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Bioinformatics and Genome Sequencing in the Fight Against Infectious Diseases

April 25, 2025

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
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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.