{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T06:55:51Z","timestamp":1775976951125,"version":"3.50.1"},"posted":{"date-parts":[[2024,12,11]]},"group-title":"Genomics","reference-count":29,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T00:00:00Z","timestamp":1733875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>The rapid increase in nucleotide sequence data generated by next-generation sequencing (NGS) technologies demands efficient computational tools for sequence comparison. Alignment-based methods, such as BLAST, are increasingly overwhelmed by the scale of contemporary datasets due to their high computational demands for classification. This study evaluates alignment-free (AF) methods as scalable and rapid alternatives for viral sequence classification, focusing on identifying techniques that maintain high accuracy and efficiency when applied to extremely large datasets.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We employed six established AF techniques to extract feature vectors from viral genomes, which were subsequently used to train Random Forest classifiers. Our primary dataset comprises 297,186 SARS-CoV-2 nucleotide sequences, categorized into 3502 distinct lineages. Furthermore, we validated our models using dengue and HIV sequences to demonstrate robustness across different viral datasets. Our AF classifiers achieved 97.8% accuracy on the SARS-CoV-2 test set, and 99.8% and 89.1% accuracy on dengue and HIV test sets, respectively.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Despite the high-class dimensionality, we show that word-based AF methods effectively represent viral sequences. Our study highlights the practical advantages of AF techniques, including significantly faster processing compared to alignment-based methods and the ability to classify sequences using modest computational resources.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2024.12.10.627186","type":"posted-content","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T22:51:24Z","timestamp":1734043884000},"source":"Crossref","is-referenced-by-count":1,"title":["Alignment-Free Viral Sequence Classification at Scale"],"prefix":"10.64898","author":[{"given":"Daniel J.","family":"van Zyl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Dunaiski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houriiyah","family":"Tegally","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheryl","family":"Baxter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The INFORM Africa research study group","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tulio","family":"de Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joicymara S.","family":"Xavier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024121405102955000_2024.12.10.627186v1.1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-017-1319-7"},{"key":"2024121405102955000_2024.12.10.627186v1.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.87.6.2264"},{"key":"2024121405102955000_2024.12.10.627186v1.3","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1990.9999"},{"key":"2024121405102955000_2024.12.10.627186v1.4","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbt072"},{"key":"2024121405102955000_2024.12.10.627186v1.5","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2018.0239"},{"key":"2024121405102955000_2024.12.10.627186v1.6","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-016-0997-x"},{"key":"2024121405102955000_2024.12.10.627186v1.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ympev.2012.07.003"},{"key":"2024121405102955000_2024.12.10.627186v1.8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu177"},{"key":"2024121405102955000_2024.12.10.627186v1.9","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab011"},{"key":"2024121405102955000_2024.12.10.627186v1.10","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-019-1755-7"},{"key":"2024121405102955000_2024.12.10.627186v1.11","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0813249106"},{"key":"2024121405102955000_2024.12.10.627186v1.12","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2148-7-41"},{"key":"2024121405102955000_2024.12.10.627186v1.13","first-page":"370","article-title":"Base-Base Correlation a Novel Sequence Feature and its Applications","volume":"2007","year":"2007","journal-title":"1st International Conference on Bioinformatics and Biomedical Engineering"},{"key":"2024121405102955000_2024.12.10.627186v1.14","first-page":"421","article-title":"Information Theoretic Distance Measures in Phylogenomics","volume":"2007","year":"2007","journal-title":"Information Theory and Applications Workshop"},{"key":"2024121405102955000_2024.12.10.627186v1.15","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.2.149"},{"key":"2024121405102955000_2024.12.10.627186v1.16","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg295"},{"key":"2024121405102955000_2024.12.10.627186v1.17","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2006.13.336"},{"key":"2024121405102955000_2024.12.10.627186v1.18","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu331"},{"key":"2024121405102955000_2024.12.10.627186v1.19","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2009.0106"},{"key":"2024121405102955000_2024.12.10.627186v1.20","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/18.8.2163"},{"key":"2024121405102955000_2024.12.10.627186v1.21","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-5571-y"},{"key":"2024121405102955000_2024.12.10.627186v1.22","doi-asserted-by":"publisher","DOI":"10.2174\/1389202923666211221110857"},{"key":"2024121405102955000_2024.12.10.627186v1.23","unstructured":"Storcheus D , Rostamizadeh A , Kumar S. A Survey of Modern Questions and Challenges in Feature Extraction. In: Storcheus D , Rostamizadeh A , Kumar S , editors. Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015. vol. 44 of Proceedings of Machine Learning Research. Montreal, Canada: PMLR; 2015. p. 1\u201318. 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