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Updated: May 23, 2026

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
10:24

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Published on: August 29, 2014

Grinder: a versatile amplicon and shotgun sequence simulator.

Florent E Angly1, Dana Willner, Forest Rohwer

  • 1Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD 4072, Australia. florent.angly@gmail.com

Nucleic Acids Research
|March 22, 2012
PubMed
Summary
This summary is machine-generated.

Grinder is a new open-source tool that simulates amplicon and shotgun sequencing data. It is the first tool to simulate amplicon datasets, crucial for microbial ecology research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Microbial Ecology

Background:

  • Accurate simulation of sequencing data is essential for developing and validating bioinformatics tools.
  • Existing tools lack the capability to simulate amplicon datasets, a common data type in microbial ecology.
  • The complexity of microbial communities requires sophisticated simulation methods to capture diversity and experimental biases.

Purpose of the Study:

  • To introduce Grinder, a novel open-source software for simulating diverse biological sequence datasets.
  • To provide a versatile tool for generating amplicon and shotgun sequencing data with specified community structures and biases.
  • To support hypothesis testing, software development, and experimental design in bioinformatics and microbial ecology.

Main Methods:

  • Development of an open-source bioinformatic tool named Grinder.
  • Implementation of simulation capabilities for amplicon (e.g., 16S rRNA) and various shotgun (genomic, metagenomic, transcriptomic, metatranscriptomic) datasets.
  • Inclusion of parameters to control community structure, alpha and beta diversities, and experimental biases (e.g., chimeras, gene copy number variation).
  • Support for commonly used sequencing platforms.

Main Results:

  • Grinder successfully simulates amplicon and shotgun datasets from reference sequences.
  • The tool allows for the creation of sequence libraries with defined community structures and diversities.
  • Grinder can incorporate experimental biases, such as chimeras and gene copy number variation.
  • This is the first tool capable of simulating amplicon datasets, addressing a gap in existing software.

Conclusions:

  • Grinder offers a versatile and powerful solution for simulating complex biological sequence data.
  • The tool is particularly valuable for microbial ecologists and researchers working with amplicon-based studies.
  • Grinder facilitates the development and benchmarking of bioinformatics tools and the design of sequencing experiments.
  • It serves as a valuable complement to in vitro mock communities for studying clinical and environmental microbial populations.