ArticleBioinformatics (Oxford, England)2012
MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed.
- Trial
- Reference-free k-mer based dissimilarity measures for metagenomes comparison.Frontiers in bioinformatics · 2026Article
- Targeted protein evolution in the gut microbiome by diversity-generating retroelements.Science (New York, N.Y.) · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- MetaComBin: combining abundances and overlaps for binning metagenomics reads.Frontiers in bioinformatics · 2025Article
- Targeted protein evolution in the gut microbiome by diversity-generating retroelements.bioRxiv : the preprint server for biology · 2024Article
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.Nature communications · 2024Article
- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
- Interpreting alignment-free sequence comparison: what makes a score a good score?NAR genomics and bioinformatics · 2022Article
- MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.Current genomics · 2022Article
- MetaCRS: unsupervised clustering of contigs with the recursive strategy of reducing metagenomic dataset's complexity.BMC bioinformatics · 2022Article
- OGRE: Overlap Graph-based metagenomic Read clustEring.Bioinformatics (Oxford, England) · 2021Article
- Metagenomic analysis through the extended Burrows-Wheeler transform.BMC bioinformatics · 2020Article
- An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome.Metabolites · 2020Review
- Article
- A review of methods and databases for metagenomic classification and assembly.Briefings in bioinformatics · 2019Review
- Alignment-free method for DNA sequence clustering using Fuzzy integral similarity.Scientific reports · 2019Article
- De novo clustering of long reads by gene from transcriptomics data.Nucleic acids research · 2019Article
- CLAME: a new alignment-based binning algorithm allows the genomic description of a novel Xanthomonadaceae from the Colombian Andes.BMC genomics · 2018Article
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4 authors.
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Abstract
motivationMetagenomic binning remains an important topic in metagenomic analysis. Existing unsupervised binning methods for next-generation sequencing (NGS) reads do not perform well on (i) samples with low-abundance species or (ii) samples (even with high abundance) when there are many extremely low-abundance species. These two problems are common for real metagenomic datasets. Binning methods that can solve these problems are desirable.
resultsWe proposed a two-round binning method (MetaCluster 5.0) that aims at identifying both low-abundance and high-abundance species in the presence of a large amount of noise due to many extremely low-abundance species. In summary, MetaCluster 5.0 uses a filtering strategy to remove noise from the extremely low-abundance species. It separate reads of high-abundance species from those of low-abundance species in two different rounds. To overcome the issue of low coverage for low-abundance species, multiple w values are used to group reads with overlapping w-mers, whereas reads from high-abundance species are grouped with high confidence based on a large w and then binning expands to low-abundance species using a relaxed (shorter) w. Compared to the recent tools, TOSS and MetaCluster 4.0, MetaCluster 5.0 can find more species (especially those with low abundance of say 6× to 10×) and can achieve better sensitivity and specificity using less memory and running time. AVAILABILITY: http://i.cs.hku.hk/~alse/MetaCluster/ CONTACT: chin@cs.hku.hk.
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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.