BIOINFORMATICS ALGORITHMS FOR GENOMIC DATA ANALYSIS

The rapid development of DNA sequencing technologies has generated large volumes of genomic data, creating a growing need for efficient computational methods for biological analysis. Bioinformatics algorithms play a significant role in processing, comparing, assembling, and interpreting genomic sequences. These algorithms support important tasks such as sequence alignment, genome assembly, variant detection, gene identification, and comparative genomics. This paper presents a narrative review of major algorithmic approaches used in genomic data analysis, focusing on their underlying principles, applications, advantages, and limitations. It discusses dynamic programming, heuristic sequence alignment, indexing structures, graph-based genome assembly, variant-calling algorithms, and comparative sequence analysis. The review also examines challenges associated with computational complexity, sequencing errors, repetitive genomic regions, data storage, and algorithm scalability. Emerging approaches, including long-read sequencing analysis, pangenome graphs, parallel computing, and machine-learning-assisted genomic interpretation, are discussed as promising research directions. The paper highlights the importance of selecting appropriate algorithms according to data characteristics, computational resources, and analytical objectives. Continued algorithmic development is essential for improving the accuracy, efficiency, and scalability of genomic data analysis.