Interpreting 16S metagenomic data without clustering to separate sequence similarity from ecological similarity
Year: 2013
Type: preprint
Abstract: The standard approach to analyzing 16S tag sequence data, which relies on clustering reads by sequence similarity into Operational Taxonomic Units (OTUs), underexploits the accuracy of modern sequencing technology. We use published data from a longitudinal time-series study of human tongue microbiota to show that the fine structure of error clouds around abundant sequences is interpretable. Our clustering-free approach identifies independent bacterial strains present in the community regardless of sequence similarity. For example, within standard 97% similarity OTUs we are able to successfully resolve up to 20 distinct substrains, all ecologically distinct but with 16S tags differing by as little as 1 nucleotide (99.2% similarity). A comparative analysis of oral communities of two cohabiting individuals reveals that most such substrains are shared between the two communities at 100% sequence identity. Overall, our analysis shows that the omnipresent use of sequence similarity as a proxy for ecological similarity is not an essential element of 16S tag sequencing methodology and can be avoided entirely.
Source: arXiv (Cornell University)
Authors: Mikhail Tikhonov, Ned S. Wingreen
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W106624816