Graph Theory in DNA Sequencing: Unveiling Genetic Patterns

Abstract

Graph theory, a branch of mathematics that studies the properties and relationships of graphs, has emerged as a powerful tool in addressing the complexities of DNA sequencing. This paper highlights the application of graph theory in DNA sequencing and its implications in various aspects of genomics research. One fundamental concept in graph theory applied to DNA sequencing is the construction of the de Bruijn graph. This graph represents overlapping k-mers, subsequences of length k, as nodes, with edges connecting adjacent k-mers. By constructing and analyzing the de Bruijn graph, researchers can infer the underlying DNA sequence, detect errors, resolve repetitive regions, and identify structural variations in the genome. Graph algorithms such as Overlap path, Eulerian path and Hamiltonian path have been adapted to reconstruct complete DNA sequences from fragmented reads obtained through sequencing. These algorithms leverage the connectivity information present in the de Bruijn graph to traverse and assemble the reads, enabling the reconstruction of long DNA sequences accurately. the application of graph theory in DNA sequencing has revolutionized the field of genomics by providing powerful computational tools for DNA assembly, sequence analysis, and functional annotation. By leveraging graph theory concepts and algorithms, researchers can unravel the intricate information embedded within DNA sequences, leading to deeper insights into the genetic basis of life and its applications in various fields, including medicine, agriculture, and evolutionary biology. Future developments in graph-based algorithms and computational techniques hold promise for further enhancing our ability to unlock the secrets encoded within the vast realm of DNA sequences.