ArticleBioinformatics (Oxford, England)2022
Metagenomic binning with assembly graph embeddings.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Potential routes of plastics biotransformation involving novel plastizymes revealed by global multi-omic analysis of plastic associated microbes.Scientific reports · 2024Pooled it
- Deployable high-fidelity metagenome binning at scale with QuickBin.Communications biology · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Improving metagenome binning by integrating intrinsic features and taxonomy.Nature biotechnology · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Recovery and microbial host assignment of mobile genetic elements in complex microbiomes: insights from a spiked gut sample.mSystems · 2026Article
- Accurate plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning.Nature biotechnology · 2026Article
- Article
- Elucidating Transitions ofJournal of computational biology : a journal of computational molecular cell biology · 2026Article
- Microbiome Profiling in Chagas Disease: Sample Collection, Sequencing, and Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- Activity-targeted metaproteomics uncovers rare syntrophic bacteria central to anaerobic community metabolism.Nature microbiology · 2025Article
- Machine Learning Enables Alignment-Free Distance Calculation and Phylogenetic Placement Using k-Mer Frequencies.Molecular ecology resources · 2025Article
- Evaluation of metagenome binning: advances and challenges.Briefings in bioinformatics · 2025Article
- Computational Tools and Resources for Long-read Metagenomic Sequencing Using Nanopore and PacBio.Genomics, proteomics & bioinformatics · 2025Review
- A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.Entropy (Basel, Switzerland) · 2025Article
- Genome-resolved long-read sequencing expands known microbial diversity across terrestrial habitats.Nature microbiology · 2025Article
- Myxozoan parasite genomes assembled from contaminated host data reveal extensive gene order conservation and rapid sequence evolution.G3 (Bethesda, Md.) · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationDespite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning.
resultsWe propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning. AVAILABILITY AND IMPLEMENTATION: GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.