{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang Z"],"funding":["Shanghai Municipal Science and Technology Major Project","ZJ Lab and Shanghai Center for Brain Science and Brain-Inspired Technology","the 111 Project","National Natural Science Foundation of China"],"pagination":["1"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9817263"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["Binning aims to recover microbial genomes from metagenomic data. For complex metagenomic communities, the available binning methods are far from satisfactory, which usually do not fully use different types of features and important biological knowledge. We developed a novel ensemble binner, MetaBinner, which generates component results with multiple types of features by k-means and uses single-copy gene information for initialization. It then employs a two-stage ensemble strategy based on single-copy genes to integrate the component results efficiently and effectively. Extensive experimental results on three large-scale simulated datasets and one real-world dataset demonstrate that MetaBinner outperforms the state-of-the-art binners significantly."],"journal":["Genome biology"],"pubmed_title":["MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities."],"pmcid":["PMC9817263"],"funding_grant_id":["62272105","B18015","2018SHZDZX01"],"pubmed_authors":["Sun F","You R","Wang Z","Huang P","Zhu S"],"additional_accession":[]},"is_claimable":false,"name":"MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities.","description":"Binning aims to recover microbial genomes from metagenomic data. For complex metagenomic communities, the available binning methods are far from satisfactory, which usually do not fully use different types of features and important biological knowledge. We developed a novel ensemble binner, MetaBinner, which generates component results with multiple types of features by k-means and uses single-copy gene information for initialization. It then employs a two-stage ensemble strategy based on single-copy genes to integrate the component results efficiently and effectively. Extensive experimental results on three large-scale simulated datasets and one real-world dataset demonstrate that MetaBinner outperforms the state-of-the-art binners significantly.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-26T06:00:37.138Z","creation":"2025-04-06T11:42:24.427Z"},"accession":"S-EPMC9817263","cross_references":{"pubmed":["36609515"],"doi":["10.1186/s13059-022-02832-6"]}}