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Genome data, with underlying new knowledge, are accumulating at exponential rate thanks to ever-improving sequencing technologies and the parallel development of dedicated efficient Bioinformatics methods and tools. Advanced Education in Bioinformatics and Genome Analyses is to a large extent not accessible to students in developing countries where endeavors to set up Bioinformatics courses concern most often only basic levels. Here, we report a pioneering pilot experience concerning the design and implementation, from scratch, of a three-months advanced and extensive course in Bioinformatics and Genome Analyses in the Institut Pasteur de Tunis. Most significantly the outcome of the course was upgrading the participants' skills in Bioinformatics and Genome Analyses to recognized international standards. Here we detail the different steps involved in the implementation of this course as well as the topics covered in the program. The description of this pilot experience might be helpful for the implementation of other similar educational projects, notably in developing countries, aiming to go beyond basics and providing young researchers with high-level skills.

The importance of ontologies in the biomedical domain is generally recognized. However, their quality is often too poor for large-scale use in critical applications, at least partially due to insufficient training of ontology developers. To show the efficacy of guideline-based ontology development training on the performance of ontology developers. The hypothesis was that students who received training on top-level ontologies and design patterns perform better than those who only received training in the basic principles of formal ontology engineering. A curriculum was implemented based on a guideline for ontology design. A randomized controlled trial on the efficacy of this curriculum was performed with 24 students from bioinformatics and related fields. After joint training on the fundamentals of ontology development the students were randomly allocated to two groups. During the intervention, each group received training on different topics in ontology development. In the assessment phase, all students were asked to solve modeling problems on topics taught differentially in the intervention phase. Primary outcome was the similarity of the students' ontology artefacts compared with gold standard ontologies developed by the authors before the experiment; secondary outcome was the intra-group similarity of group members' ontologies. The experiment showed no significant effect of the guideline-based training on the performance of ontology developers (a) the ontologies developed after specific training were only slightly but not significantly closer to the gold standard ontologies than the ontologies developed without prior specific training; (b) although significant differences for certain ontologies were detected, the intra-group similarity was not consistently influenced in one direction by the differential training. Methodologically limited, this study cannot be interpreted as a general failure of a guideline-based approach to ontology development. Further research is needed to increase insight into whether specific development guidelines and practices in ontology design are effective.

Both a means of denoising and simplification, it can be beneficial for the majority of modern biological datasets, in which it’s not uncommon to have hundreds or even millions of simultaneous measurements collected for a single sample. Because of “the curse of dimensionality,” many statistical methods lack power when applied to high-dimensional data. Formally, the Marchenko–Pastur distribution asymptotically models the distribution of the singular values of large random matrices. [...]for datasets large in both the number of observations and features, you use a rule of retaining only eigenvalues outside the support of the fitted Marchenko–Pastur distribution; however, remember that this applies only when your data have at least thousands of samples and thousands of features. [...]the height-to-width ratio of a PCA plot should be consistent with the ratio between the corresponding eigenvalues. Because eigenvalues reflect the variance in coordinates of the associated PCs, you only need to ensure that in the plots, one \"unit\" in direction of one PC has the same length as one \"unit\" in direction of another PC. Because batch effects can confound the signal of interest, it is a good practice to check for their presence and, if found, to remove them before proceeding with further downstream analysis.

Bioinformatics is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. These use cases are intended to demonstrate how others can make use of the competencies and engage in the process of their continuing refinement and application. The report concludes with a consideration of remaining challenges and future plans.

Phylogenetics and phylodynamics are central topics in modern evolutionary biology. Phylogenetic methods reconstruct the evolutionary relationships among organisms, whereas phylodynamic approaches reveal the underlying diversification processes that lead to the observed relationships. These two fields have many practical applications in disciplines as diverse as epidemiology, developmental biology, palaeontology, ecology, and linguistics. The combination of increasingly large genetic data sets and increases in computing power is facilitating the development of more sophisticated phylogenetic and phylodynamic methods. Big data sets allow us to answer complex questions. However, since the required analyses are highly specific to the particular data set and question, a black-box method is not sufficient anymore. Instead, biologists are required to be actively involved with modeling decisions during data analysis. The modular design of the Bayesian phylogenetic software package BEAST 2 enables, and in fact enforces, this involvement. At the same time, the modular design enables computational biology groups to develop new methods at a rapid rate. A thorough understanding of the models and algorithms used by inference software is a critical prerequisite for successful hypothesis formulation and assessment. In particular, there is a need for more readily available resources aimed at helping interested scientists equip themselves with the skills to confidently use cutting-edge phylogenetic analysis software. These resources will also benefit researchers who do not have access to similar courses or training at their home institutions. Here, we introduce the “Taming the Beast” (https://taming-the-beast.github.io/) resource, which was developed as part of a workshop series bearing the same name, to facilitate the usage of the Bayesian phylogenetic software package BEAST 2.

Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondent's degree of training, time since degree earned, and/or the Carnegie Classification of the respondent's institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of the syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate biology students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula.

  [...]you will likely share your code with multiple lab mates or collaborators, and they may have suggestions on how to improve it. A version control system (VCS) allows you to track the iterative changes you make to your code. [...]you can experiment with new ideas but always have the option to revert to a specific past version of the code you used to generate particular results. [...]by forking public repositories and sending pull requests, you can directly improve scientific software (Fig 4).

Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed increases in the total number and diversity of experiments: the share of experiments other than bulk RNA- or DNA-sequencing increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of approximately 300 academic biomedical researchers interested in computational biology, referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops, and coding sessions while evolving along with the needs of its members. A survey of CBB’s impact confirmed the group’s events increased the members’ exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.

Next-generation sequencing (NGS) technologies offer detailed and inexpensive identification of the genetic structure of living organisms. The massive data volume necessitates the utilization of advanced computational resources for analyses. However, the rapid accumulation of data and the urgent need for analysis tools have caused the development of imperfect software solutions. Given their immense potential in clinical applications and the recent reproducibility crisis discussions in science and technology, these tools must be thoroughly examined. Typically, NGS data analysis tools are benchmarked under homogeneous conditions, with well-trained personnel and ideal hardware and data environments. However, in the real world, these analyses are done under heterogeneous conditions in terms of computing environments and experience levels. This difference is mostly overlooked, therefore studies that examine NGS workflows generated under various conditions would be highly valuable. Moreover, a detailed assessment of the difficulties faced by the trainees would allow for improved educational programs for better NGS analysis training. Considering these needs, we designed an elective undergraduate bioinformatics course project for computer engineering students at Istanbul Technical University. Students were tasked to perform and compare 12 different somatic variant calling pipelines on the recently published SEQC2 dataset. Upon examining the results, we have realized that despite seeming correct, the final variant lists created by different student groups display a high level of heterogeneity. Notably, the operating systems and installation methods were the most influential factors in variant-calling performance. Here, we present detailed evaluations of our case study and provide insights for better bioinformatics training.

As genomics technologies advance, there is a growing demand for computational biologists trained for genomics analysis but instructors face significant hurdles in providing formal training in computer programming, statistics, and genomics to biology students. Fully online learners represent a significant and growing community that can contribute to meet this need, but they are frequently excluded from valuable research opportunities which mostly do not offer the flexibility they need. To address these opportunity gaps, we developed an asynchronous course-based undergraduate research experience (CURE) for computational genomics specifically for fully online biology students. We generated custom learning materials and leveraged remotely accessible computational tools to address 2 novel research questions over 2 iterations of the genomics CURE, one testing bioinformatics approaches and one mining cancer genomics data. Here, we present how the instructional team distributed analysis needed to address these questions between students over a 7.5-week CURE and provided concurrent training in biology and statistics, computer programming, and professional development. Scores from identical learning assessments administered before and after completion of each CURE showed significant learning gains across biology and coding course objectives. Open-response progress reports were submitted weekly and identified self-reported adaptive coping strategies for challenges encountered throughout the course. Progress reports identified problems that could be resolved through collaboration with instructors and peers via messaging platforms and virtual meetings. We implemented asynchronous communication using the Slack messaging platform and an asynchronous journal club where students discussed relevant publications using the Perusall social annotation platform. The online genomics CURE resulted in unanticipated positive outcomes, including students voluntarily discussing plans to continue research after the course. These outcomes underscore the effectiveness of this genomics CURE for scientific training, recruitment and student-mentor relationships, and student successes. Asynchronous genomics CUREs can contribute to a more skilled, diverse, and inclusive workforce for the advancement of biomedical science.