ArticleNature communications2024
Taxometer: Improving taxonomic classification of metagenomics contigs.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Improving metagenome binning by integrating intrinsic features and taxonomy.Nature biotechnology · 2026Article
- ChloroScan: Recovering Plastid Genome Bins From Metagenomic Data.Molecular ecology resources · 2026Article
- Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.Microorganisms · 2026Review
- Accurate plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning.Nature biotechnology · 2026Article
- Machine Learning Enables Alignment-Free Distance Calculation and Phylogenetic Placement Using k-Mer Frequencies.Molecular ecology resources · 2025Article
- Evaluation of metagenome binning: advances and challenges.Briefings in bioinformatics · 2025Article
- The Role of Nucleotide Sequencing in Natural Product Drug Discovery.Journal of natural products · 2025Review
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes.Nature methods · 2025Review
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Taxometer: Improving taxonomic classification of metagenomics contigs.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomic binning, contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier using contig abundance profiles and tetra-nucleotide frequencies. We apply Taxometer to five short-read CAMI2 datasets and find that it increases the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2%. Additionally, it reduce the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset by an average of two-fold for Metabuli, Centrifuge, and Kraken2. Futhermore, we use Taxometer for benchmarking taxonomic classifiers on two complex long-read metagenomics data sets where ground truth is not known. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs.
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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.