ArticleBMC bioinformatics2015
MBBC: an efficient approach for metagenomic binning based on clustering.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- Constructing metagenome-assembled genomes for almost all components in a real bacterial consortium for binning benchmarking.BMC genomics · 2022Article
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- OGRE: Overlap Graph-based metagenomic Read clustEring.Bioinformatics (Oxford, England) · 2021Article
- mixtureS: a novel tool for bacterial strain genome reconstruction from reads.Bioinformatics (Oxford, England) · 2021Article
- Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses.Microbial genomics · 2020Review
- BHap: a novel approach for bacterial haplotype reconstruction.Bioinformatics (Oxford, England) · 2019Article
- Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018Article
- Article
- Improving contig binning of metagenomic data using [Formula: see text] oligonucleotide frequency dissimilarity.BMC bioinformatics · 2017Article
- Interpreting Microbial Biosynthesis in the Genomic Age: Biological and Practical Considerations.Marine drugs · 2017Review
- rRNAFilter: A Fast Approach for Ribosomal RNA Read Removal Without a Reference Database.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- A framework for space-efficient read clustering in metagenomic samples.BMC bioinformatics · 2017Article
- A Metagenomic Approach to Cyanobacterial Genomics.Frontiers in microbiology · 2017Review
- Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics.Computational and structural biotechnology journal · 2017Review
- MBMC: An Effective Markov Chain Approach for Binning Metagenomic Reads from Environmental Shotgun Sequencing Projects.Omics : a journal of integrative biology · 2016Article
- Single-Cell-Genomics-Facilitated Read Binning of Candidate Phylum EM19 Genomes from Geothermal Spring Metagenomes.Applied and environmental microbiology · 2016Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundBinning environmental shotgun reads is one of the most fundamental tasks in metagenomic studies, in which mixed reads from different species or operational taxonomical units (OTUs) are separated into different groups. While dozens of binning methods are available, there is still room for improvement.
resultsWe developed a novel taxonomy-independent approach called MBBC (Metagenomic Binning Based on Clustering) to cluster environmental shotgun reads, by considering k-mer frequency in reads and Markov properties of the inferred OTUs. Tested on twelve simulated datasets, MBBC reliably estimated the species number, the genome size, and the relative abundance of each species, independent of whether there are errors in reads. Tested on multiple experimental datasets, MBBC outperformed two state-of-the-art taxonomy-independent methods, in terms of the accuracy of the estimated species number, genome sizes, and percentages of correctly assigned reads, among other metrics.
conclusionsWe have developed a novel method for binning metagenomic reads based on clustering. This method is demonstrated to reliably predict species numbers, genome sizes, relative species abundances, and k-mer coverage in simple datasets. Our method also has a high accuracy in read binning. The MBBC software is freely available at http://eecs.ucf.edu/~xiaoman/MBBC/MBBC.html .
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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.