Bioinformatics techniques for comparative genomic analysis
Bipin Sulebipin.sule@vit.eduVishwakarma Institute of TechnologySavitribai Phule Pune UniversityPune, Maharashtra, 411037, IndiaView full profile → , *Mahendra AlateCorresponding authormahendra.alate@gmail.comKrishna Vishwa Vidyapeeth (Deemed to be University)Krishna Institute of Medical SciencesKarad, Maharashtra, 415539, IndiaView full profile → , Uma Bhardwajvc@niu.edu.inDepartment of Biotechnology and MicrobiologyNoida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , Kavita Prasadkavita@rungta.orgDepartment of Computer Science and EngineeringRungta College of Engineering and TechnologyRungta International Skills UniversityBhilai, Chhattisgarh, 490024, IndiaView full profile → , Vinay Shivajirao Nalawadevinaynalawade2007@gmail.comDepartment of Artificial Intelligence & Data Science EngineeringS B Patil College of EngineeringIndapur, Maharashtra, 413106, IndiaView full profile → , Sorabh Sharmasorabh.sharma.orp@chitkara.edu.inCentre for Research Impact and Outcome (CRIO)Chitkara UniversityRajpura, Punjab, 140401, IndiaView full profile → , Brijeshkumar M. Patelbmpatel.me.hcet@gokuluniversity.ac.inFaculty of EngineeringGokul Global UniversitySidhpur, Gujarat, 384151, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 27 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIM-2825
- Pages:
- 3135–3148
Abstract
Comparative genomic analysis is hindered by the ability to identify conserved regions, variations in evolution, and functional differences in the genome in large and heterogeneous sequence datasets. In this work, an integrated bioinformatics solution for the systematic genome comparison using BLAST sequence alignment, Multiple Sequence Alignment (MSA) detection of SNPs, phylogenetic analysis and gene annotation is proposed. Pre-processing, alignment, annotation and evaluation of genomic sequences were performed by applying sequence identity, alignment coverage, variant detection rate, computational time and classification accuracy as criteria for analysis. The proposed approach yielded a sequence-matching accuracy of 96.4%, alignment coverage of 94.8% and variant-detection accuracy of 93.7% with overall analytical accuracy of about 5.6% higher than conventional alignment-based analysis. Comparative evaluation showed better results in terms of sensitivity, specificity, computational efficiency and evolutionary relationship identification. It’s novel because it’s a single analytical pipeline that combines complementary genomic approaches. This approach provides a scalable, accurate method to identify genomic similarities and differences and evolutionary relationships, aiding in the accurate comparative genomics and biological interpretation.
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References
[1] D. Liu, J. Kuo, and C.-H. Lin, “Comparative genomic analysis of extracellular electron transfer in bacteria,” Processes, vol. 12, no. 12, Art. no. 2636 (2024), doi: 10.3390/pr12122636.
[2] H. Bono, “Recent advances in genome editing and bioinformatics: Addressing challenges in genome editing implementation and genome sequencing,” International Journal of Molecular Sciences, vol. 26, no. 7, Art. no. 3442 (2025), doi: 10.3390/ijms26073442.
[3] L. Yang, Y. Zhu, Z. Peng, Y. Ding, K. Jie, Z. Wang, Y. Peng, X. Tang, X. Wang, H. Chen, and C. Tan, “Comparative genome analysis of a pathogenic Erysipelothrix rhusiopathiae isolate WH13013 from pig reveals potential genes involve in bacterial adaptions and pathogenesis,” Veterinary Sciences, vol. 7, no. 2, Art. no. 74 (2020), doi: 10.3390/vetsci7020074.
[4] Z. Boxonov, “Global dynamics of a discrete mosquito model with Allee effect,” Journal of Interdisciplinary Mathematics, vol. 29, no. 7, pp. 2133–2157 (2026), doi: 10.47974/JIM-2273.
[5] M. S. Basha, K. R. Prasad, S. K. Mouleeswaran, R. C. Poonia, and S. Sebastian, “Multi-disease detection system with X-ray images using deep learning techniques,” Journal of Information and Optimization Sciences, vol. 45, no. 5, pp. 1379–1388 (2024), doi: 10.47974/JIOS-1710.
[6] S.-S. Kim, “Application of comparative genomics for the development of PCR primers for the detection of harmful or beneficial microorganisms in food: Mini-review,” Foods, vol. 14, no. 6, Art. no. 1060 (2025), doi: 10.3390/foods14061060.
[7] S. Nambiar, M. Tatiya, K. S. Diwate, D. Sharma, S. N. Ajani, and Y. Nagargoje, “Privacy-preserving machine learning techniques ensuring data confidentiality and model accuracy in federated learning environments,” Journal of Information and Optimization Sciences, vol. 47, no. 5-B, pp. 1887–1895 (2026), doi: 10.47974/JIOS-2281.
[8] V. Baev, “Bioinformatics research in bacterial genomics and metagenomics,” Current Issues in Molecular Biology, vol. 47, no. 4, Art. no. 258 (2025), doi: 10.3390/cimb47040258.
[9] A. Glykeria-Myrto, S. Theodora, T. Vasileios, B. Loulouda, and M. Marios, “Comparative genomic analysis and antimicrobial resistance profile of Enterococcus strains isolated from raw sheep milk,” Veterinary Sciences, vol. 12, no. 8, Art. no. 685 (2025), doi: 10.3390/vetsci12080685.
[10] D. P. Bohada-Lizarazo, K. D. Bravo-Sanabria, P. Cárdenas-Malpica, and R. Rodríguez, “Comparative genomic analysis of Mycobacterium tuberculosis isolates circulating in North Santander, Colombia,” Tropical Medicine and Infectious Disease, vol. 9, no. 9, Art. no. 197 (2024), doi: 10.3390/tropicalmed9090197.
[11] I. Domingues and J. A. M. Santos, “Bioinformatics tools, databases, and data formats: A functional overview for introductory sequence-based and NGS workflows,” Applied Sciences, vol. 16, no. 13, Art. no. 6666 (2026), doi: 10.3390/app16136666.
[12] A. J. Clark and J. W. Lillard, Jr., “A comprehensive review of bioinformatics tools for genomic biomarker discovery driving precision oncology,” Genes, vol. 15, no. 8, Art. no. 1036 (2024), doi: 10.3390/genes15081036.
