Thumbnail Image
Item

Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches

Chasapi, Maria N
; 
Kontis, Nicholas
; 
Lehmann, Robert
; 
Tasneem, Ruqaiya
; 
Patel, Niketan S
; 
Khan, Sumeer A
; 
Martínez de Morentin, Xabier
; 
Chasapi, Iro N
; 
Aplakidou, Eleni
; 
Galaras, Alexandros
... show 9 more
Citations
Google Scholar:
Lookup
Altmetric:
Files
Thumbnail Image
bbag236.pdf
Adobe PDF, 2.5 MB
Author
Chasapi, Maria N
Kontis, Nicholas
Lehmann, Robert
Tasneem, Ruqaiya
Patel, Niketan S
Khan, Sumeer A
Martínez de Morentin, Xabier
Chasapi, Iro N
Aplakidou, Eleni
Galaras, Alexandros
Aldakheel, Lila
Su, Minjing
Baltoumas, Fotis A
Venkateswaran, Kasthuri
Lagani, Vincenzo
Gómez-Cabrero, David
Tegnér, Jesper
Pavlopoulos, Georgios A
Soares Rosado, Alexandre
Supervisor
Department
Computational Biology
Embargo End Date
Type
Journal article
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
Collections
Publications
Show all metadata
Research Projects
Organizational Units
Journal Issue
Abstract
Life thrives in Earth's most inhospitable environments, from boiling hydrothermal vents to hypersaline lakes and frozen polar deserts, thanks to the remarkable adaptations of extremophilic microorganisms. The study of these organisms has rapidly evolved from early cultivation-based discoveries to a data-rich discipline powered by advanced omics technologies. This review comprehensively outlines the current landscape and future directions in extremophile research, emphasizing the pivotal role of bioinformatics, machine learning (ML), and data-driven approaches. We begin by charting the evolution of methodologies, from innovative in situ cultivation techniques and robust biomolecule extraction protocols to modern multi-omics workflows (metagenomics, transcriptomics, proteomics, and metabolomics) that decode the genetic and functional basis of extremophiles. We then catalogue essential bioinformatics resources and specialized databases critical for annotating extremophile genomes and uncovering their unique adaptive strategies, including protein stabilization and syntrophic metabolic relationships. Finally, we explore the transformative potential of artificial intelligence (AI) and ML in overcoming fundamental challenges in the field. These include predicting the functions of uncharacterized "hypothetical" proteins, identifying novel extremozymes, modeling complex genotype-phenotype relationships, and guiding the targeted engineering of industrially relevant strains. By synthesizing insights across these domains, this review highlights how integrating computational biology and AI is poised to unlock the full biotechnological potential of extremophiles and redefine the boundaries of life itself.
Citation
M.N. Chasapi, N. Kontis, R. Lehmann, R. Tasneem, N.S. Patel, S.A. Khan , et al., "Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches," Briefings in Bioinformatics, vol. 27, no. 3, pp. bbag236-, 2026, https://doi.org/10.1093/bib/bbag236.
Source
Briefings in Bioinformatics
Conference
Keywords
31 Biological Sciences, 3101 Biochemistry and Cell Biology, 3102 Bioinformatics and Computational Biology, 3105 Genetics, Computational Biology, Culture Techniques, Environmental Microbiology, Extremophiles, Machine Learning
Subjects
Source
Publisher
Oxford University Press
DOI
10.1093/bib/bbag236
Additional links
https://academic.oup.com/bib/article/27/3/bbag236/8687185
Full-text link