The development and application of bioinformatics core competencies to improve bioinformatics training and education.

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.

An instructional definition and assessment rubric for bioinformatics instruction.

The lack of an instructional definition of bioinformatics delays its effective integration into biology coursework. Using an iterative process, our team of biologists, a mathematician/computer scientist, and a bioinformatician together with an educational evaluation and assessment specialist, developed an instructional definition of the discipline: Bioinformatics is "an interdisciplinary field that is concerned with the development and application of algorithms that analyze biological data to investigate the structure and function of biological polymers and their relationships to living systems." The field is defined in terms of its two primary foundational disciplines, biology and computer science, and its interdisciplinary nature. At the same time, we also created a rubric for assessing open-ended responses to a prompt about what bioinformatics is and how it is used. The rubric has been shown to be reliable in successive rounds of testing using both common percent agreement (89.7%) and intraclass correlation coefficient (0.620) calculations. We offer the definition and rubric to life sciences instructors to help further integrate bioinformatics into biology instruction, as well as for fostering further educational research projects.

Incubators: Building community networks and developing open educational resources to integrate bioinformatics into life science education.

While it is essential for life science students to be trained in modern techniques and approaches, rapidly developing, interdisciplinary fields such as bioinformatics present distinct challenges to undergraduate educators. In particular, many educators lack training in new fields, and high-quality teaching and learning materials may be sparse. To address this challenge with respect to bioinformatics, the Network for the Integration of Bioinformatics into Life Science Education (NIBLSE), in partnership with Quantitative Undergraduate Biology Education and Synthesis (QUBES), developed incubators, a novel collaborative process for the development of open educational resources (OER). Incubators are short-term, online communities that refine unpublished teaching lessons into more polished and widely usable learning resources. The resulting products are published and made freely available in the NIBLSE Resource Collection, providing recognition of scholarly work by incubator participants. In addition to producing accessible, high-quality resources, incubators also provide opportunities for faculty development. Because participants are intentionally chosen to represent a range of expertise in bioinformatics and pedagogy, incubators also build professional connections among educators with diverse backgrounds and perspectives and promote the discussion of practical issues involved in deploying a resource in the classroom. Here we describe the incubator process and provide examples of beneficial outcomes. Our experience indicates that incubators are a low cost, short-term, flexible method for the development of OERs and professional community that could be adapted to a variety of disciplinary and pedagogical contexts.

Barriers to integration of bioinformatics into undergraduate life sciences education: A national study of US life sciences faculty uncover significant barriers to integrating bioinformatics into undergraduate instruction.

Bioinformatics, a discipline that combines aspects of biology, statistics, mathematics, and computer science, is becoming increasingly important for biological research. However, bioinformatics instruction is not yet generally integrated into undergraduate life sciences curricula. To understand why we studied how bioinformatics is being included in biology education in the US by conducting a nationwide survey of faculty at two- and four-year institutions. The survey asked several open-ended questions that probed barriers to integration, the answers to which were analyzed using a mixed-methods approach. The barrier most frequently reported by the 1,260 respondents was lack of faculty expertise/training, but other deterrents-lack of student interest, overly-full curricula, and lack of student preparation-were also common. Interestingly, the barriers faculty face depended strongly on whether they are members of an underrepresented group and on the Carnegie Classification of their home institution. We were surprised to discover that the cohort of faculty who were awarded their terminal degree most recently reported the most preparation in bioinformatics but teach it at the lowest rate.

Bioinformatics core competencies for undergraduate life sciences education.

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.

Leveraging human genomic information to identify nonhuman primate sequences for expression array development.

BACKGROUND: Nonhuman primates (NHPs) are essential for biomedical research due to their similarities to humans. The utility of NHPs will be greatly increased by the application of genomics-based approaches such as gene expression profiling. Sequence information from the 3' end of genes is the key resource needed to create oligonucleotide expression arrays. RESULTS: We have developed the algorithms and procedures necessary to quickly acquire sequence information from the 3' end of nonhuman primate orthologs of human genes. To accomplish this, we identified terminal exons of over 15,000 human genes by aligning mRNA sequences with genomic sequence. We found the mean length of complete last exons to be approximately 1,400 bp, significantly longer than previous estimates. We designed primers to amplify genomic DNA, which included at least 300 bp of the terminal exon. We cloned and sequenced the PCR products representing over 5,500 Macaca mulatta (rhesus monkey) orthologs of human genes. This sequence information has been used to select probes for rhesus gene expression profiling. We have also tested 10 sets of primers with genomic DNA from Macaca fascicularis (Cynomolgus monkey), Papio hamadryas (Baboon), and Chlorocebus aethiops (African green monkey, vervet). The results indicate that the primers developed for this study will be useful for acquiring sequence from the 3' end of genes for other nonhuman primate species. CONCLUSION: This study demonstrates that human genomic DNA sequence can be leveraged to obtain sequence from the 3' end of NHP orthologs and that this sequence can then be used to generate NHP oligonucleotide microarrays. Affymetrix and Agilent used sequences obtained with this approach in the design of their rhesus macaque oligonucleotide microarrays.

Large scale analysis of positional effects of single-base mismatches on microarray gene expression data.

BACKGROUND: Affymetrix GeneChips utilize 25-mer oligonucleotides probes linked to a silica surface to detect targets in solution. Mismatches due to single nucleotide polymorphisms (SNPs) can affect the hybridization between probes and targets. Previous research has indicated that binding between probes and targets strongly depends on the positions of these mismatches. However, there has been substantial variability in the effect of mismatch type across studies. METHODS: By taking advantage of naturally occurring mismatches between rhesus macaque transcripts and human probes from the Affymetrix U133 Plus 2 GeneChip, we collected the largest 25-mer probes dataset with single-base mismatches at each of the 25 positions on the probe ever used in this type of analysis. RESULTS: A mismatch at the center of a probe led to a greater loss in signal intensity than a mismatch at the ends of the probe, regardless of the mismatch type. There was a slight asymmetry between the ends of a probe: effects of mismatches at the 3' end of a probe were greater than those at the 5' end. A cross study comparison of the effect of mismatch types revealed that results were not in good agreement among different reports. However, if the mismatch types were consolidated to purine or pyrimidine mismatches, cross study conclusions could be generated. CONCLUSION: The comprehensive assessment of the effects of single-base mismatches on microarrays provided in this report can be useful for improving future versions of microarray platform design and the corresponding data analysis algorithms.