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

doi:10.1128/mSystems.00003-15

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

mSystems. 2016 Feb 9;1(1):e00003-15. doi: 10.1128/mSystems.00003-15. eCollection 2016 Jan-Feb.

Authors

Affiliations

¹ Department of Pediatrics, UCSD School of Medicine, La Jolla, California, USA.
² Department 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.
³ Laboratoire d'Ecologie Alpine (LECA), CNRS UMR 5553, Université Grenoble Alpes, Grenoble, France.
⁴ Department of Ecology, University of Kaiserslautern, Kaiserslautern, Germany.
⁵ Department 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.
⁶ Department of Informatics, University of Oslo, Oslo, Norway; Department of Microbiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
⁷ Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA.

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. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Keywords: amplicon sequencing; microbial community analysis; operational taxonomic units; sequence clustering.

Grants and funding

This work was partially supported by the Howard Hughes Medical Institute and the Alfred P. Sloan Foundation.