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.

Motifome comparison between modern human, Neanderthal and Denisovan.

BACKGROUND: The availability of the genomes of two archaic humans, Neanderthal and Denisovan, and that of modern humans provides researchers an opportunity to investigate genetic differences between these three subspecies on a genome-wide scale. Here we describe an algorithm that predicts statistically significant motifs based on the difference between a given motif's actual and expected distributions. The algorithm was previously applied to plants but was modified for this work. RESULTS: The result of applying the algorithm to the human, Neanderthal, and Denisovan genomes is a catalog of potential regulatory motifs in these three human subspecies. We examined the distributions of these motifs in genetic elements including human retroviruses, human accelerated regions, and human accelerated conserved noncoding sequences regions. Differences in these distributions could be the origin of differences in phenotype between the three subspecies. Twenty significant motifs common to all three genomes were found; thirty-three were found in endogenous retroviruses in Neanderthal and Denisovan. Ten of these motifs mapped to the 22 bp core of MiR-1304. The core of this genetic element regulates the ENAM and AMTN genes, which take part in odontogenesis and whose 3' UTRs contained significant motifs. The introns of 20 genes were found to contain a large number of significant motifs, which were also overrepresented in 49 human accelerated regions. These genes include NAV2, SorCS2, TRAPPC9, GRID1, PRDM16, CAMTA1, and ASIC which are all involved in neuroregulation. Further analysis of these genes using the GO database indicates that many are associated with neurodevelopment. Also, varying numbers of significant motifs were found to occur in regions of the Neanderthal and Denisovan genomes that are missing from the human genome, suggesting further functional differences between modern and archaic humans. CONCLUSION: Although Neanderthal and Denisovan are now extinct, detailed examination of elements from their genomes can shed light on possible phenotypic and cognitive differences between these two archaic human subspecies and modern humans. Genetic similarities and differences between these three subspecies and other fossil hominids would also be of interest.

Identification and distribution of high-abundance proteins in the octopus spring microbial mat community.

A shotgun metaproteomics approach was employed to identify proteins in a hot spring microbial mat community. We identified 202 proteins encompassing 19 known functions from 12 known phyla. Importantly, we identified two key enzymes involved in the 3-hydroxypropionate CO(2) fixation pathway in uncultivated Roseiflexus spp., which are known photoheterotrophs.

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.

There Is More than Multiple Choice: Crowd-Sourced Assessment Tips for Online, Hybrid, and Face-to-Face Environments.

Developing effective assessments of student learning is a challenging task for faculty and even more difficult for those in emerging disciplines that lack readily available resources and standards. With the power of technology-enhanced education and accessible digital learning platforms, instructors are also looking for assessments that work in an online format. This article will be useful for all teachers, but especially for entry-level instructors, in addition to more mature instructors who are looking to become more well versed in assessment, who seek a succinct summary of assessment types to springboard the integration of new forms of assessment of student learning into their courses. In this paper, ten assessment types, all appropriate for face-to-face, blended, and online modalities, are discussed. The assessments are mapped to a set of bioinformatics core competencies with examples of how they have been used to assess student learning. Although bioinformatics is used as the focus of the assessment types, the question types are relevant to many disciplines.

Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource.

As powerful computational tools and 'big data' transform the biological sciences, bioinformatics training is becoming necessary to prepare the next generation of life scientists. Furthermore, because the tools and resources employed in bioinformatics are constantly evolving, bioinformatics learning materials must be continuously improved. In addition, these learning materials need to move beyond today's typical step-by-step guides to promote deeper conceptual understanding by students. One of the goals of the Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE) is to create, curate, disseminate, and assess appropriate open-access bioinformatics learning resources. Here we describe the evolution, integration, and assessment of a learning resource that explores essential concepts of biological sequence similarity. Pre/post student assessment data from diverse life science courses show significant learning gains. These results indicate that the learning resource is a beneficial educational product for the integration of bioinformatics across curricula.

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.

A method of precise mRNA/DNA homology-based gene structure prediction.

BACKGROUND: Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical splice sites, micro-exons and partial gene structure predictions that span across several genomic clones. RESULTS: We present a mRNA/DNA homology based gene structure prediction tool, GIGOgene. We use a new affine gap penalty splice-enhanced global alignment algorithm running in linear memory for a high quality annotation of splice sites. Our tool includes a novel algorithm to assemble partial gene structure predictions using interval graphs. GIGOgene exhibited a sensitivity of 99.08% and a specificity of 99.98% on the Genie learning set, and demonstrated a higher quality of gene structural prediction when compared to Sim4, est2genome, Spidey, Galahad and BLAT, including when genes contained micro-exons and non-canonical splice sites. GIGOgene showed an acceptable loss of prediction quality when confronted with a noisy Genie learning set simulating ESTs. CONCLUSION: GIGOgene shows a higher quality of gene structure prediction for mRNA/DNA spliced alignment when compared to other available tools.

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.

A new approach for gene annotation using unambiguous sequence joining.

The problem addressed by this paper is accurate and automatic gene annotation following precise identification/ annotation of exon and intron boundaries of biologically verified nucleotide sequences using the alignment of human genomic DNA to curated mRNA transcripts. We provide a detailed description of a new cDNA/DNA homology gene annotation algorithm that combines the results of BLASTN searches and spliced alignments. Compared to other programs currently in use, annotation quality is significantly increased through the unambiguous junction of genomic DNA sequences. We also address gene annotation with both non-canonic splice sites and short exons. The approach has been tested on the Genie learning subset as well as full-scale human RefSeq, and has demonstrated performance as high as 97%.