Volume 8 Number 1 2025
Bioinformatics in Microbiology: Reviewing the role of bioinformatics in studying microbial genomics, metagenomics, and phylogenetics
Md Abu Bakar Siddique1*, Asim Debnath2, Nabil Deb Nath3
Microbial Bioactives 8 (1) 1-8 https://doi.org/10.25163/microbbioacts.8110414
Submitted: 29 April 2025 Revised: 14 May 2025 Accepted: 18 May 2025 Published: 22 May 2025
Abstract
Microorganisms are the unseen architects of life on Earth—driving evolution, shaping ecosystems, and influencing human health in profound ways. Yet, only with the rise of bioinformatics have we begun to truly understand their hidden world. This systematic review explores how the fusion of biology and computational science has transformed microbiology, allowing us to read, interpret, and connect the vast genomic stories written within microbial DNA. Through genome sequencing, metagenomic exploration, and phylogenetic modeling, bioinformatics provides a window into microbial diversity and evolution that traditional tools could never offer. It helps scientists uncover the genetic foundations of infectious diseases, trace microbial ancestry across time, and predict emerging resistance patterns that threaten global health. Beyond laboratories, these tools enable the study of entire microbial communities in their natural environments, revealing the intricate symbioses that sustain life—from soil ecosystems to the human gut. Bioinformatics is not just a method but a bridge—linking molecular biology, ecology, and data science in a shared pursuit of understanding how microbes shape the biosphere. As computing power grows and algorithms evolve, this interdisciplinary partnership continues to unravel the mysteries of microbial life, paving the way for breakthroughs in medicine, agriculture, and environmental stewardship.
Keywords: Bioinformatics, Microbial Genomics, Metagenomics, Phylogenetics, Microbial Diversity, Computational Biology, Evolutionary Microbiology
References
Afiahayati, S., Sato, K., & Sakakibara, Y. (2015). MetaVelvet-SL: An extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Research, 22(1), 69–77. https://doi.org/10.1093/dnares/dsu041
Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., … Zagnitko, O. (2008). The RAST server: Rapid annotations using subsystems technology. BMC Genomics, 9(1), 75. https://doi.org/10.1186/1471-2164-9-75
Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., … Minimum Information About a Microarray Experiment (MIAME)-Toward Standards for Microarray Data. (2001). Nature Genetics, 29(4), 365–371. https://doi.org/10.1038/ng1201-365
Cowan, D. A., Arslanoglu, A., Burton, S. G., Baker, G. C., Cameron, R. A., Smith, J. J., & Meyer, Q. (2004). Metagenomics, gene discovery and the ideal biocatalyst. Biochemical Society Transactions, 32(2), 298–302. https://doi.org/10.1042/bst0320298
Curtis, T. P., Sloan, W. T., & Scannell, J. W. (2002). Estimating prokaryotic diversity and its limits. Proceedings of the National Academy of Sciences, 99(16), 10494–10499. https://doi.org/10.1073/pnas.142680199
Delcher, A. L., Bratke, K. A., Powers, E. C., & Salzberg, S. L. (2007). Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics, 23(6), 673–679. https://doi.org/10.1093/bioinformatics/btm009
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., … D'Eustachio, P. (2016). The Reactome pathway knowledgebase. Nucleic Acids Research, 44(D1), D481–D487. https://doi.org/10.1093/nar/gkv1351
Fleischmann, R. D., Adams, M. D., White, O., Clayton, R. A., Kirkness, E. F., Kerlavage, A. R., … Venter, J. C. (1995). Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 269(5223), 496–512. https://doi.org/10.1126/science.7542800
Gabaldón, T. (2008). Comparative genomics-based prediction of protein function. In Genomics Protocols (pp. 387–401). https://doi.org/10.1007/978-1-59745-188-8_26
Glass, E. M., Wilkening, J., Wilke, A., Antonopoulos, D., & Meyer, F. (2010). Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. Cold Spring Harbor Protocols, 2010(ANL/MCS/JA-65695). https://doi.org/10.1101/pdb.prot5368
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016). Coming of age: Ten years of next-generation sequencing technologies. Nature Reviews Genetics, 17, 333–351. https://doi.org/10.1038/nrg.2016.49
Hogeweg, P. (2011). The roots of bioinformatics in theoretical biology. PLoS Computational Biology, 7(3), e1002021. https://doi.org/10.1371/journal.pcbi.1002021
Jones, D. T. (2000). Protein structure prediction in the postgenomic era. Current Opinion in Structural Biology, 10(3), 371–379. https://doi.org/10.1016/S0959-440X(00)00099-3
Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., … Yamanishi, Y. (2007). KEGG for linking genomes to life and the environment. Nucleic Acids Research, 36(suppl_1), D480–D484. https://doi.org/10.1093/nar/gkm882
Katsila, T., Patrinos, G. P., & Mitropoulou, C. (2016). Pharmacogenomics and pharmacogenetics of personalized medicine: Recent developments and future challenges. In Personalized Medicine (pp. 43–57). Elsevier.
Khanna, V. K. (2007). Existing and emerging detection technologies for DNA (Deoxyribonucleic Acid) fingerprinting, sequencing, bio- and analytical chips: A multidisciplinary development unifying molecular biology, chemical and electronics engineering. Biotechnology Advances, 25(1), 85–98. https://doi.org/10.1016/j.biotechadv.2006.10.003
Kitano, H. (2002). Systems biology: A brief overview. Science, 295(5560), 1662–1664. https://doi.org/10.1126/science.1069492
Lamb, J. (2007). The Connectivity Map: A new tool for biomedical research. Nature Reviews Cancer, 7(1), 54–60. https://doi.org/10.1038/nrc2044
Laskowski, R. A., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (2012). PROCHECK: A program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26(2), 283–291. https://doi.org/10.1107/S0021889892009944
Le Novère, N. (2015). Quantitative and logic modelling of molecular and gene networks. Nature Reviews Genetics, 16(3), 146–158. https://doi.org/10.1038/nrg3885
Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., … Jin, W. (2013). Comparison of next-generation sequencing systems. Journal of Biomedicine and Biotechnology, 2012, 251364. https://doi.org/10.1155/2012/251364
Liu, M. Y., Kjelleberg, S., & Thomas, T. (2011). Functional genomic analysis of an uncultured δ-proteobacterium in the sponge Cymbastela concentrica. The ISME Journal, 5(3), 427–435. https://doi.org/10.1038/ismej.2010.139
Loman, N. J., & Watson, M. (2015). Successful test launch for nanopore sequencing. Nature Methods, 12, 303–304. https://doi.org/10.1038/nmeth.3327
Lomsadze, A., Gemayel, K., Tang, S., & Borodovsky, M. (2018). Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes. Genome Research, 28(7), 1079–1089. https://doi.org/10.1101/gr.230615.117
Markowitz, V. M., Ivanova, N. N., Szeto, E., Palaniappan, K., Chu, K., Dalevi, D., … Kyrpides, N. C. (2007). IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Research, 36(suppl_1), D534–D538. https://doi.org/10.1093/nar/gkm869
Mitchell, A., Chang, H. Y., Daugherty, L., Fraser, M., Hunter, S., Lopez, R., … et al. (2015). The InterPro protein families database: The classification resource after 15 years. Nucleic Acids Research, 43, D213–D221. https://doi.org/10.1093/nar/gku1243
Nagarajan, N., & Pop, M. (2013). Sequence assembly demystified. Nature Reviews Genetics, 14, 157–167. https://doi.org/10.1038/nrg3367
Nakashima, N., Mitani, Y., & Tamura, T. (2005). Actinomycetes as host cells for production of recombinant proteins. Microbial Cell Factories, 4(1), 7. https://doi.org/10.1186/1475-2859-4-7
Noguchi, H., Park, J., & Takagi, T. (2006). MetaGene: Prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Research, 34(19), 5623–5630. https://doi.org/10.1093/nar/gkl723
Ouzounis, C. (2002). Bioinformatics and the theoretical foundations of molecular biology. Bioinformatics, 18(3), 377–378. https://doi.org/10.1093/bioinformatics/18.3.377
Ouzounis, C. A. (2012). Rise and demise of bioinformatics? Promise and progress. PLoS Computational Biology, 8(7), e1002487. https://doi.org/10.1371/journal.pcbi.1002487
Peng, Y., Leung, H. C., Yiu, S. M., & Chin, F. Y. (2012). IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics, 28(11), 1420–1428. https://doi.org/10.1093/bioinformatics/bts174
Roehe, R., Dewhurst, R. J., Duthie, C. A., Rooke, J. A., McKain, N., Ross, D. W., … Wallace, R. J. (2016). Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane-emitting and efficiently feed-converting hosts based on metagenomic gene abundance. PLoS Genetics, 12(2), e1005846. https://doi.org/10.1371/journal.pgen.1005846
Sharma, B., & Shukla, P. (2020). Designing synthetic microbial communities for effectual bioremediation: A review. Biocatalysis and Biotransformation, 38(6), 405–414. https://doi.org/10.1080/10242422.2020.1813727
Shendure, J., & Ji, H. (2008). Next-generation DNA sequencing. Nature Biotechnology, 26(10), 1135–1145. https://doi.org/10.1038/nbt1486
Sunagawa, S., Coelho, L. P., Chaffron, S., Kultima, J. R., Labadie, K., Salazar, G., … et al. (2015). Structure and function of the global ocean microbiome. Science, 348, 1261359. https://doi.org/10.1126/science.1261359
Treangen, T. J., Koren, S., Sommer, D. D., Liu, B., Astrovskaya, I., Ondov, B., … Pop, M. (2013). MetAMOS: A modular and open source metagenomic assembly and analysis pipeline. Genome Biology, 14, R2. https://doi.org/10.1186/gb-2013-14-1-r2
Varshney, R. K., Nayak, S. N., May, G. D., & Jackson, S. A. (2018). Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends in Biotechnology, 26(9), 522–530. https://doi.org/10.1016/j.tibtech.2009.05.006
Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., … Smith, H. O. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science, 304(5667), 66–74. https://doi.org/10.1126/science.1093857
Wang, Q., Fish, J. A., Gilman, M., Sun, Y., Brown, C. T., Tiedje, J. M., & Cole, J. R. (2015). Xander: Employing a novel method for efficient gene-targeted metagenomic assembly. Microbiome, 3, 9. https://doi.org/10.1186/s40168-015-0093-6
Watson, M. (2014). Illuminating the future of DNA sequencing. Genome Biology, 15, 165. https://doi.org/10.1186/gb4165
Zerbino, D. R., & Birney, E. (2008). Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Research, 18(5), 821–829. https://doi.org/10.1101/gr.074492.107