ArticleAdvances in bioinformatics2008
Metagenome fragment classification using N-mer frequency profiles.
Article in Advances in bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 110 citations in OpenAlex.
- HYMET: a hybrid metagenomic pipeline for accurate and efficient taxonomic classification.GigaScience · 2026Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- The Naïve Bayes classifier++ for metagenomic taxonomic classification-query evaluation.Bioinformatics (Oxford, England) · 2024Article
- Missing microbial eukaryotes and misleading meta-omic conclusions.Nature communications · 2024Article
- MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification.Bioinformatics (Oxford, England) · 2024Article
- Species annotation using a k-mer based KNN model.Bioinformation · 2024Article
- HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads.Bioinformatics advances · 2024Article
- POSMM: an efficient alignment-free metagenomic profiler that complements alignment-based profiling.Environmental microbiome · 2023Article
- Improving taxonomic classification with feature space balancing.Bioinformatics advances · 2023Article
- A convenient correspondence between k-mer-based metagenomic distances and phylogenetically-informed β-diversity measures.PLoS computational biology · 2023Article
- Strain level microbial detection and quantification with applications to single cell metagenomics.Nature communications · 2022Article
- Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences.mSystems · 2022Article
- Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation.Frontiers in microbiology · 2022Review
- A tail of two pandas- whole genome k-mer signature analysis of the red panda (Ailurus fulgens) and the Giant panda (Ailuropoda melanoleuca).BMC genomics · 2021Article
- Microbial community analysis using high-throughput sequencing technology: a beginner's guide for microbiologists.Journal of microbiology (Seoul, Korea) · 2020Article
- Reads Binning Improves Alignment-Free Metagenome Comparison.Frontiers in genetics · 2019Article
- The Promises and Pitfalls of Machine Learning for Detecting Viruses in Aquatic Metagenomes.Frontiers in microbiology · 2019Article
- Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks.Frontiers in microbiology · 2019Review
- Sequence-based 5-mers highly correlated to epigenetic modifications in genes interactions.Genes & genomics · 2018Article
- Cluster oligonucleotide signatures for rapid identification by sequencing.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A vast amount of microbial sequencing data is being generated through large-scale projects in ecology, agriculture, and human health. Efficient high-throughput methods are needed to analyze the mass amounts of metagenomic data, all DNA present in an environmental sample. A major obstacle in metagenomics is the inability to obtain accuracy using technology that yields short reads. We construct the unique N-mer frequency profiles of 635 microbial genomes publicly available as of February 2008. These profiles are used to train a naive Bayes classifier (NBC) that can be used to identify the genome of any fragment. We show that our method is comparable to BLAST for small 25 bp fragments but does not have the ambiguity of BLAST's tied top scores. We demonstrate that this approach is scalable to identify any fragment from hundreds of genomes. It also performs quite well at the strain, species, and genera levels and achieves strain resolution despite classifying ubiquitous genomic fragments (gene and nongene regions). Cross-validation analysis demonstrates that species-accuracy achieves 90% for highly-represented species containing an average of 8 strains. We demonstrate that such a tool can be used on the Sargasso Sea dataset, and our analysis shows that NBC can be further enhanced.
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What OpenQuestion holds
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.