Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs — Course Based Undergraduate Research Experiences — evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.

Presented at the ECCB2016, den Haag, NL, Sept 2016Africa is not unique in its need for basic bioinformatics training for individuals from a molecular biology background. However, unique logistical challenges in Africa, most notably access to bioinformatics expertise and internet stability, must be addressed in order to meet this need on the continent. H3ABioNet (www.h3abionet.org), the Pan African Bioinformatics Network for H3Africa, has therefore developed an innovative, free Introduction to bioinformatics course taking these challenges into account. A distance-based learning model has been selected for this 3 month course (July-September 2016) to increase access to expert African and European Bioinformatics trainers covering several bioinformatics topics, including: databases and resources; genomics; Linux; sequence alignment; and phylogenetics. Classrooms with a total of >350 participants are hosted at 20 institutions, across 11 African countries, in order to provide local administrative and academic support. Classroom selection was based on certain infrastructure criteria, including: computer resources; Internet access; and availability of local teaching assistants. Although lectures are delivered live to remote sites via an online platform, to ensure that classroom success does not rely on stable Internet, classrooms can watch pre-recorded and pre-downloaded lecture videos, as well as work through practical assignments on the lecture content, during biweekly contact sessions. Lecture recordings are available on the course website http://training.h3abionet.org/IBT_2016/. While trainers are available via video conferencing to take questions during contact sessions, online ‘question and discussion’ forums, hosted on the course management platform, are also available. This distance based model, developed for a resource limited setting, could easily be adapted to other settings.

Bioinformatics training programs have been developed independently around the world based on the perceived needs of the local and global academic communities. The field of bioinformatics is complicated by the need to train audiences from diverse backgrounds in a variety of topics to various levels of competencies. While there have been several attempts to develop standardised approaches to provide bioinformatics training globally, the challenges encountered in resource limited settings hinder the adaptation of these global approaches. H3ABioNet, a Pan-African Bioinformatics Network with 27 nodes in 16 African countries, has realised that there is no single simple solution to this challenge and has rather, over the years, evolved and adapted training approaches to create a sustainable training environment, with several components that allow for the successful dissemination of bioinformatics knowledge to diverse audiences. This has been achieved through the implementation of a combination of training modalities and sharing of high quality training material and experiences. The results highlight the success of implementing this multi-pronged approach to training, to reach audiences from different backgrounds and provide training in a variety of different areas of expertise. While face-to-face training was initially required and successful, the mixed-model teaching approach allowed for an increased reach, providing training in advanced analysis topics to reach large audiences across the continent with minimal teaching resources. The transition to hackathons provided an environment to allow the progression of skills, once basic skills had been developed, together with the development of real-world solutions to bioinformatics problems. Ensuring our training materials are FAIR, and through synergistic collaborations with global training partners, the reach of our training materials extends beyond H3ABioNet. Coupled with the opportunity to develop additional career building soft skills, such as scientific communication, H3ABioNet has created a flexible, sustainable and high quality bioinformatics training environment that has successfully been implemented to train several highly skilled African bioinformaticians on the continent.

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.

Syllabi from the courses taught in 2013. (PDF)

Markdown source, PDF, and HTML rendering of bioinformatics training resources from http://stephenturner.us/p/edu.

Due to the insufficient human and infrastructure capacity to use novel genomics and bioinformatics technologies, Sub-Saharan Africa countries have not entirely ripped the benefits of these technologies in health and other sectors. The main objective of this study was to map out the interest and capacity for conducting bioinformatics and related research in Tanzania. The survey collected demographic information like age group, experience, seniority level, gender, number of respondents per institution, number of publications, and willingness to join the community of practice. The survey also investigated the capacity of individuals and institutions about computing infrastructure, operating system use, statistical packages in use, the basic Microsoft packages experience, programming language experience, bioinformatics tools and resources usage, and type of analyses performed. Moreover, respondents were surveyed about the challenges they faced in implementing bioinformatics and their willingness to join the bioinformatics community of practice in Tanzania. Out of 84 respondents, 50 (59.5%) were males. More than half of these 44 (52.4%) were between 26–32 years. The majority, 41 (48.8%), were master’s degree holders with at least one publication related to bioinformatics. Eighty (95.2%) were willing to join the bioinformatics network and initiative in Tanzania. The major challenge faced by 22 (26.2%) respondents was the lack of training and skills. The most used resources for bioinformatics analyses were BLAST, PubMed, and GenBank. Most respondents who performed analyses included sequence alignment and phylogenetics, which was reported by 57 (67.9%) and 42 (50%) of the respondents, respectively. The most frequently used statistical software packages were SPSS and R. A quarter of the respondents were conversant with computer programming. Early career and young scientists were the largest groups of responders engaged in bioinformatics research and activities across surveyed institutions in Tanzania. The use of bioinformatics tools for analysis is still low, including basic analysis tools such as BLAST, GenBank, sequence alignment software, Swiss-prot and TrEMBL. There is also poor access to resources and tools for bioinformatics analyses. To address the skills and resources gaps, we recommend various modes of training and capacity building of relevant bioinformatics skills and infrastructure to improve bioinformatics capacity in Tanzania.

The mechanism of alternative pre-mRNA splicing (AS) during preimplantation development is largely unknown. In order to capture the dynamic changes of AS occurring during embryogenesis, we carried out bioinformatics analysis based on scRNA-seq data over the time-course preimplantation development in mouse. We detected numerous previously-unreported differentially expressed genes at specific developmental stages and investigated the nature of AS at both minor and major zygotic genome activation (ZGA). The AS and differential AS atlas over preimplantation development were established. The differentially alternatively spliced genes (DASGs) are likely to be key splicing factors (SFs) during preimplantation development. We also demonstrated that there is a regulatory cascade of AS events in which some key SFs are regulated by differentially AS of their own gene transcripts. Moreover, 212 isoform switches (ISs) during preimplantation development were detected, which may be critical for decoding the mechanism of early embryogenesis. Importantly, we uncovered that zygotic AS activation (ZASA) is in conformity with ZGA and revealed that AS is coupled with transcription during preimplantation development. Our results may provide a deeper insight into the regulation of early embryogenesis.

The Molecular Biology Information Service (MBIS) of the Health Sciences Library System (HSLS) at the University of Pittsburgh conducted a 33-question online survey to evaluate the effectiveness of services provided by the MBIS. The survey was administered via Qualtrics. Questions were organized into 6 categories: Demographics, Software, Instruction, Website, Service, and Outreach. Questions were a mix of multiple choice, ranking, and free text. Participants were recruited during a six-week period in early 2018. The survey was advertised via numerous methods: MBIS blog post, HSLS website post, MBIS listserv notifications, direct email invitations, and during MBIS workshops. The survey did not require oversight by the University of Pittsburgh IRB.The CSV file contains de-identifed survey responses--identifying information for Q6.7 was redacted.
Also included is a PDF of the survey questions and a PDF of the Qualtrics survey response report.

Epidemics of emerging and re-emerging infectious diseases are a danger to civilian and military populations worldwide. Health security and mitigation of infectious disease threats is a priority of the United States Government and the Department of Defense (DoD). Next generation sequencing (NGS) and Bioinformatics (BI) enhances traditional biosurveillance by providing additional data to understand transmission, identify resistance and virulence factors, make predictions, and update risk assessments. As more and more laboratories adopt NGS and BI technologies they encounter challenges in building local capacity. In addition to choosing the right sequencing platform and approach, considerations must also be made for the complexity of bioinformatics analyses, data storage, as well as personnel and computational requirements. To address these needs, a comprehensive training program was developed covering wet lab and bioinformatics approaches to NGS. The program is meant to be modular and adaptive to meet both common and individualized needs of medical research and public health laboratories across the DoD. The training program was first deployed internationally to the Basic Science Laboratory of the US Army Medical Research Directorate-Africa in Kisumu, Kenya, which is an overseas Lab of the Walter Reed Army Institute of Research (WRAIR). A week-long workshop with intensive focus on targeted sequencing and the bioinformatics of genome assembly (n = 24 participants) was held. Post-workshop self-assessment (completed by 21 participants) noted significant median gains in knowledge domains related to NGS targeted sequencing, bioinformatics for genome assembly, and sequence quality assessment. The participants also reported that the information on study design, sample preparation, sequencing quality control, data quality assessment, reporting, and basic and advanced bioinformatics analysis were the most useful information presented in the training. While longer-term evaluations are planned, the training resulted in significant short-term improvement of a laboratory’s self-reported wet lab and bioinformatics capabilities. This framework can be used for future DoD laboratory development in the area of NGS and BI for infectious disease surveillance, ultimately enhancing this global DoD capability.

Bioinformatics skills are increasingly relevant to research in most areas of the life sciences. The availability of genome sequences and large data sets provide unique opportunities to incorporate bioinformatics exercises into undergraduate microbiology courses. The goal of this project was to develop a teaching module to investigate the abundance and phylogenetic relationships amongst bacteriophages using a set of freely available bioinformatics tools. Computational identification and examination of bacteriophage genomes, followed by phylogenetic analyses, provides opportunities to incorporate core bioinformatics competencies in microbiology courses and enhance students’ bioinformatics skills. The first activity consisted of using PHASTER (PHAge Search Tool Enhanced Release), a bioinformatics tool that identifies bacteriophage sequences within bacterial chromosomes. Further computational analyses were conducted to align bacteriophage proteins, genomes, and determine phylogenetic relationships amongst these viruses. This part of the project was carried out using the Clustal omega, MAFFT (Multiple Alignment using Fast Fourier Transform), and Interactive Tree of Life (iTOL) programs for sequence alignments and phylogenetic analyses. The laboratory activities were field tested in undergraduate directed research, and microbiology classes. The learning objectives were assessed by comparing the scores of pre and post-tests and grading final presentations. Post-tests were higher than pre-test scores at or below p = 0.002. The data suggest in silico phage hunting improves students’ ability to search databases, interpret phylogenetic trees, and use bioinformatics tools to examine genome structure. This activity allows instructors to integrate key bioinformatic concepts in their curriculums and gives students the opportunity to participate in a research-directed learning environment in the classroom.

In this article, we present our three-class course sequence to educate students about microbiome analysis and metagenomics through experiential learning by taking them from inquiry to analysis of the microbiome: Molecular Ecology Lab, Bioinformatics, and Computational Microbiome Analysis. Students developed hypotheses, designed lab experiments, sequenced the DNA from microbiomes, learned basic python/R scripting, became proficient in at least one microbiome analysis software, and were able to analyze data generated from the microbiome experiments. While over 150 students (graduate and undergraduate) were impacted by the development of the series of courses, our assessment was only on undergraduate learning, where 45 students enrolled in at least one of the three courses and 4 students took all three. Students gained skills in bioinformatics through the courses, and several positive comments were received through surveys and private correspondence. Through a summative assessment, general trends show that students became more proficient in comparative genomic techniques and had positive attitudes toward their abilities to bridge biology and bioinformatics. While most students took individual or 2 of the courses, we show that pre- and post-surveys of these individual classes still showed progress toward learning objectives. It is expected that students trained will enter the workforce with skills needed to innovate in the biotechnology, health, and environmental industries. Students are trained to maximize impact and tackle real world problems in biology and medicine with their learned knowledge of data science and machine learning. The course materials for the new microbiome analysis course are available on Github: https://github.com/EESI/Comp_Metagenomics_resources.

The development and application of bioinformatics has been growing steadily, but its learning and training has been lagging. We have approached this problem through a bi-annual event, called EGB (Escola Gaúcha de Bioinformática), dedicated to undergraduate and graduate students (mainly from biology, biomedicine, chemistry, physics, and computer sciences), as well as professionals, to mingle and be presented to bioinformatics from sequence, structure, and computational standpoints simultaneously. The interactive environment provided by EGB allows for participants mingling, independently from their training background, fostering collaborative learning and experience exchange. Both lecturers and students are encouraged to collaborate and communicate, with no formal acknowledgement of “status differentiation”.

Alternative splicing is an essential regulatory mechanism for gene expression in mammalian cells contributing to protein, cellular, and species diversity. In cancer, alternative splicing is frequently disturbed, leading to changes in the expression of alternatively spliced protein isoforms. Advances in sequencing technologies and analysis methods led to new insights into the extent and functional impact of disturbed alternative splicing events. In this review, we give a brief overview of the molecular mechanisms driving alternative splicing, highlight the function of alternative splicing in healthy tissues and describe how alternative splicing is disrupted in cancer. We summarize current available computational tools for analyzing differential transcript usage, isoform switching events, and the pathogenic impact of cancer-specific splicing events. Finally, the strategies of three recent pan-cancer studies on isoform switching events are compared. Their methodological similarities and discrepancies are highlighted and lessons learned from the comparison are listed. We hope that our assessment will lead to new and more robust methods for cancer-specific transcript detection and help to produce more accurate functional impact predictions of isoform switching events.

The increasing adoption of high-throughput “omics” technologies has heightened the demand for standardized, scalable, and reproducible bioinformatics workflows. Nextflow and nf-core provide a robust framework for researchers, particularly early- and mid-career researchers (EMCRs), to navigate complex data analysis. At The Kids Research Institute Australia, we implemented a structured approach to bioinformatics capacity building using these tools. This perspective presents nine practical rules derived from lessons learnt, which facilitated the successful adoption of Nextflow and nf-core, addressing implementation challenges, knowledge gaps, resource allocation, and community support. Our experience serves as a guide for institutions aiming to establish sustainable bioinformatics capabilities and empower EMCRs.

BackgroundDespite the positive effects of endurance training on the cardiovascular (CV) system, excessive exercise induces not only physiological adaptations but also adverse changes in CV system, including the heart. We aimed to evaluate the selected miRNAs expression based on bioinformatic analysis and their changes before and after an ultramarathon run.Materials and MethodsCardiac tissue-specific targets were identified with the Tissue 2.0 database. Gene-gene interaction data were retrieved from the STRING app for Cytoscape. Twenty-three endurance athletes were recruited to the study. Athletes ran to completion (100 km) or exhaustion (52–91 km, median 74 km). All participants completed pre- and post-run testing. miRNAs expressions were measured both before and after the race.ResultsEnrichment analysis of the signaling pathways associated with the genes targeted by miRNAs selected for qRT-PCR validation (miR-1-3p, miR-126, miR-223, miR-125a-5p, miR-106a-5p, and miR-15a/b). All selected miRNAs showed overlap in regulation in pathways associated with cancer, IL-2 signaling, TGF-β signaling as well as BDNF signaling pathway. Analysis of metabolites revealed significant regulation of magnesium and guanosine triphosphate across analyzed miRNA targets. MiR-1-3p, miR-125a-5p, miR-126, and miR-223 expressions were measured in 23 experienced endurance athletes, before and after an ultramarathon wherein athletes ran to completion (100 km) or exhaustion (52–91 km, median 74 km). The expressions of miR-125a-5p, miR-126, and miR-223 were significantly increased after the race (p = 0.007, p = 0.001, p = 0.014, respectively). MiR-1-3p expression post-run showed a negative correlation with the post-run levels of high-sensitivity C-reactive protein (hs-CRP) (r = −0.632, p = 0.003). Higher miR-1-3p expression was found in runners, who finished the race under 10 h compared to runners who finished over 10 h (p = 0.001). Post-run miR-125a-5p expression showed a negative correlation with the peak lactate during the run (r = −0.576, p = 0.019).ConclusionExtreme physical activity, as exemplified by an ultramarathon, is associated with changes in circulating miRNAs’ expression related to inflammation, fibrosis, and cardiac muscle function. In particular, the negative correlations between miR-125a-5p and lactate concentrations, and miR-1-3p and hs-CRP, support their role in specific exercise-induced adaptation. Further studies are essential to validate the long-term effect of these observations.

Exercise and exercise-induced weight loss have a beneficial effect on overall health, including positive effects on molecular pathways associated with immune function, especially in overweight individuals. The main aim of our study was to assess how energy deprivation (i.e., “semi-starvation”) leading to substantial fat mass loss affects the immune system and immunosuppression in previously normal weight individuals. Thus, to address this hypothesis, we applied a high-throughput systems biology approach to better characterize potential key pathways associated with immune system modulation during intensive weight loss and subsequent weight regain. We examined 42 healthy female physique athletes (age 27.5 ± 4.0 years, body mass index 23.4 ± 1.7 kg/m2) volunteered into either a diet group (n = 25) or a control group (n = 17). For the diet group, the energy intake was reduced and exercise levels were increased to induce loss of fat mass that was subsequently regained during a recovery period. The control group was instructed to maintain their typical lifestyle, exercise levels, and energy intake at a constant level. For quantification of systems biology markers, fasting blood samples were drawn at three time points: baseline (PRE), at the end of the weight loss period (MID 21.1 ± 3.1 weeks after PRE), and at the end of the weight regain period (POST 18.4 ± 2.9 weeks after MID). In contrast to the control group, the diet group showed significant (false discovery rate

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible Staphylococcus aureus strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by S. aureus cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.

The mechanism of alternative pre-mRNA splicing (AS) during preimplantation development is largely unknown. In order to capture the dynamic changes of AS occurring during embryogenesis, we carried out bioinformatics analysis based on scRNA-seq data over the time-course preimplantation development in mouse. We detected numerous previously-unreported differentially expressed genes at specific developmental stages and investigated the nature of AS at both minor and major zygotic genome activation (ZGA). The AS and differential AS atlas over preimplantation development were established. The differentially alternatively spliced genes (DASGs) are likely to be key splicing factors (SFs) during preimplantation development. We also demonstrated that there is a regulatory cascade of AS events in which some key SFs are regulated by differentially AS of their own gene transcripts. Moreover, 212 isoform switches (ISs) during preimplantation development were detected, which may be critical for decoding the mechanism of early embryogenesis. Importantly, we uncovered that zygotic AS activation (ZASA) is in conformity with ZGA and revealed that AS is coupled with transcription during preimplantation development. Our results may provide a deeper insight into the regulation of early embryogenesis.