Comparative Review of DNA and RNA Sequence Analysis Tools in Modern Bioinformatics

Sequence analysis lies at the computational core of contemporary molecular biology, translating raw nucleotide reads into biological knowledge that informs genomics, transcriptomics, and precision medicine. The proliferation of next-generation and third-generation sequencing platforms has been matched by an equally rapid expansion of software designed to align, assemble, quantify, and annotate DNA and RNA sequences, leaving researchers with a crowded and often confusing landscape of competing tools. This review provides a critical, comparative examination of the algorithmic foundations, computational performance, and practical trade-offs of the most widely used sequence analysis tools in current bioinformatics practice. DNA-oriented aligners, namely BLAST, BWA, Bowtie2, HISAT2, and Minimap2, are evaluated against criteria of indexing strategy, computational complexity, memory footprint, and suitability for short- versus long-read data. RNA-oriented tools, including STAR, Salmon, Kallisto, StringTie, and Cufflinks, are assessed with respect to alignment-based versus alignment-free quantification paradigms, isoform reconstruction accuracy, and runtime efficiency. The review further synthesizes evidence on genome assembly, variant calling, and functional annotation pipelines, and devotes particular attention to the accelerating integration of deep learning and transformer-based genomic language models, such as DNABERT, the Nucleotide Transformer, and Enformer, into conventional bioinformatics workflows. Persistent challenges, including the scaling of computational infrastructure to petabyte-level genomic datasets, reproducibility across heterogeneous pipelines, and ethical concerns surrounding genomic privacy, are discussed alongside emerging directions in cloud-native bioinformatics, multi-omics integration, and real-time genomic analytics. The review concludes with practical, evidence-based recommendations to guide tool selection according to study design, data modality, and available computational resources, offering a reference point for students and early-career researchers entering computational genomics.