FN Archimer Export Format
PT J
TI Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification
BT 
AF Mathon, Laetitia
   Valentini, Alice
   Guérin, Pierre‐Edouard
   Normandeau, Eric
   Noel, Cyril
   Lionnet, Clément
   Boulanger, Emilie
   Thuillier, Wilfried
   Bernatchez, Louis
   Mouillot, David
   Dejean, Tony
   Manel, Stéphanie
AS 1:1,2;2:2;3:1;4:3;5:4;6:5;7:1,6;8:5;9:3;10:6,7;11:2;12:1;
FF 1:;2:;3:;4:;5:PDG-IRSI-SEBIMER;6:;7:;8:;9:;10:;11:;12:;
C1 CEFE, Univ. Montpellier, CNRS, EPHE‐PSL University, IRD, Univ Paul Valéry Montpellier 3 Montpellier ,France
   SPYGEN, 17 rue du Lac Saint‐André, Savoie Technolac 73370 Le Bourget du Lac, France
   Université Laval IBIS (Institut de Biologie Intégrative et des Systèmes) 1030 av. de la Médecine Québec QC G1V 0A6 ,Canada
   IFREMER ‐ IRSI ‐ Service de Bioinformatique (SeBiMER) 29280 Plouzané ,France
   Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine F‐ 38000 Grenoble ,France
   MARBEC, Univ. Montpellier,CNRS, IRD, Ifremer Montpellier ,France
   Institut Universitaire de France IUF Paris 75231, France
C2 UNIV MONTPELLIER, FRANCE
   SPYGEN, FRANCE
   UNIV LAVAL, CANADA
   IFREMER, FRANCE
   UNIV GRENOBLE ALPES, FRANCE
   UNIV MONTPELLIER, FRANCE
   INST UNIV FRANCE, FRANCE
SI BREST
SE PDG-IRSI-SEBIMER
UM MARBEC
IN WOS Ifremer UPR
   WOS Cotutelle UMR
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 8.678
TC 19
UR https://archimer.ifremer.fr/doc/00696/80761/84065.pdf
LA English
DT Article
DE ;benchmark;bioinformatics;eDNA;metabarcoding;sensitivity;species identification
AB Bioinformatic analysis of eDNA metabarcoding data is crucial toward rigorously assessing biodiversity. Many programs are now available for each step of the required analyses, but their relative abilities at providing fast and accurate species lists have seldom been evaluated. We used simulated mock communities and real fish eDNA metabarcoding data to evaluate the performance of 13 bioinformatic programs and pipelines to retrieve fish occurrence and read abundance using the 12S mt rRNA gene marker. We used four indices to compare the outputs of each program with the simulated samples: sensitivity, F-measure, root-mean-square error (RMSE) on read relative abundances, and execution time. We found marked differences among programs only for the taxonomic assignment step, both in terms of sensitivity, F-measure and RMSE. Running time was highly different between programs for each step. The fastest programs with best indices for each step were assembled into a pipeline. We compare this pipeline to pipelines constructed from existing toolboxes (OBITools, Barque, and QIIME 2). Our pipeline and Barque obtained the best performance for all indices and appear to be better alternatives to highly used pipelines for analyzing fish eDNA metabarcoding data with a complete reference database. Real eDNA metabarcoding data also indicated differences for taxonomic assignment and execution time only. This study reveals major differences between programs during the taxonomic assignment step. The choice of algorithm for the taxonomic assignment can have a significant impact on diversity estimates and should be made according to the objectives of the study. 
PY 2021
PD OCT
SO Molecular Ecology Resources
SN 1755-098X
PU Wiley
VL 21
IS 7
UT 000661079300001
BP 2565
EP 2579
DI 10.1111/1755-0998.13430
ID 80761
ER

EF