title: Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering.
abstract: BACKGROUND
  High-throughput bacterial 16S rRNA gene sequencing followed by clustering of short sequences into operational taxonomic units (OTUs) is widely used for microbiome profiling. However, clustering of short 16S rRNA gene reads into biologically meaningful OTUs is challenging, in part because nucleotide variation along the 16S rRNA gene is only partially captured by short reads. The recent emergence of long-read platforms, such as single-molecule real-time (SMRT) sequencing from Pacific Biosciences, offers the potential for improved taxonomic and phylogenetic profiling. Here, we evaluate the performance of long- and short-read 16S rRNA gene sequencing using simulated and experimental data, followed by OTU inference using computational pipelines based on heuristic and complete-linkage hierarchical clustering.
  
  
  RESULTS
  In simulated data, long-read sequencing was shown to improve OTU quality and decrease variance. We then profiled 40 human gut microbiome samples using a combination of Illumina MiSeq and Blautia-specific SMRT sequencing, further supporting the notion that long reads can identify additional OTUs. We implemented a complete-linkage hierarchical clustering strategy using a flexible computational pipeline, tailored specifically for PacBio circular consensus sequencing (CCS) data that outperforms heuristic methods in most settings: https://github.com/oscar-franzen/oclust/ .
  
  
  CONCLUSIONS
  Our data demonstrate that long reads can improve OTU inference; however, the choice of clustering algorithm and associated clustering thresholds has significant impact on performance.
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. p.oscar.franzen@gmail.com.
authors.forename: Oscar
authors.initials: O
authors.lastname: Franzén
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. jianzhong.hu@mssm.edu.
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Hu
authors.affiliation: Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. xiuliang.bao@mssm.edu.
authors.forename: Xiuliang
authors.initials: X
authors.lastname: Bao
authors.affiliation: Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. steven.itzkowitz@mountsinai.org.
authors.forename: Steven H
authors.initials: SH
authors.lastname: Itzkowitz
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. inga.peter@mssm.edu.
authors.forename: Inga
authors.initials: I
authors.lastname: Peter
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ali.bashir@mssm.edu.
authors.forename: Ali
authors.initials: A
authors.lastname: Bashir
mesh_terms: D001419:Bacteria / Q000145:classification / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D054892:Metagenome; D064307:Microbiota; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D015203:Reproducibility of Results
chemical_list: D012336:RNA, Ribosomal, 16S