{"title":"Efficient TF-IDF method for alignment-free DNA sequence similarity analysis.","authors":"Emre Delibaş","doi":"10.1016/j.jmgm.2025.109011","DOIUrl":null,"url":null,"abstract":"<p><p>This study proposes a pioneering alignment-free approach for the analysis of DNA sequence similarity. The method employs the representation of DNA sequences as n-grams, a technique that involves the adaptation of the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to genomic data. The primary objective of this approach is to enhance the accuracy of the results while concomitantly reducing the computational costs of the process, by ascertaining the most informative n-grams. The approach adopted in this study successfully circumvents the limitations of both traditional alignment-based and alignment-free methods, thereby demonstrating a commendable level of performance. The proposed method was tested on three different datasets and achieved high agreement with reference phylogenetic trees in the AFProject benchmark system. The results demonstrate that TF-IDF-based similarity matrices effectively capture phylogenetic relationships and significantly reduce processing time. The high accuracy rates obtained prove that the method offers a scalable and robust alternative in large genomic datasets. The method demonstrates considerable potential in DNA sequence similarity analysis, exhibiting high accuracy and low computational cost.</p>","PeriodicalId":16361,"journal":{"name":"Journal of molecular graphics & modelling","volume":"137 ","pages":"109011"},"PeriodicalIF":2.7000,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of molecular graphics & modelling","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.jmgm.2025.109011","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposes a pioneering alignment-free approach for the analysis of DNA sequence similarity. The method employs the representation of DNA sequences as n-grams, a technique that involves the adaptation of the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to genomic data. The primary objective of this approach is to enhance the accuracy of the results while concomitantly reducing the computational costs of the process, by ascertaining the most informative n-grams. The approach adopted in this study successfully circumvents the limitations of both traditional alignment-based and alignment-free methods, thereby demonstrating a commendable level of performance. The proposed method was tested on three different datasets and achieved high agreement with reference phylogenetic trees in the AFProject benchmark system. The results demonstrate that TF-IDF-based similarity matrices effectively capture phylogenetic relationships and significantly reduce processing time. The high accuracy rates obtained prove that the method offers a scalable and robust alternative in large genomic datasets. The method demonstrates considerable potential in DNA sequence similarity analysis, exhibiting high accuracy and low computational cost.
期刊介绍:
The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design.
As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.