ArticleBioinformatics (Oxford, England)2020
MetaBCC-LR: metagenomics binning by coverage and composition for long reads.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- DecBin: Bayesian-optimized deep clustering for long-read metagenomic binning.Briefings in bioinformatics · 2026Article
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Computational Tools and Resources for Long-read Metagenomic Sequencing Using Nanopore and PacBio.Genomics, proteomics & bioinformatics · 2025Review
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- METASEED: a novel approach to full-length 16S rRNA gene reconstruction from short read data.BMC bioinformatics · 2024Article
- Unveiling microbial diversity: harnessing long-read sequencing technology.Nature methods · 2024Review
- Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.Nature communications · 2024Article
- Unraveling metagenomics through long-read sequencing: a comprehensive review.Journal of translational medicine · 2024Review
- MarkerScan: Separation and assembly of cobionts sequenced alongside target species in biodiversity genomics projects.Wellcome open research · 2024Article
- SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing.Bioinformatics (Oxford, England) · 2023Article
- Biological data studies, scale-up the potential with machine learning.European journal of human genetics : EJHG · 2023Article
- A novel and diverse group of Candidatus Patescibacteria from bathypelagic Lake Baikal revealed through long-read metagenomics.Environmental microbiome · 2023Article
- Recovery and Analysis of Long-Read Metagenome-Assembled Genomes.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Metagenomic binning with assembly graph embeddings.Bioinformatics (Oxford, England) · 2022Article
- Binning long reads in metagenomics datasets using composition and coverage information.Algorithms for molecular biology : AMB · 2022Article
- Article
- Disentangle genus microdiversity within a complex microbial community by using a multi-distance long-read binning method: example of Candidatus Accumulibacter.Environmental microbiology · 2022Article
- Improving metagenomic binning results with overlapped bins using assembly graphs.Algorithms for molecular biology : AMB · 2021Article
- Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies.Frontiers in genetics · 2021Article
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4 authors.
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Abstract
motivationMetagenomics studies have provided key insights into the composition and structure of microbial communities found in different environments. Among the techniques used to analyse metagenomic data, binning is considered a crucial step to characterize the different species of micro-organisms present. The use of short-read data in most binning tools poses several limitations, such as insufficient species-specific signal, and the emergence of long-read sequencing technologies offers us opportunities to surmount them. However, most current metagenomic binning tools have been developed for short reads. The few tools that can process long reads either do not scale with increasing input size or require a database with reference genomes that are often unknown. In this article, we present MetaBCC-LR, a scalable reference-free binning method which clusters long reads directly based on their k-mer coverage histograms and oligonucleotide composition.
resultsWe evaluate MetaBCC-LR on multiple simulated and real metagenomic long-read datasets with varying coverages and error rates. Our experiments demonstrate that MetaBCC-LR substantially outperforms state-of-the-art reference-free binning tools, achieving ∼13% improvement in F1-score and ∼30% improvement in ARI compared to the best previous tools. Moreover, we show that using MetaBCC-LR before long-read assembly helps to enhance the assembly quality while significantly reducing the assembly cost in terms of time and memory usage. The efficiency and accuracy of MetaBCC-LR pave the way for more effective long-read-based metagenomics analyses to support a wide range of applications. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/anuradhawick/MetaBCC-LR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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