Bioinformatics mentorship in a resource limited setting

Designing and delivering bioinformatics project-based learning in East Africa

BACKGROUND

The Eastern Africa Network for Bioinformatics Training (EANBiT) has matured through continuous evaluation, feedback, and codesign. We highlight how the program has evolved to meet challenges and achieve its goals and how experiential learning through mini projects enhances the acquisition of skills and collaboration. We continued to learn and grow through honest feedback and evaluation of the program, trainers, and modules, enabling us to provide robust training even during the Coronavirus disease 2019 (COVID-19) pandemic, when we had to redesign the program due to restricted travel and in person group meetings.

RESULTS

In response to the pandemic, we developed a program to maintain "residential" training experiences and benefits remotely. We had to answer the following questions: What must change to still achieve the RT goals? What optimal platforms should be used? How would we manage connectivity and data challenges? How could we avoid online fatigue? Going virtual presented an opportunity to reflect on the essence and uniqueness of the program and its ability to meet the objective of strengthening bioinformatics skills among the cohorts of students using different delivery approaches. It allowed an increase in the number of participants. Evaluating each program component is critical for improvement, primarily when feedback feeds into the program's continuous amendment. Initially, the participants noted that there were too many modules, insufficient time, and a lack of hands-on training as a result of too much focus on theory. In the subsequent iterations, we reduced the number of modules from 27 to five, created a harmonized repository for the materials on GitHub, and introduced project-based learning through the mini projects.

CONCLUSION

We demonstrate that implementing a program design through detailed monitoring and evaluation leads to success, especially when participants who are the best fit for the program are selected on an appropriate level of skills, motivation, and commitment.

HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis

Summary

Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms.

Availability and implementation

HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.

Revolutionizing Medical Microbiology: How Molecular and Genomic Approaches Are Changing Diagnostic Techniques

Molecular and genomic approaches have revolutionized medical microbiology by offering faster and more accurate diagnostic techniques for infectious diseases. Traditional methods, which include culturing microbes and biochemical testing, are time-consuming and may not detect antibiotic-resistant strains. In contrast, molecular and genomic methods, including polymerase chain reaction (PCR)-based techniques and whole-genome sequencing, provide rapid and precise detection of pathogens, early-stage diseases, and antibiotic-resistant strains. These approaches have advantages such as high sensitivity and specificity, the potential for targeted therapies, and personalized medicine. However, implementing molecular and genomic techniques faces challenges related to cost, equipment, expertise, and data analysis. Ethical and legal considerations regarding patient privacy and genetic data usage also arise. Nonetheless, the future of medical microbiology lies in the widespread adoption of molecular and genomic approaches, which can lead to improved patient outcomes and the identification of antibiotic-resistant strains. Continued advancements, education, and exploration of ethical implications are necessary to fully harness the potential of molecular and genomic techniques in medical microbiology.

Integrating computational skills in undergraduate Microbiology curricula in developing countries

The employability of young graduates has gained increasing significance in the labour market of the 21st century. Universities turn out millions of graduates annually, but at the same time, employers highlight their lack of the requisite skills for sustainable employment. We live today in a world of data, and therefore courses that feature numerical and computational tools to gather and analyse data are to be sourced for and integrated into life sciences' curricula as they provide a number of benefits for both the students and faculty members that are engaged in teaching the courses. The lack of this teaching in undergraduate Microbiology curricula is devastating and leaves a knowledge gap in the graduates that are turned out. This results in an inability of the emerging graduates to compete favourably with their counterparts from other parts of the world. There is a necessity on the part of life science educators to adapt their teaching strategies to best support students' curricula that prepare them for careers in science. Bioinformatics, Statistics and Programming are key computational skills to embrace by life scientists and the need for training beginning at undergraduate level cannot be overemphasized. This article reviews the need to integrate computational skills in undergraduate Microbiology curricula in developing countries with emphasis on Nigeria.

Building awareness and capacity of bioinformatics and open science skills in Kenya: a sensitize, train, hack, and collaborate model

We have applied the sensitize-train-hack-community model to build awareness of and capacity in bioinformatics in Kenya. Open science is the practice of science openly and collaboratively, with tools, techniques, and data freely shared to facilitate reuse and collaboration. Open science is not a mandatory curriculum course in schools, whereas bioinformatics is relatively new in some African regions. Open science tools can significantly enhance bioinformatics, leading to increased reproducibility. However, open science and bioinformatics skills, especially blended, are still lacking among students and researchers in resource-constrained regions. We note the need to be aware of the power of open science among the bioinformatics community and a clear strategy to learn bioinformatics and open science skills for use in research. Using the OpenScienceKE framework-Sensitize, Train, Hack, Collaborate/Community-the BOSS (Bioinformatics and Open Science Skills) virtual events built awareness and empowered researchers with the skills and tools in open science and bioinformatics. Sensitization was achieved through a symposium, training through a workshop and train-the-trainer program, hack through mini-projects, community through conferences, and continuous meet-ups. In this paper, we discuss how we applied the framework during the BOSS events and highlight lessons learnt in planning and executing the events and their impact on the outcome of each phase. We evaluate the impact of the events through anonymous surveys. We show that sensitizing and empowering researchers with the skills works best when the participants apply the skills to real-world problems: project-based learning. Furthermore, we have demonstrated how to implement virtual events in resource-constrained settings by providing Internet and equipment support to participants, thus improving accessibility and diversity.

A mentorship and incubation program using project-based learning to build a professional bioinformatics pipeline in Kenya

The demand for well-trained bioinformaticians to support genomics research continues to rise. Unfortunately, undergraduate training in Kenya does not prepare students for specialization in bioinformatics. Graduates are often unaware of the career opportunities in bioinformatics, and those who are may lack mentors to help them choose a specialization. The Bioinformatics Mentorship and Incubation Program seeks to bridge the gap by laying the foundation for a bioinformatics training pipeline using project-based learning. The program selects six participants through an intensive open recruitment exercise for highly competitive students to join the program for four months. The six interns undergo intensive training within the first one and a half months before being assigned to mini-projects. We track the progress of the interns weekly through code review sessions and a final presentation at the end of the four months. We have trained five cohorts, most of whom have secured master's scholarships within and outside the country and job opportunities. We demonstrate the benefit of structured mentorship using project-based learning in filling the training gap after undergraduate programs to generate well-trained bioinformaticians who are competitive in graduate programs and bioinformatics jobs.

African Centers of Excellence in Bioinformatics and Data Intensive Science: Building Capacity for Enhancing Data Intensive Infectious Diseases Research in Africa

Africa faces both a disproportionate burden of infectious diseases coupled with unmet needs in bioinformatics and data science capabilities which impacts the ability of African biomedical researchers to vigorously pursue research and partner with institutions in other countries. The African Centers of Excellence in Bioinformatics and Data Intensive Science are collaborating with African academic institutions, industry partners, the Foundation for the National Institutes of Health (FNIH) and the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH) in a public-private partnership to address these challenges through enhancing computational infrastructure, fostering the development of advanced bioinformatics and data science skills among local researchers and students and providing innovative emerging technologies for infectious diseases research.

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data

Summary

Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%.

Availability and implementation

QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.