term improvement on students' research ability on bioinformatics data analysis and visualization were also observed. In summary, this CURE course is useful for undergraduate students learning related knowledge and participate in authentic research in the field of bioinformatics.

Novel Ground-Up 3D Multicellular Simulators for Synthetic Biology CAD Integrating Stochastic Gillespie Simulations Benchmarked with Topologically Variable SBML Models.

The elevation of Synthetic Biology from single cells to multicellular simulations would be a significant scale-up. The spatiotemporal behavior of cellular populations has the potential to be prototyped in silico for computer assisted design through ergonomic interfaces. Such a platform would have great practical potential across medicine, industry, research, education and accessible archiving in bioinformatics. Existing Synthetic Biology CAD systems are considered limited regarding population level behavior, and this work explored the in silico challenges posed from biological and computational perspectives. Retaining the connection to Synthetic Biology CAD, an extension of the Infobiotics Workbench Suite was considered, with potential for the integration of genetic regulatory models and/or chemical reaction networks through Next Generation Stochastic Simulator (NGSS) Gillespie algorithms. These were executed using SBML models generated by in-house SBML-Constructor over numerous topologies and benchmarked in association with multicellular simulation layers. Regarding multicellularity, two ground-up multicellular solutions were developed, including the use of Unreal Engine 4 contrasted with CPU multithreading and Blender visualization, resulting in a comparison of real-time versus batch-processed simulations. In conclusion, high-performance computing and client-server architectures could be considered for future works, along with the inclusion of numerous biologically and physically informed features, whilst still pursuing ergonomic solutions.

Bioinformatics education for undergraduates: the need for project-based and experiential approaches

Bioinformatics education for undergraduates: the need for project-based and experiential approaches

Bioinformatics education for undergraduates: the need for project-based and experiential approaches

Bioinformatics education for undergraduates: the need for project-based and experiential approaches

Implementation of Bioinformatics Learning in Senior High School: A Systematic Review

This study aimed to analyze the implementation of bioinformatics learning in senior high school from research articles. A systematic review was conducted in this study and showed the findings of relevant studies about the implementation of bioinformatics learning in senior high school. Three journals used for a systematic review: PLOS Computational Biology, Briefing Bioinformatics, and CBE-Life Science Education. A total of nine papers were included in the systematic review from 166 articles screened by criteria; bioinformatics learning or bioinformatics education at the senior high school level, published from 2010 to 2019. The result of this study is the trend of learning outcomes of bioinformatics learning at senior high school is to increase cognitive or knowledge in the Biology field, especially molecular biology (67%). The trend for the type of assessment in bioinformatics learning at senior high school is the observation to assess the learning outcome (44%). The trend of contents in bioinformatics learning used Biology, computer science, and information technologies (100%). The trend of bioinformatics tools in bioinformatics learning at senior high school is BLAST (44%). The trend of teaching strategy to implement bioinformatics learning at senior high school is inquiry and problem-based learning (each teaching strategy is 22%). The last is the trend of the period in implementing bioinformatics learning at senior high school is three periods in each topic (33%). For further study, the trend of implementation of bioinformatics learning can be integrated to design the new framework for implementing bioinformatics learning at senior high school. These findings recommended that science teachers or science educators promote bioinformatics learning at senior high schools.

RECENT TRENDS IN BIOINFORMATICS

The development of biotechnology has accelerated, especially during the past ten years, as a result of the massive amounts of data generated by the sequencing of genomes and the solution of crystal structures. Future possibilities are given along with a description of bioinformatics education in India. In order to serve the scientific community, the Indian government made a concerted effort to build India's bioinformatics infrastructure in the 1980s. The necessity for skilled people with knowledge of information technology and biotechnology was reinforced by developments in the field of bioinformatics. Keywords: Bioinformatics, genomes, biological data.

Leveraging H3Africa Scholarly Publications for Technology-Enhanced Personalized Bioinformatics Education

The Coronavirus Disease 2019 (COVID-19) pandemic has catalyzed the expectations for technology-enhanced interactions with personalized educational materials. Adjusting the content of educational materials to the geographical location of a learner is a customization feature of personalized education and is used to develop the interest of a learner in the content. The educational content of interest in this report is bioinformatics, in which the knowledge spans biological science and applied mathematics disciplines. The Human Heredity and Health in Africa (H3Africa) Initiative is a resource suitable for use when obtaining data and peer-reviewed scholarly articles, which are geographically relevant and focus on authentic problem solving in the human health domain. We developed a computerized platform of interactive visual representations of curated bioinformatics datasets from H3Africa projects, which also supports customization, individualization and adaptation features of personalized education. We obtained evidence for the positive effect size and acceptable usability of a visual analytics resource designed for the retrieval-based learning of facts on functional impacts of genomic sequence variants. We conclude that technology-enhanced personalized bioinformatics educational interventions have implications in (1) the meaningful learning of bioinformatics; (2) stimulating additional student interest in bioinformatics; and (2) improving the accessibility of bioinformatics education to non-bioinformaticians.

Michael Waterman's Contributions to Computational Biology and Bioinformatics.

On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).

Report of the ML4Microbiome workshop 2021 - Statistical and Machine Learning Techniques for Microbiome Data Analysis

The bacteria living in and on us, over 100 trillion, are essential for human development, immunity and nutrition and ultimately for human health. Researchers awareness of the importance of the microbiome for human wellbeing brought in the latest decade to the flourishing of new and numerous projects focused on the human microbiome, increasing the number of large microbiome datasets available. As a result, statistical and machine learning techniques to solve microbiome data analysis challenges are in high demand. The workshop “Statistical and Machine Learning Techniques for Microbiome Data Analysis” was organised by the COST Action ML4Microbiome to introduce the main concepts of study design and statistical/machine learning techniques used in human microbiome studies to a broad community of researchers and to foster connections between the discovery-oriented microbiome and statistical/machine learning researchers inside and outside the ML4Microbiome COST Action. To this aim, the workshop was organised as part of the training programme of the GOBLET & EMBnet Annual General Meeting 2021. This allowed ML4Microbiome to reach a broad and multidisciplinary community of scientists working in Bioinformatics and Computational Biology research and education from all over the world. The workshop attracted more than 80 participants from 40 different countries. In this paper, we report about the main topics treated and discuss the attendants’ feedback regarding their level of satisfaction and their specific needs/demands for possible improvement of the training offer of ML4Microbiome in the microbiome research field. Under the authors’ permission, presentations were recorded and are available as an ML4Microbiome playlist on YouTube (www.youtube.com/playlist?list=PL2aQNGWO1UUUmK7GJBK29yZr8Hnq6xFSD). The programme and presentations files are available at the ML4Microbiome website (https://www.ml4microbiome.eu/activities-and-events/ml4microbiome-workshop-on-statistical-and-machine-learning-techniques-for-microbiome-data-analysis/).

Interdisciplinary and Transferable Concepts in Bioinformatics Education: Observations and Approaches From a UK MSc Course

Bioinformatics is a highly interdisciplinary subject, with substantial and growing influence in health, environmental science and society, and is utilised by scientists from many diverse academic backgrounds. Education in bioinformatics therefore necessitates effective development of skills in interdisciplinary collaboration, communication, ethics, and critical analysis of research, in addition to practical and technical skills. Insights from bioinformatics training can additionally inform developing education in the tightly aligned and emerging disciplines of data science and artificial intelligence. Here, we describe the design, implementation, and review of a module in a UK MSc-level bioinformatics programme attempting to address these goals for diverse student cohorts. Reflecting the philosophy of the field and programme, the module content was designed either as “diversity-addressing”—working toward a common foundation of knowledge—or “diversity-exploiting”—where different student viewpoints and skills were harnessed to facilitate student research projects “greater than the sum of their parts.” For a universal introduction to technical concepts, we combined a mixed lecture/immediate computational practical approach, facilitated by virtual machines, creating an efficient technical learning environment praised in student feedback for building confidence among cohorts with diverse backgrounds. Interdisciplinary group research projects where diverse students worked on real research questions were supervised in tandem with interactive contact time covering transferable skills in collaboration and communication in diverse teams, research presentation, and ethics. Multi-faceted feedback and assessment provided a constructive alignment with real peer-reviewed bioinformatics research. We believe that the inclusion of these transferable, interdisciplinary, and critical concepts in a bioinformatics course can help produce rounded, experienced graduates, ready for the real world and with many future options in science and society. In addition, we hope to provide some ideas and resources to facilitate such inclusion.

Bringing bioinformatics to schools with the 4273pi project

Over the last few decades, the nature of life sciences research has changed enormously, generating a need for a workforce with a variety of computational skills such as those required to store, manage, and analyse the large biological datasets produced by next-generation sequencing. Those with such expertise are increasingly in demand for employment in both research and industry. Despite this, bioinformatics education has failed to keep pace with advances in research. At secondary school level, computing is often taught in isolation from other sciences, and its importance in biological research is not fully realised, leaving pupils unprepared for the computational component of Higher Education and, subsequently, research in the life sciences. The 4273pi Bioinformatics at School project (https://4273pi.org) aims to address this issue by designing and delivering curriculum-linked, hands-on bioinformatics workshops for secondary school biology pupils, with an emphasis on equitable access. So far, we have reached over 180 schools across Scotland through visits or teacher events, and our open education resources are used internationally. Here, we describe our project, our aims and motivations, and the practical lessons we have learned from implementing a successful bioinformatics education project over the last 5 years.

Methods for analyzing next-generation sequencing data XVII. The Future of Bioinformatics Education.

Methods for analyzing next-generation sequencing data XVII. The Future of Bioinformatics Education.

An Appraisal of Skill Gaps in Bioinformatics Education

There has been an exponential rise in the field of bioinformatics in the last decade. The specialists of this field need to be well versed in computing, statistics, and mathematics, along with expertise in biological sciences. This review is an attempt to understand the existing skill gaps in the education of bioinformatics globally and to give the policy developers some indicators while designing the curriculum of the bioinformatics course. Authors have found that the pace with which this field is developing is not commensurate with the training and education efforts at the university level globally. But, on a positive note, academia and industry both seem to recognise this lag, therefore, efforts are being made in this direction. This review also summarizes the distinctive features needed to integrate bioinformatics in education for curriculum designing, teaching, and learning methods.