{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:46:24Z","timestamp":1781001984062,"version":"3.54.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2019,4,12]],"date-time":"2019-04-12T00:00:00Z","timestamp":1555027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572139"],"award-info":[{"award-number":["61572139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872094"],"award-info":[{"award-number":["61872094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]},{"name":"Shanghai Municipal Science and Technology Major","award":["B18015"],"award-info":[{"award-number":["B18015"]}]},{"name":"Shanghai Science & Technology","award":["16JC1420402"],"award-info":[{"award-number":["16JC1420402"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM120624"],"award-info":[{"award-number":["R01GM120624"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R01GM131407"],"award-info":[{"award-number":["1R01GM131407"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Metagenomic contig binning is an important computational problem in metagenomic research, which aims to cluster contigs from the same genome into the same group. Unlike classical clustering problem, contig binning can utilize known relationships among some of the contigs or the taxonomic identity of some contigs. However, the current state-of-the-art contig binning methods do not make full use of the additional biological information except the coverage and sequence composition of the contigs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a novel contig binning method, Semi-supervised Spectral Normalized Cut for Binning (SolidBin), based on semi-supervised spectral clustering. Using sequence feature similarity and\/or additional biological information, such as the reliable taxonomy assignments of some contigs, SolidBin constructs two types of prior information: must-link and cannot-link constraints. Must-link constraints mean that the pair of contigs should be clustered into the same group, while cannot-link constraints mean that the pair of contigs should be clustered in different groups. These constraints are then integrated into a classical spectral clustering approach, normalized cut, for improved contig binning. The performance of SolidBin is compared with five state-of-the-art genome binners, CONCOCT, COCACOLA, MaxBin, MetaBAT and BMC3C on five next-generation sequencing benchmark datasets including simulated multi- and single-sample datasets and real multi-sample datasets. The experimental results show that, SolidBin has achieved the best performance in terms of F-score, Adjusted Rand Index and Normalized Mutual Information, especially while using the real datasets and the single-sample dataset.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/sufforest\/SolidBin.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz253","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T21:11:36Z","timestamp":1554498696000},"page":"4229-4238","source":"Crossref","is-referenced-by-count":72,"title":["SolidBin: improving metagenome binning with semi-supervised normalized cut"],"prefix":"10.1093","volume":"35","author":[{"given":"Ziye","family":"Wang","sequence":"first","affiliation":[{"name":"Centre for Computational Systems Biology, School of Mathematical Sciences , Shanghai, China"},{"name":"School of Computer Science and Shanghai Key Lab of Intelligent Information Processing , Shanghai, China"},{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Shanghai Key Lab of Intelligent Information Processing , Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang Young","family":"Lu","sequence":"additional","affiliation":[{"name":"Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengzhu","family":"Sun","sequence":"additional","affiliation":[{"name":"Centre for Computational Systems Biology, School of Mathematical Sciences , Shanghai, China"},{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai, China"},{"name":"Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California , Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Shanghai Key Lab of Intelligent Information Processing , Shanghai, China"},{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,4,12]]},"reference":[{"key":"2023062712450313600_btz253-B1","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1038\/nmeth.3103","article-title":"Binning metagenomic contigs by coverage and composition","volume":"11","author":"Alneberg","year":"2014","journal-title":"Nat. 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