TY - JOUR
T1 - A pile of pipelines
T2 - An overview of the bioinformatics software for metabarcoding data analyses
AU - Hakimzadeh, Ali
AU - Abdala Asbun, Alejandro
AU - Albanese, Davide
AU - Bernard, Maria
AU - Buchner, Dominik
AU - Callahan, Benjamin
AU - Caporaso, J. Gregory
AU - Curd, Emily
AU - Djemiel, Christophe
AU - Brandström Durling, Mikael
AU - Elbrecht, Vasco
AU - Gold, Zachary
AU - Gweon, Hyun S.
AU - Hajibabaei, Mehrdad
AU - Hildebrand, Falk
AU - Mikryukov, Vladimir
AU - Normandeau, Eric
AU - Özkurt, Ezgi
AU - M. Palmer, Jonathan
AU - Pascal, Géraldine
AU - Porter, Teresita M.
AU - Straub, Daniel
AU - Vasar, Martti
AU - Větrovský, Tomáš
AU - Zafeiropoulos, Haris
AU - Anslan, Sten
N1 - Funding Information:
This work was supported by the European Regional Development Fund and the programme Mobilitas Pluss (MOBTP198). MH and TMP received funding from Genome Canada and Ontario Genomics through the Sequencing the Rivers for Environmental Assessment and Monitoring (STREAM) project. DS acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy, cluster of Excellence EXC2124 “Controlling microbes to fight infection” (CMFI), project ID 390838134. We wish to offer our heartfelt thanks to Sébastien Terrat, the leading developer of BIOCOM‐PIPE (including ReClustOR) for the support extended to certain points in this tool. TV was supported by the Czech Science Foundation (21‐17749S). We thank other FROGS'members Vincent Darbot, Lucas Auer and Olivier Rué, and Frédéric Mahé (swarm software author) for their fruitful exchanges on the ASV/OTU/cluster terminology. EC received funding through an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103449.
Publisher Copyright:
© 2023 John Wiley & Sons Ltd.
PY - 2023
Y1 - 2023
N2 - 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 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.
AB - 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 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.
KW - amplicon data analysis
KW - bioinformatics
KW - environmental DNA
KW - metabarcoding
KW - pipeline
KW - review
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U2 - 10.1111/1755-0998.13847
DO - 10.1111/1755-0998.13847
M3 - Review article
AN - SCOPUS:85167335441
SN - 1755-098X
JO - Molecular Ecology Resources
JF - Molecular Ecology Resources
ER -