Decoding Genetic Alleles: A Computational Exploration of Bioinformatics Algorithms

Shaurya Chandna

Abstract: This paper will discuss how computational algorithms are relevant in bioinformatics and especially in the decoding and interpretation of genetic alleles. It contrasts the different methods including the sequence alignment, hidden Markov models and machine learning methods and compares their performance in the detection of alleles, the annotation, and the analysis of variants. It has a focus on practical use in genomics, personalized medicine and evolutionary biology. The results show that the current bioinformatics algorithms have revolutionized the study of genetics because they allow accurate determination of alleles to be identified with large volumes of data never before. Through examining the various computational strategies, this research paper demonstrates that combination of multiple strategies is better than single ones in terms of variant detection and functional interpretation.

Keywords: bioinformatics algorithms, genetic allele analysis, machine learning in genomics, personalized medicine, computational biology

How to Cite?: Shaurya Chandna, "Decoding Genetic Alleles: A Computational Exploration of Bioinformatics Algorithms", Volume 14 Issue 10, October 2025, International Journal of Science and Research (IJSR), Pages: 1540-1549, https://www.ijsr.net/getabstract.php?paperid=SR251027201800, DOI: https://dx.doi.org/10.21275/SR251027201800