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A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Authors: Ali Hakimzadeh; Alejandro Abdala Asbun; Davide Albanese; Maria Bernard; Dominik Buchner; Benjamin Callahan; J. Gregory Caporaso; +19 Authors

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Abstract

AbstractEnvironmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high‐throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon‐specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty‐two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Countries
France, France, Finland, United Kingdom, Netherlands
Keywords

Data Analysis, 570, review, amplicon data analysis, DNA Barcoding, Taxonomic, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], DNA-analyysi, Bacteria, bioinformatiikka, pipeline, Computational Biology, High-Throughput Nucleotide Sequencing, Eukaryota, bioinformatics, environmental DNA, Archaea, DNA, Environmental, 004, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, DNA-viivakoodit, sekvenssianalyysi, metabarcoding, Metagenomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Software

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
54
Top 1%
Top 10%
Top 1%
Green