SolidBin: improving metagenome binning with semi-supervised normalized cut | Zendy

Ziye Wang | Zendy; Zhengyang Wang | Zendy; Yang Young Lu | Zendy; Fengzhu Sun | Zendy; Shanfeng Zhu | Zendy

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SolidBin: improving metagenome binning with semi-supervised normalized cut

Author(s) -

Ziye Wang,

Zhengyang Wang,

Yang Young Lu,

Fengzhu Sun,

Shanfeng Zhu

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz253

Subject(s) - contig , metagenomics , cluster analysis , benchmark (surveying) , computer science , rand index , pattern recognition (psychology) , sample (material) , artificial intelligence , similarity (geometry) , data mining , genome , biology , genetics , image (mathematics) , chemistry , geodesy , chromatography , gene , geography

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.

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