COVID-19 pandemic: Lessons learned for undergraduate research training.

This article reports a session from the virtual international 2021 IUBMB/ASBMB workshop, "Teaching Science on Big Data." The awareness of using publicly available research data sets for undergraduate training is low in certain parts of the world. Final year projects always revolve around wet-lab based projects. The challenges occur during COVID-19 pandemic when it forces full lockdown to the nation, but at the same time faculty members need to provide consistent training to the students and projects to work with. We aim to identify supervisors in the faculty that are ready to convert their proposed project from wet-lab to an online-based project. As coordinators of the course we created an online survey to identify projects that can be converted into dry-lab/online projects. Our surveys identified only 32.5% projects implemented dry-lab/online based projects. Most academicians described that they are not ready or familiar to apply changes for their research design. With the unknown future of the world living with COVID-19 and directional changes of life science research toward big data driven research indeed we should be ready to adopt such changes. Awareness on reusing public data sets as tools for research should be provided to strengthen undergraduate training. Life science undergraduates should be exposed to reusing public data sets as these materials are readily available case studies that allow in depth exploration to answer specific research questions. Members of the faculty should take part to pave the way for them, ensuring that they understand that life science research revolves around a multidisciplinary field.

The Corona Virus Disease 2019 (COVID‐19) pandemic has significantly influenced the global education system due to the implementation of the movement restriction order or in certain parts of the world where the full lockdown was imposed. It imposed significant burdens on the universities as teaching and learning sessions had to be performed in an unusual condition where accessible internet connections and electronic and computer devices were ultimately essential tools in order to allow lecturers to teach and reach students for assessment and making sure students achieve their learning goals. The biggest obstacle was to ensure undergraduate research training without jeopardizing the quality of research. The Biomedical Science Undergraduate Program, under the Faculty of Medicine, University of Malaya, Malaysia has also taken part in providing consistent training of students through projects adapting to the pandemic situation.

Biomedical science research training during pre‐COVID‐19 era was traditional lab‐based research. The guidelines follow the requirements of accreditation bodies such as the Malaysia Qualifications Agency (MQA) and Malaysian Allied Health Professions Act. Every year, students will have to complete their final year research project and report their findings in written dissertation form. Amongst key challenges identified during COVID‐19 were the uncontrolled situations caused by the sudden restrictions of movements by the government. Undergraduates were prohibited from attending physical training and performing laboratory work jeopardizing their research projects. Nevertheless, restrictions should never be an excuse, as training must continue regardless of the situation. The Coordinators for Biomedical Science Research Training courses were challenged to offer research projects. At the start, most faculty members were not ready, and some believed that research output could only be gained through wet‐lab based research.

To ensure appropriate adaptations we implemented alternative guidelines for “Online‐Biomedical Science Research projects” suggested by University of Leeds. Such projects can be on systematic review and meta‐analysis, analysis of previously collected data from supervisors or research teams, mathematical simulation/computational/ molecular modeling, data mining by using all public access to online databases (genomic, transcriptomic), and focused literature review. We surveyed all 40 potential final‐year project supervisors in the faculty and identified 32.5% (13 out of 40) lecturers who agreed to include online components in projects offered. Six supervisors agreed to offer full‐time online projects, which consist of systematic review, meta‐analysis, utilizing GEO data sets for data mining and in silico molecular modeling. Seven projects proposed as a hybrid‐project utilizing The Cancer Genome Atlas (TCGA) and GEO data sets data mining performed on the first semester followed by simple gene expression wet‐lab experiments in the second semester. However, 27 projects still rely on full‐time wet lab experiments.

Challenges occur in research focuses on data mining from gene expression data sets where students have no basic bioinformatics or programming and are not clear about its applications for biomedical research. Supervisors face similar issues, as this is their first time implementing such an online‐based project. To solve these issues, identifying suitable graphical user interface (GUI) software would remove the need to juggle with programming. Interestingly, online free software is available to analyze these data sets namely Networkanalyst.ca 3.0 and it performs as good as proprietary software. These data can easily be uploaded and analyzed in a systematic manner. Specific aspect of gene expression, pathway, and network analysis associated tools are summarized in Table 1.

TABLE 1

Suitable GUI‐based tools to analyze gene expression data sets for undergraduate online research training during COVID‐19 pandemic

Aspect of research	Informatics tools
Gene expression data mining	GEO data sets 4 (https://www.ncbi.nlm.nih.gov/geo/) TCGA data sets 5 (https://portal.gdc.cancer.gov/)
Identification of differentially expressed genes	Networkanalyst.ca 3.0 6 (https://www.networkanalyst.ca/)
Pathway analysis	WebGestalt 7 (http://www.webgestalt.org/)
Gene‐sets and pathway enrichment analysis	GSEA‐MsigDB 8 (https://www.gsea‐msigdb.org/gsea/msigdb/)
Identification of key hub genes	MCODE 9 and Cytohubba 10 from Cytoscape ver 3.8.2 (https://cytoscape.org/)
Mirna‐mrna network interaction	Mirnet.ca 11 (https://www.mirnet.ca/)
In silico validation of cancer‐related genes	GEPIA 12 or UALCAN server 13 (http://gepia.cancer‐pku.cn/) (http://ualcan.path.uab.edu/analysis.html)
Primers design	NCBI‐primer BLAST 14 (https://www.ncbi.nlm.nih.gov/tools/primer‐blast/)

GEO data sets

TCGA data sets

Networkanalyst.ca 3.0

WebGestalt

GSEA‐MsigDB

MCODE and Cytohubba from Cytoscape ver 3.8.2

Mirnet.ca

GEPIA or UALCAN server

NCBI‐primer BLAST

Lessons learned from implementing online research are the enormous amount of unique high throughput gene expression data sets based on diseases allowed students to dissect and understand many different aspects of the molecular basis of diseases in an integrative manner.

Students will be able to analyze and visualize the information in a systematic approach. This generates interesting case studies. Furthermore, biochemistry, physiology, and immunology are seen as integral parts of disease mechanisms. Secondly, utilizing these data sets allows students to appreciate the nature of omics technology and big data‐driven research. Thirdly, we realized that hybrid research, especially those relying on bioinformatics tools is a flexible and dynamic way of performing biomedical research as it is part of discovery and nurtures hypothesis‐driven research and experimentation. Lastly, we realized that it is very critical for our biomedical science students to grasp basic bioinformatics to comprehend future needs in this digital era. Therefore, our department has decided to introduce a “Bioinformatics for Biomedical Sciences” course for the upcoming new cohort of students. This is to ensure our future graduates will be able to work with interdisciplinary teams such as pharmaceutical industries and healthcare informatics agencies.

CONFLICT OF INTEREST

All authors have no conflict of interest.