ArticleBMC bioinformatics2010
Clustering metagenomic sequences with interpolated Markov models.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Solving genomic puzzles: computational methods for metagenomic binning.Briefings in bioinformatics · 2024Review
- Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review.Foods (Basel, Switzerland) · 2024Review
- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
- MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.Current genomics · 2022Article
- Introduction to the principles and methods underlying the recovery of metagenome-assembled genomes from metagenomic data.MicrobiologyOpen · 2022Review
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- Improving metagenomic binning results with overlapped bins using assembly graphs.Algorithms for molecular biology : AMB · 2021Article
- Antibiotic resistance: Time of synthesis in a post-genomic age.Computational and structural biotechnology journal · 2021Review
- Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses.Microbial genomics · 2020Review
- Species complex delimitations in the genusApplications in plant sciences · 2020Article
- Microbial community analysis using high-throughput sequencing technology: a beginner's guide for microbiologists.Journal of microbiology (Seoul, Korea) · 2020Article
- MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage.BMC bioinformatics · 2019Article
- A review of methods and databases for metagenomic classification and assembly.Briefings in bioinformatics · 2019Review
- A signal processing method for alignment-free metagenomic binning: multi-resolution genomic binary patterns.Scientific reports · 2019Article
- Scalable methods for analyzing and visualizing phylogenetic placement of metagenomic samples.PloS one · 2019Article
- Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018Article
- CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads.Genes · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Decontaminating eukaryotic genome assemblies with machine learning.BMC bioinformatics · 2017Article
- DESMAN: a new tool for de novo extraction of strains from metagenomes.Genome biology · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundSequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.
resultsWe present SCIMM (Sequence Clustering with Interpolated Markov Models), an unsupervised sequence clustering method. SCIMM achieves greater clustering accuracy than previous unsupervised approaches. We examine the limitations of unsupervised learning on complex datasets, and suggest a hybrid of SCIMM and supervised learning method Phymm called PHYSCIMM that performs better when evolutionarily close training genomes are available.
conclusionsSCIMM and PHYSCIMM are highly accurate methods to cluster metagenomic sequences. SCIMM operates entirely unsupervised, making it ideal for environments containing mostly novel microbes. PHYSCIMM uses supervised learning to improve clustering in environments containing microbial strains from well-characterized genera. SCIMM and PHYSCIMM are available open source from http://www.cbcb.umd.edu/software/scimm.
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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.