Evidence map›Paper›PMID 27822515›Full record

ArticlemSystems

Open-Source Sequence Clustering Methods Improve the State Of the Art.

Evguenia Kopylova, Jose A Navas-Molina, Céline Mercier, Zhenjiang Zech Xu, Frédéric Mahé, Yan He, Hong-Wei Zhou, Torbjørn Rognes, J Gregory Caporaso, Rob Knight

Abstract read
In one paragraph

Article in mSystems. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed.

  1. Trial
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  6. International journal of microbiology · 2026
    Review
  7. Microbial communities in the rhizosphere of tropical soils cultivated with maize as a function of nitrogen and phosphorus fertilizers.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025
    Article
  8. Article
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  12. Review
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  20. Article

25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Evguenia KopylovaDepartment of Pediatrics, UCSD School of Medicine, La Jolla, California, USA.
Jose A Navas-MolinaDepartment of Pediatrics, UCSD School of Medicine, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA.
Céline MercierLaboratoire d'Ecologie Alpine (LECA), CNRS UMR 5553, Université Grenoble Alpes, Grenoble, France.
Zhenjiang Zech XuDepartment of Pediatrics, UCSD School of Medicine, La Jolla, California, USA.
Frédéric MahéDepartment of Ecology, University of Kaiserslautern, Kaiserslautern, Germany.
Yan HeDepartment of Environmental Health, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Hong-Wei ZhouDepartment of Environmental Health, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health and Tropical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Torbjørn RognesDepartment of Informatics, University of Oslo, Oslo, Norway; Department of Microbiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
J Gregory CaporasoDepartment of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA.
Rob KnightDepartment of Pediatrics, UCSD School of Medicine, La Jolla, California, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release.

Indexed as

amplicon sequencingmicrobial community analysisoperational taxonomic unitssequence clustering

Identifiers

PMID27822515
PMCPMC5069751

What OpenQuestion holds

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Registered trials

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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.