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Synthetic Sequencing Standards: A Guide to Database Choice for Rumen Microbiota Amplicon Sequencing Analysis

Authors
  • Smith, Paul E.1, 2
  • Waters, Sinead M.1
  • Gómez Expósito, Ruth3
  • Smidt, Hauke3
  • Carberry, Ciara A.1
  • McCabe, Matthew S.1
  • 1 Teagasc Animal and Bioscience Research Department, Teagasc Grange, Meath , (Ireland)
  • 2 UCD School of Agricultural and Food Science, University College Dublin, Dublin , (Ireland)
  • 3 Laboratory of Microbiology, Wageningen University & Research, Wageningen , (Netherlands)
Type
Published Article
Journal
Frontiers in Microbiology
Publisher
Frontiers Media SA
Publication Date
Dec 08, 2020
Volume
11
Identifiers
DOI: 10.3389/fmicb.2020.606825
Source
Frontiers
Keywords
Disciplines
  • Microbiology
  • Original Research
License
Green

Abstract

Our understanding of complex microbial communities, such as those residing in the rumen, has drastically advanced through the use of high throughput sequencing (HTS) technologies. Indeed, with the use of barcoded amplicon sequencing, it is now cost effective and computationally feasible to identify individual rumen microbial genera associated with ruminant livestock nutrition, genetics, performance and greenhouse gas production. However, across all disciplines of microbial ecology, there is currently little reporting of the use of internal controls for validating HTS results. Furthermore, there is little consensus of the most appropriate reference database for analyzing rumen microbiota amplicon sequencing data. Therefore, in this study, a synthetic rumen-specific sequencing standard was used to assess the effects of database choice on results obtained from rumen microbial amplicon sequencing. Four DADA2 reference training sets (RDP, SILVA, GTDB, and RefSeq + RDP) were compared to assess their ability to correctly classify sequences included in the rumen-specific sequencing standard. In addition, two thresholds of phylogenetic bootstrapping, 50 and 80, were applied to investigate the effect of increasing stringency. Sequence classification differences were apparent amongst the databases. For example the classification of Clostridium differed between all databases, thus highlighting the need for a consistent approach to nomenclature amongst different reference databases. It is hoped the effect of database on taxonomic classification observed in this study, will encourage research groups across various microbial disciplines to develop and routinely use their own microbiome-specific reference standard to validate analysis pipelines and database choice.

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