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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Synthetic Sequencing Standards: A Guide to Database Choice for Rumen Microbiota Amplicon Sequencing Analysis.

Paul E Smith1,2, Sinead M Waters1, Ruth Gómez Expósito3

  • 1Teagasc Animal and Bioscience Research Department, Teagasc Grange, Meath, Ireland.

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|December 28, 2020
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Summary

Choosing the right reference database is crucial for accurately analyzing rumen microbial communities using high-throughput sequencing. This study highlights significant classification differences based on database choice, impacting livestock research.

Keywords:
amplicon sequencingrRNAreference databaserumen microbiotasequencing standard

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Area of Science:

  • Microbial Ecology
  • Metagenomics
  • Bioinformatics

Background:

  • High-throughput sequencing (HTS) advances rumen microbial community analysis.
  • Identifying rumen microbes aids livestock nutrition, genetics, performance, and greenhouse gas studies.
  • Lack of internal controls and consensus on reference databases hinder HTS result validation.

Purpose of the Study:

  • To evaluate the impact of different reference databases on rumen microbial amplicon sequencing results.
  • To assess the performance of four DADA2 reference training sets (RDP, SILVA, GTDB, RefSeq + RDP).
  • To investigate the effect of phylogenetic bootstrapping thresholds (50 and 80) on classification accuracy.

Main Methods:

  • Development and use of a synthetic rumen-specific sequencing standard.
  • Comparison of four DADA2 reference training sets for sequence classification.
  • Application of two phylogenetic bootstrapping thresholds to assess stringency.

Main Results:

  • Significant differences in taxonomic classification were observed across the tested databases.
  • The classification of specific genera, such as *Clostridium*, varied notably between databases.
  • Increasing phylogenetic bootstrapping stringency influenced classification outcomes.

Conclusions:

  • Database choice significantly affects rumen microbial community analysis using amplicon sequencing.
  • Inconsistent nomenclature and classification across databases necessitate a standardized approach.
  • Development and routine use of microbiome-specific reference standards are recommended for HTS data validation across microbial ecology disciplines.