mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA genes.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34260698", "kind": "science-record", "revision": 3, "content_hash": "sha256:4dc7745091f781e0a3cec0b8c8e17f0aac3ad555190f28da9e04ef173ccb6293", "created_at": "2026-11-01T15:41:43Z", "updated_at": "2026-11-01T21:25:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA genes.", "doi": "10.1093/bioinformatics/btab465", "pmid": "34260698", "authors": ["Guillem Salazar", "Hans-Joachim Ruscheweyh", "Falk Hildebrand", "Silvia G Acinas", "Shinichi Sunagawa"], "journal": "Bioinformatics (Oxford, England)", "year": 2021, "abstract": "Profiling the taxonomic composition of microbial communities commonly involves the classification of ribosomal RNA gene fragments. As a trade-off to maintain high classification accuracy, existing tools are typically limited to the genus level. Here, we present mTAGs, a taxonomic profiling tool that implements the alignment of metagenomic sequencing reads to degenerate consensus reference sequences of small subunit ribosomal RNA genes. It uses DNA fragments, that is, paired-end sequencing reads, as count units and provides relative abundance profiles at multiple taxonomic ranks, including operational taxonomic units based on a 97% sequence identity cutoff. At the genus rank, mTAGs outperformed other tools across several metrics, such as the F1 score by >11% across data from different environments, and achieved competitive (F1 score) or better results (Bray-Curtis dissimilarity) at the sub-genus level.\nAVAILABILITY AND IMPLEMENTATION: The software tool mTAGs is implemented in Python. The source code and binaries are freely available (https://github.com/SushiLab/mTAGs). The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.4352762.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "mTAGs achieved a median F1 score of 0.88 and a median Bray–Curtis similarity to the expected abundance profile of 0.89 outperforming other tools classifying SSU-rRNA gene fragments down to the genus-level, the lowest taxonomic rank common to all tools (QIIME 1, QIIME 2, mothur and MAPseq achieved median F1 scores of 0.72, 0.80, 0.53 and 0.60 and Bray–Curtis similarities of 0.75, 0.77, 0.51 and 0.60, respectively).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34260698", "locator": "sections[3] \"3 Results\" paragraph 4", "quote": "mTAGs achieved a median F1 score of 0.88 and a median Bray–Curtis similarity to the expected abundance profile of 0.89 outperforming other tools classifying SSU-rRNA gene fragments down to the genus-level, the lowest taxonomic rank common to all tools (QIIME 1, QIIME 2, mothur and MAPseq achieved median F1 scores of 0.72, 0.80, 0.53 and 0.60 and Bray–Curtis similarities of 0.75, 0.77, 0.51 and 0.60, respectively)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.4352762.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34260698", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.4352762."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34260698"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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