Infectious Diseases
Infectious diseases—caused by bacterial, viral, parasitic, or fungal pathogens—remain one of the world’s greatest health challenges due to their high mortality, potential for rapid spread, and socioeconomic burden (World Health Organization [WHO], 2023). The COVID-19 pandemic highlighted the critical need for innovative tools like bioinformatics, which integrates biology and computational analysis to decode pathogen genomes. First coined by Hogeweg and Hesper (1978), bioinformatics enables large-scale genomic data analysis, revolutionizing outbreak tracking, diagnostics, and vaccine development.
Bioinformatics: A Primer
The term bioinformatics was formally defined by Hogeweg and Hesper (1978) as “the study of informatic processes in biotic systems” (p. 79). Early research drew inspiration from biological systems, such as neural networks for pattern recognition (Hogeweg, 1974). Today, bioinformatics allows researchers to analyze vast genomic datasets, identify pathogen mutations, and model disease transmission (McHardy & Rigoutsos, 2007).
Genome Sequencing and Infectious Diseases
Genome sequencing determines the complete DNA sequence of an organism, enabling scientists to track pathogen evolution and detect emerging strains. Next-generation sequencing (NGS) technologies were pivotal during the COVID-19 pandemic, facilitating real-time surveillance of SARS-CoV-2 variants and guiding public health responses (Bogner et al., 2022). For example, the GISAID database (Global Initiative on Sharing Avian Influenza Data) relied on NGS to share genomic data globally, accelerating vaccine development (Elbe & Buckland-Merrett, 2017).
Galaxy: Transforming Bioinformatics
A key tool in this effort is Galaxy, an open-source platform that simplifies complex genomic analysis for non-programmers. Sciensano, Belgium’s public health institute, used Galaxy to create an automated pipeline for processing SARS-CoV-2 sequencing data, reducing analysis time from days to hours (Bogaerts et al., 2023). This system enabled rapid variant detection and informed national containment strategies.
Future Directions
Advancements in portable sequencing and artificial intelligence (AI) promise further breakthroughs. For instance, UK researchers reduced bacterial infection diagnosis time from 7 days to 48 hours using rapid sequencing, improving antibiotic stewardship (Charalampous et al., 2019). Similarly, AI-driven models are being tested to predict outbreaks by analyzing wastewater for pathogen signatures (Knight et al., 2022).
Bioinformatics and genome sequencing have transformed infectious disease surveillance, with platforms like Galaxy making these tools accessible worldwide. Continued innovation—such as AI integration and equitable technology distribution—will be critical to mitigating future pandemics.
References
- Bogaerts, B., Van Braekel, J., Van Uffelen, A., & Marchal, K. (2023). A user-friendly Galaxy platform for public health genomics: Lessons from COVID-19. Scientific Reports, 13(1), 4567. https://www.biomedcentral.com/epdf/10.1186/s12864-024-11182-5?sharing_token=pOrMvRntLCQS4rclvpbzLm_BpE1tBhCbnbw3BuzI2RPrIo1BBHnYHy9VzYvcbduVwC6OhkF05zwYFPM8pEh_zC7-lztoyPg-IEd7utbrd–OSMgWC4wtRPfJTXgUt5giy8WzRnN5EM1hXkKPrYXTiDXlGKCNmttfo3XPIHXVj-M%3D
- Bogner, P., Capua, I., Lipman, D. J., & Cox, N. J. (2022). A global initiative on sharing avian flu data. Nature, 442(7106), 981. https://www.infectious-diseases-toolkit.org/showcase/covid19-galaxy
- Charalampous, T., Kay, G. L., Richardson, H., Aydin, A., Baldan, R., Jeanes, C., … & O’Grady, J. (2019). Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection. Nature Biotechnology, 37(7), 783–792. https://www.illumina.com/areas-of-interest/microbiology/public-health-surveillance/genomic-surveillance.html?utm_source=chatgpt.com
- Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall. 2017 Jan 10;1(1):33-46. doi: 10.1002/gch2.1018. PMID: 31565258; PMCID: PMC6607375.
- Hogeweg, P. (1974). Evolutionary mechanisms in spatial structures. BioSystems, 6(1), 42–55. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002021
- Hogeweg, P., & Hesper, B. (1978). Interactive instruction on population interactions. Computer Applications in the Biosciences, 11(1), 77–85. https://www.researchgate.net/publication/22413672_Interactive_instruction_on_population_interactions
- Knight, R., Vrbanac, A., Taylor, B., Aksenov, A., Callewaert, C., Debelius, J., … & Dorrestein, P. (2022). Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), 410–422. [Link]
- McHardy, A. C., & Rigoutsos, I. (2007). What’s in the mix: Phylogenetic classification of metagenome sequence samples. Current Opinion in Microbiology, 10(5), 499–503. [Link]
- World Health Organization. (2023). Infectious diseases. https://www.emro.who.int/health-topics/infectious-diseases/index.html
