A survey of k-mer methods and applications in bioinformatics.

Autor:	Moeckel C; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA., Mareboina M; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA., Konnaris MA; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA., Chan CSY; Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA., Mouratidis I; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA.; Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA., Montgomery A; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA., Chantzi N; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA., Pavlopoulos GA; Institute for Fundamental Biomedical Research, BSRC 'Alexander Fleming', Vari 16672, Greece., Georgakopoulos-Soares I; Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA.; Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 May 21; Vol. 23, pp. 2289-2303. Date of Electronic Publication: 2024 May 21 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2024.05.025
Abstrakt:	The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research. Competing Interests: All authors declare that they have no conflicts of interest. (© 2024 The Authors.)
Databáze:	MEDLINE
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