ArticleJournal of computational biology : a journal of computational molecular cell biology2018
Unbiased Taxonomic Annotation of Metagenomic Samples.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Machine learning-based assessment of the healthy human gut mycobiota landscape using ITS1 DNA metabarcoding data.BioData mining · 2026Article
- Diversity analysis of the endosymbiotic bacterial community in field-collected Haemaphysalis ticks on the tropical Hainan Island, China.Folia parasitologica · 2023Article
- Extension of the shelf-life of fresh pasta using modified atmosphere packaging and bioprotective cultures.Frontiers in microbiology · 2022Article
- Evaluating the Efficiency of DNA Metabarcoding to Analyze the Diet ofLife (Basel, Switzerland) · 2021Article
- Genome-resolved metagenomics using environmental and clinical samples.Briefings in bioinformatics · 2021Review
- Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning.Computational and structural biotechnology journal · 2021Article
- Accurate quantification of bacterial abundance in metagenomic DNAs accounting for variable DNA integrity levels.Microbial genomics · 2020Article
- No metagenomic evidence of tumorigenic viruses in cancers from a selected cohort of immunosuppressed subjects.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The classification of reads from a metagenomic sample using a reference taxonomy is usually based on first mapping the reads to the reference sequences and then classifying each read at a node under the lowest common ancestor of the candidate sequences in the reference taxonomy with the least classification error. However, this taxonomic annotation can be biased by an imbalanced taxonomy and also by the presence of multiple nodes in the taxonomy with the least classification error for a given read. In this article, we show that the Rand index is a better indicator of classification error than the often used area under the receiver operating characteristic (ROC) curve and F-measure for both balanced and imbalanced reference taxonomies, and we also address the second source of bias by reducing the taxonomic annotation problem for a whole metagenomic sample to a set cover problem, for which a logarithmic approximation can be obtained in linear time and an exact solution can be obtained by integer linear programming. Experimental results with a proof-of-concept implementation of the set cover approach to taxonomic annotation in a next release of the TANGO software show that the set cover approach further reduces ambiguity in the taxonomic annotation obtained with TANGO without distorting the relative abundance profile of the metagenomic sample.
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Registered trials
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