ArticleNucleic acids research2014
Strain/species identification in metagenomes using genome-specific markers.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Rifaximin is associated with modest, transient decreases in multiple taxa in the gut microbiota of patients with diarrhoea-predominant irritable bowel syndrome.Gut microbes · 2019Trial
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- Rational Design of Live Biotherapeutic Products for the Prevention ofbioRxiv : the preprint server for biology · 2024Article
- CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level.Environmental science & technology · 2024Article
- The Future Is Now: Unraveling the Expanding Potential of Human (Necro)Microbiome in Forensic Investigations.Microorganisms · 2023Review
- HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer.Bioinformatics (Oxford, England) · 2023Article
- Strain level microbial detection and quantification with applications to single cell metagenomics.Nature communications · 2022Article
- Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics.International journal of molecular sciences · 2022Review
- Ontology-aware neural network: a general framework for pattern mining from microbiome data.Briefings in bioinformatics · 2022Article
- Vibrio Clade 3.0: New Vibrionaceae Evolutionary Units Using Genome-Based Approach.Current microbiology · 2021Article
- Metagenomics: a path to understanding the gut microbiome.Mammalian genome : official journal of the International Mammalian Genome Society · 2021Review
- Clonal evolution ofJournal of oral microbiology · 2021Article
- Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy.PloS one · 2021Article
- Application of Deep Learning in Plant-Microbiota Association Analysis.Frontiers in genetics · 2021Review
- UniqueInternational journal of molecular sciences · 2020Article
- Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data.Frontiers in microbiology · 2020Review
- Qualitative microbiome profiling along a wastewater system in Kampala, Uganda.Scientific reports · 2019Article
- Metagenomic binning through low-density hashing.Bioinformatics (Oxford, England) · 2019Article
- The implementation of omics technologies in cancer microbiome research.Ecancermedicalscience · 2018Review
- A novel data structure to support ultra-fast taxonomic classification of metagenomic sequences with k-mer signatures.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.
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