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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Microbiome research utilizes next-generation sequencing and complex data analysis.
  • Multi-amplicon kits enhance microbiome speciation but introduce analytical challenges.
  • Mixed-orientation reads from multi-hypervariable regions require specialized bioinformatics approaches.

Purpose of the Study:

  • To develop and present two distinct analysis pipelines for mixed-orientation reads from multi-hypervariable (V) region amplicons.
  • To assess the agreement of sequencing data with expected abundances, considering different variable regions and reference databases.
  • To provide a benchmark for microbiome researchers using multi-amplicon sequencing kits.

Main Methods:

  • Two analysis workflows were developed: one using a specialized plugin (CutPrimers) and another using Cutadapt.
  • Mock community sequence data from the Ion16S™ Metagenomics Kit on the Ion Torrent Platform were analyzed.
  • Taxonomic assignment was performed using three reference databases: Ribosomal Database Project, Greengenes, and Silva.
  • Annotation consistency was evaluated using Bray-Curtis, Euclidean, and Jensen-Shannon distances.

Main Results:

  • Both CutPrimers and Cutadapt-based methods showed variability in read mapping across V2-V9 regions.
  • The CutPrimers pipeline demonstrated the best agreement with expected amplicon distribution for V3 regions, while V9 regions showed the poorest agreement.
  • Accurate taxonomic annotation varied significantly depending on the genus-level taxon and the specific V region analyzed.
  • Overall agreement with expected mock community abundances differed across variable regions and reference databases.

Conclusions:

  • The study presents novel microbiome analysis pipelines for separating multi-amplicon data into V-specific reads, specifically for the Ion16S Metagenomics Kit and Ion Torrent platform.
  • The findings highlight significant biases introduced by variable regions and reference databases in microbiome data analysis.
  • Researchers should consider this benchmarking data when planning multi-amplicon sequencing studies to mitigate potential biases.