A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

Abstract

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.

Microbiology: Create a global microbiome effort

“…Differences between the primers (which provide a starting point for the DNA synthesis) used to amplify this gene can have a big effect on the sequence data ultimately obtained. And estimates of species numbers can vary by two to three orders of magnitude, even when applied to the same sample, because different software packages are being used to analyse the amplified genes 7 .…”

Performing Skin Microbiome Research: A Method to the Madness

“…There are several approaches to clustering (Chen et al , 2013). The reference clusters used and the similarity threshold significantly affect the clustering results (He et al , 2015; Kopylova et al , 2016; Schloss, 2016).…”

Bacteria of the Humboldt sulfuretum comply with unifying macroecological principles

“…Subsequently, JBPC scientists processed the resulting data and uploaded information in two forms, i. e., taxonomic annotations (Sogin et al 2006) and OTU matrices. The single linkage preclustering (SLP) method was used to cluster sequences into OTUs (Huse et al 2010), which is one of the most suitable methods to deal with short sequences (Chen et al 2013). The data is accessible at the "Visualization and Analysis of Microbial Population Structures" (VAMPS) website (Huse et al 2014) and at the National Center for Biotechnology Information (NCBI) short reads archive (SRA) under the code SRP048832.…”