Computational Molecular Biology 2025, Vol.15, No.6, 273-281 http://bioscipublisher.com/index.php/cmb 279 adapt to specific ecological niches. Phylogenetic relationships may be far apart, but convergent evolution still brings them back together - perhaps because these extreme environments exert similar survival pressures (which is quite evident in homology analysis). So, these pieced together genomes are not merely data; they serve as a window to understand their potential functions and application values. Of course, not all problems can be solved by "competing on genes". Often, no matter how clear the measurement is, it still gets stuck when facing a genome with high GC content, complex structure or dense repetitive sequences. Although existing sequencing and assembly methods are making progress, they still fail to capture certain key variations or low-abundance regions adequately. Some structural information that affects adaptability may leave no trace at all. Moreover, the number of functional genes verified through experiments is too small, making subsequent annotation and interpretation difficult. To break through these blind spots, in addition to the continuous optimization of the hybrid sequencing strategy, the "caliber" of the reference database must also keep up, so as to truly restore the complex and complete genetic blueprint of these microorganisms. If the early genome is a "map", then the next step undoubtedly depends on multiple omics working together. Just having maps is not enough. Real-time dynamic data - such as transcriptional responses, protein expression, and metabolic flow maps - must all be superimposed to see how they respond to environmental stimuli and maintain their homeostasis. The relationships among different "groups" of data are complex and difficult to sort out by intuition. At this time, algorithms such as machine learning become particularly important. They can identify those key variables in the chaos, such as potential biomarkers or certain specific adaptation mechanisms. This entire set of integrated measures is not merely about helping scientific research "see clearly", but also paves the way for the future design of engineered strains with specific functions, and makes the application of extremist microorganisms in industrial or synthetic biology fields more practical and feasible. Acknowledgments We are grateful to colleagues for their critically reading the manuscript and providing valuable feedback that improved the clarity of the text. We also thank to the two anonymous peer reviewers for their revision suggestions on this study. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Arias P., Butler J., Randhawa G., Soltysiak M., Hill K., and Kari L., 2023, Environment and taxonomy shape the genomic signature of prokaryotic extremophiles, Scientific Reports, 13(1): 16105. https://doi.org/10.1101/2023.05.24.542097 Chan A., Sutton G., DePew J., Krishnakumar R., Choi Y., Huang X., Beck E., Harkins D., Kim M., Lesho E., Nikolich M., and Fouts D., 2015, A novel method of consensus pan-chromosome assembly and large-scale comparative analysis reveal the highly flexible pan-genome of Acinetobacter baumannii, Genome Biology, 16(1): 143. https://doi.org/10.1186/s13059-015-0701-6 Chen Z., Erickson D., and Meng J., 2020, Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing, BMC Genomics, 21(1): 631. https://doi.org/10.1186/s12864-020-07041-8 Chklovski A., Parks D., Woodcroft B., and Tyson G., 2022, CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning, Nature Methods, 20(8): 1203-1212. https://doi.org/10.1038/s41592-023-01940-w Cooper Z., Rapp J., Shoemaker A., Anderson R., Zhong Z., and Deming J., 2022, Evolutionary divergence of Marinobacter strains in cryopeg brines as revealed by pangenomics, Frontiers in Microbiology, 13: 879116. https://doi.org/10.3389/fmicb.2022.879116 De Almeida F., De Campos T., and Pappas G., 2023, Scalable and versatile container-based pipelines for de novo genome assembly and bacterial annotation, F1000Research, 12: 1205. https://doi.org/10.12688/f1000research.139488.1 De Maio N., Shaw L., Hubbard A., George S., Sanderson N., Swann J., Wick R., AbuOun M., Stubberfield E., Hoosdally S., Crook D., Peto T., Sheppard A., Bailey M., Read D., Anjum M., Walker A., and Stoesser N., 2019, Comparison of long-read sequencing technologies in the hybrid assembly of complex bacterial genomes, Microbial Genomics, 5(9): e000294. https://doi.org/10.1099/mgen.0.000294
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