{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:09:30Z","timestamp":1779178170506,"version":"3.51.4"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62303193"],"award-info":[{"award-number":["62303193"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Development Plan Project of Jilin Province, China","award":["20230101064JC"],"award-info":[{"award-number":["20230101064JC"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>DNA contigs binning is necessary to reconstruct metagenome-assembled genomes. Current metagenomic DNA contigs binning methods often leverage coverage profiles across multiple related metagenomes and have demonstrated strong performance on co-assembled contigs. However, in single-sample scenarios where coverage information is rare, their performance drops significantly, limiting the in-depth development of metagenomics at the individual sample level. To address this issue, we propose DCVBin, a novel single-sample metagenomic contigs binning method that incorporates semantic features extracted from a DNA language model. Specifically, our approach continues pretraining on a DNA language model to capture more domain-specific semantic representations, which are then integrated with 4-mer frequencies using a variational autoencoder. Clustering is subsequently performed using the k-means algorithm, in which the number of clusters is determined by single copy genes. Experimental results on six publicly available datasets demonstrate that DCVBin achieves high-accuracy single-sample metagenomic binning and outperforms other state-of-the-art methods. Furthermore, DCVBin is included into a disease diagnostic framework that is evaluated on a cohort of gut metagenomes from people with colorectal cancer and healthy people. The framework is shown to be accurate in predicting colorectal cancer using gut metagenomes and has identified a list of potential microbial biomarkers.<\/jats:p>","DOI":"10.1093\/bib\/bbag241","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T12:09:58Z","timestamp":1777637398000},"source":"Crossref","is-referenced-by-count":0,"title":["DCVBin: a novel binning method for single-sample metagenomes based on DNA language model and variational autoencoder"],"prefix":"10.1093","volume":"27","author":[{"given":"Jingyuan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Qianjin Street No. 3003, 130000, Changchun, Jilin ,","place":["China"]},{"name":"School of Electrical and Information Engineering, Jilin Engineering Normal University , Kaixuan Road No. 3050, 130000, Changchun, Jilin 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