<scp>SimpleMetaPipeline</scp> : Breaking the bioinformatics bottleneck in metabarcoding

Abstract The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted (‘point‐and‐click’) elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times. Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs. We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences. SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs.

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Publication Details

Journal
Methods in Ecology and Evolution
Published
2024-10-07
DOI
https://doi.org/10.1111/2041-210x.14434
Citations
5
Primary Topic
Environmental DNA in Biodiversity Studies
Type
article
Field-Weighted Citation Impact
2.29

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article

SimpleMetaPipeline : Breaking the bioinformatics bottleneck in metabarcoding

Emma Ransome, Sarah M. Tweedt, Chris Meyer, Jake Williams et al.
5 citations
Methods in Ecology and Evolution
Environmental DNA in Biodiversity Studies
2.29
article

SimpleMetaPipeline : Breaking the bioinformatics bottleneck in metabarcoding

Emma Ransome, Sarah M. Tweedt, Chris Meyer, Jake Williams, Nathalie Pettorelli, Margaux Steyaert, Rosalie Dowell, Kenneth S. Macdonald
article en
5 citations

Abstract

Abstract The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted (‘point‐and‐click’) elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times. Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs. We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences. SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs.

Methods in Ecology and EvolutionVol. 15(11)
Smithsonian Institution (US), Zoological Society of London (GB), National Museum of Natural History (US), Imperial College London (GB)
Natural Environment Research Council
Openalex Percentile: Top 12%
Environmental DNA in Biodiversity Studies
2.29
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