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\begin{document}

\title{A pile of pipelines: an overview of the bioinformatics software for
metabarcoding data analyses}



\author[1]{Ali Hakimzadeh}%
\author[2]{Alejandro Abdala Asbun}%
\author[3]{Davide  Albanese}%
\author[4]{Maria  Bernard}%
\author[5]{Dominik  Buchner}%
\author[6]{Benjamin Callahan}%
\author[7]{Gregory Caporaso}%
\author[8]{Emily Curd}%
\author[9]{Christophe DJEMIEL}%
\author[10]{Mikael Brandström Durling}%
\author[5]{Vasco Elbrecht}%
\author[11]{Zachary  Gold}%
\author[12]{Hyun Gweon}%
\author[13]{Mehrdad Hajibabaei}%
\author[14]{Falk  Hildebrand}%
\author[1]{Vladimir Mikryukov}%
\author[15]{Eric Normandeau}%
\author[14]{Ezgi Ozkurt}%
\author[16]{Jonathan M. Palmer}%
\author[17]{Géraldine Pascal}%
\author[13]{Teresita Porter}%
\author[18]{Daniel Straub}%
\author[1]{Martti Vasar}%
\author[19]{Tomáš Větrovský}%
\author[20]{Haris Zafeiropoulos}%
\author[1]{Sten Anslan}%
\affil[1]{University of Tartu Institute of Ecology and Earth Sciences}%
\affil[2]{NIOZ Royal Netherlands Institute for Sea Research}%
\affil[3]{Fondazione Edmund Mach Istituto Agrario di San Michele all'Adige}%
\affil[4]{Université Paris-Saclay}%
\affil[5]{University of Duisburg-Essen}%
\affil[6]{North Carolina State University College of Veterinary Medicine}%
\affil[7]{Northern Arizona University}%
\affil[8]{University of Vermont}%
\affil[9]{INRA Dijon}%
\affil[10]{Swedish University of Agricultural Sciences}%
\affil[11]{University of California San Diego}%
\affil[12]{Center for Ecology and Hydrology}%
\affil[13]{University of Guelph}%
\affil[14]{Quadram Institute Bioscience}%
\affil[15]{Université Laval 'Institut de Biologie Intégrative et des Systèmes}%
\affil[16]{Northern Research Station, US Forest Service}%
\affil[17]{GenPhySE}%
\affil[18]{University of Tübingen}%
\affil[19]{Institute of Microbiology Czech Academy of Sciences}%
\affil[20]{KU Leuven Rega Institute for Medical Research}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Environmental 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 amongst 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-one amplicon processing pipelines with
the goal of helping users to select a pipeline for their metabarcoding
projects.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Metabarcoding pipelines review paper.rtf`  available at \url{https://authorea.com/users/585035/articles/623934-a-pile-of-pipelines-an-overview-of-the-bioinformatics-software-for-metabarcoding-data-analyses}\selectlanguage{english}
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\textbf{Hosted file}

\verb`Table 1. Pipelines list .xlsx`  available at \url{https://authorea.com/users/585035/articles/623934-a-pile-of-pipelines-an-overview-of-the-bioinformatics-software-for-metabarcoding-data-analyses}

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