ArticleAlgorithms for molecular biology : AMB2021
Improving metagenomic binning results with overlapped bins using assembly graphs.
Article in Algorithms for molecular biology : AMB, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.Entropy (Basel, Switzerland) · 2025Article
- Landscape of mobile genetic elements and their functional cargo across the gastrointestinal tract microbiomes in ruminants.Microbiome · 2025Article
- Impact of microbiological molecular methodologies on adaptive sampling using nanopore sequencing in metagenomic studies.Environmental microbiome · 2025Article
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- PlasmidEC and gplas2: an optimized short-read approach to predict and reconstruct antibiotic resistance plasmids inMicrobial genomics · 2024Article
- Phables: from fragmented assemblies to high-quality bacteriophage genomes.Bioinformatics (Oxford, England) · 2023Article
- Phables: from fragmented assemblies to high-quality bacteriophage genomes.bioRxiv : the preprint server for biology · 2023Article
- Host interactions of novelMicrobial genomics · 2023Article
- Unitig level assembly graph based metagenome-assembled genome refiner (UGMAGrefiner): A tool to increase completeness and resolution of metagenome-assembled genomes.Computational and structural biotechnology journal · 2023Article
- Constructing metagenome-assembled genomes for almost all components in a real bacterial consortium for binning benchmarking.BMC genomics · 2022Article
- Binning long reads in metagenomics datasets using composition and coverage information.Algorithms for molecular biology : AMB · 2022Article
- Massively parallel single-cell genomics of microbiomes in rice paddies.Frontiers in microbiology · 2022Article
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3 authors.
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Abstract
backgroundMetagenomic sequencing allows us to study the structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for binning contigs only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species).
resultsIn this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.
conclusionGraphBin2 incorporates the coverage information into the assembly graph to refine the binning results obtained from existing binning tools. GraphBin2 also enables the detection of contigs that may belong to multiple species. We show that GraphBin2 outperforms its predecessor GraphBin on both simulated and real datasets. GraphBin2 is freely available at https://github.com/Vini2/GraphBin2 .
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