in

MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding

  • 1.

    Porter, T. M. & Hajibabaei, M. Scaling up: A guide to high‐throughput genomic approaches for biodiversity analysis. Mol. Ecol. 27, 313–338, https://doi.org/10.1111/mec.14478 (2018).

    Article  PubMed  Google Scholar 

  • 2.

    Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next‐generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050, https://doi.org/10.1111/j.1365-294X.2012.05470.x (2012).

    CAS  Article  PubMed  Google Scholar 

  • 3.

    Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research And Monitoring. (Oxford University Press (2018).

  • 4.

    Park, S.-C. & Won, S. Evaluation of 16S rRNA databases for taxonomic assignments using mock community. Genomics Inform. 16, e24, https://doi.org/10.5808/GI.2018.16.4.e24 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  • 5.

    Richardson, R. T., Bengtsson-Palme, J., Gardiner, M. M. & Johnson, R. M. A reference cytochrome c oxidase subunit I database curated for hierarchical classification of arthropod metabarcoding data. PeerJ 6, e5126, https://doi.org/10.7717/peerj.5126 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  • 6.

    Decelle, J. et al. Phyto REF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15, 1435–1445, https://doi.org/10.1111/1755-0998.12401 (2015).

    CAS  Article  PubMed  Google Scholar 

  • 7.

    Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264, https://doi.org/10.1093/nar/gky1022 (2019).

    CAS  Article  PubMed  Google Scholar 

  • 8.

    Weigand, H. et al. DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work. Sci. Total Environ. 678, 499–524, https://doi.org/10.1016/j.scitotenv.2019.04.247 (2019).

    ADS  CAS  Article  PubMed  Google Scholar 

  • 9.

    Carew, M. E. et al. A DNA barcode database of Australia’s freshwater macroinvertebrate fauna. Mar. Freshwat. Res. 68, 1788–1802, https://doi.org/10.1071/MF16304 (2017).

    Article  Google Scholar 

  • 10.

    Leray, M. & Knowlton, N. Censusing marine eukaryotic diversity in the twenty-first century. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371 https://doi.org/10.1098/rstb.2015.0331 (2016).

  • 11.

    Bik, H. M., Halanych, K. M., Sharma, J. & Thomas, W. K. Dramatic shifts in benthic microbial eukaryote communities following the Deepwater Horizon oil spill. PloS one 7, e38550, https://doi.org/10.1371/journal.pone.0038550 (2012).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 12.

    Berry, O. et al. Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar. Ecol. Prog. Ser. 540, 167–181, https://doi.org/10.3354/meps11524 (2015).

    ADS  CAS  Article  Google Scholar 

  • 13.

    Hardy, N. et al. Assessing the trophic ecology of top predators across a recolonisation frontier using DNA metabarcoding of diets. Mar. Ecol. Prog. Ser. 573, 237–254, https://doi.org/10.3354/meps12165 (2017).

    ADS  CAS  Article  Google Scholar 

  • 14.

    von Ammon, U. et al. Linking environmental DNA and RNA for improved detection of the marine invasive fanworm Sabella spallanzanii. Front. Mar. Sci. 6, 621, https://doi.org/10.3389/fmars.2019.00621 (2019).

    Article  Google Scholar 

  • 15.

    Bourlat, S. J. et al. Genomics in marine monitoring: new opportunities for assessing marine health status. Mar. Pollut. Bull. 74, 19–31, https://doi.org/10.1016/j.marpolbul.2013.05.042 (2013).

    CAS  Article  PubMed  Google Scholar 

  • 16.

    Andújar, C., Arribas, P., Yu, D. W., Vogler, A. P. & Emerson, B. C. Why the COI barcode should be the community DNA metabarcode for the metazoa. Mol. Ecol. 27, 3968–3975, https://doi.org/10.1111/mec.14844 (2018).

    Article  PubMed  Google Scholar 

  • 17.

    Porter, T. M. & Hajibabaei, M. Over 2.5 million COI sequences in GenBank and growing. PloS one 13, e0200177, https://doi.org/10.1371/journal.pone.0200177 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 18.

    Ratnasingham, S. & Hebert, P. D. N. BOLD: The Barcode of Life Data System. Mol. Ecol. Notes 7, 355–364, https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 19.

    Wangensteen, O. S. & Turon, X. Metabarcoding Techniques for Assessing Biodiversity of Marine Animal Forests in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Sergio Rossi, Lorenzo Bramanti, Andrea Gori, & Covadonga Orejas Saco del Valle) 1-29 (Springer International Publishing (2015).

  • 20.

    NCBI Resource Coordinators. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Res. 45, D12–D17, https://doi.org/10.1093/nar/gkw1071 (2016).

    CAS  Article  PubMed Central  Google Scholar 

  • 21.

    Meiklejohn, K. A., Damaso, N. & Robertson, J. M. Assessment of BOLD and GenBank – Their accuracy and reliability for the identification of biological materials. PloS one 14, e0217084, https://doi.org/10.1371/journal.pone.0217084 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 22.

    Leray, M., Knowlton, N., Ho, S.-L., Nguyen, B. N. & Machida, R. J. GenBank is a reliable resource for 21st century biodiversity research. Proc. Natl. Acad. Sci. USA 116, 22651–22656, https://doi.org/10.1073/pnas.1911714116 (2019).

    ADS  CAS  Article  PubMed  Google Scholar 

  • 23.

    Macher, J. N., Macher, T. H. & Leese, F. Combining NCBI and BOLD databases for OTU assignment in metabarcoding and metagenomic datasets: The BOLD_NCBI _Merger. Metabarcoding and Metagenomics 1, e22262, https://doi.org/10.3897/mbmg.1.22262 (2017).

    Article  Google Scholar 

  • 24.

    Huson, D. H. et al. MEGAN Community edition – Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957, https://doi.org/10.1371/journal.pcbi.1004957 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 25.

    Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257, https://doi.org/10.1186/s13059-019-1891-0 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 26.

    WoRMS Editorial Board. World Register of Marine Species https://doi.org/10.14284/170. (2019).

  • 27.

    Guiry, M. D. & Guiry, G. M. AlgaeBase https://www.algaebase.org. (2019).

  • 28.

    Chamberlain, S. bold: Interface to Bold Systems API https://CRAN.R-project.org/package=bold (2019).

  • 29.

    R Core Team R: A language and environment for statistical computing. v. 3.6.1 http://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria. (2019).

  • 30.

    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584, https://doi.org/10.7717/peerj.2584 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  • 31.

    Federhen, S. The NCBI taxonomy database. Nucleic Acids Res. 40, D136–D143, https://doi.org/10.1093/nar/gkr1178 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 32.

    McIntyre, A. B. et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18, 182, https://doi.org/10.1186/s13059-017-1299-7 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 33.

    Boyer, F. et al. obitools: A unix‐inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182, https://doi.org/10.1111/1755-0998.12428 (2016).

    CAS  Article  PubMed  Google Scholar 

  • 34.

    Leonard, G. guyleonard/taxdump_edit v. 1.1 Zenodo https://doi.org/10.5281/zenodo.3701276 (2020).

  • 35.

    Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34, https://doi.org/10.1186/1742-9994-10-34 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 36.

    Derycke, S., Vanaverbeke, J., Rigaux, A., Backeljau, T. & Moens, T. Exploring the use of cytochrome oxidase c subunit 1 (COI) for DNA barcoding of free-living marine nematodes. PloS one 5, e13716, https://doi.org/10.1371/journal.pone.0013716 (2010).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 37.

    Krehenwinkel, H. et al. Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale. GigaScience 8, giz006, https://doi.org/10.1093/gigascience/giz006 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 38.

    Arranz, V., Pearman, W. S., Aguirre, J. D. & Liggins, L. MARES Custom Metabarcoding Database. Open Science Framework https://doi.org/10.17605/osf.io/8rdqk (2019).

  • 39.

    Wangensteen, O. & Turon, X. db_COI_MBPK. GitHub http://github.com/metabarpark/Reference-databases (2016).

  • 40.

    Curd, E. E. et al. Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets. Methods Ecol. Evol. 10, 1469–1475, https://doi.org/10.1111/2041-210X.13214 (2019).

    Article  Google Scholar 

  • 41.

    Curd, E. E. et al. CRUX-CO1. Dryad Digital Repository, https://doi.org/10.5061/dryad.mf0126f/1 (2019).

  • 42.

    Ficetola, G. F. et al. An in silico approach for the evaluation of DNA barcodes. BMC Genomics 11, 434, https://doi.org/10.1186/1471-2164-11-434 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 43.

    Machida, R. J. Data from: Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Dryad, https://doi.org/10.5061/dryad.2v00t (2018).

  • 44.

    Machida, R. J., Leray, M., Ho, S.-L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027, https://doi.org/10.1038/sdata.2017.27 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  • 45.

    Macheriotou, L. et al. Metabarcoding free‐living marine nematodes using curated 18S and CO1 reference sequence databases for species‐level taxonomic assignments. Ecol. Evol. 9, 1211–1226, https://doi.org/10.1002/ece3.4814 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  • 46.

    Whittaker, R. H. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr. 30, 279–338, https://doi.org/10.2307/1943563 (1960).

    Article  Google Scholar 

  • 47.

    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143, https://doi.org/10.1111/j.1466-8238.2009.00490.x (2010).

    Article  Google Scholar 

  • 48.

    Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812, https://doi.org/10.1111/j.2041-210X.2012.00224.x (2012).

    Article  Google Scholar 

  • 49.

    Sonet, G. et al. Utility of GenBank and the Barcode of Life Data Systems (BOLD) for the identification of forensically important Diptera from Belgium and France. ZooKeys, 307, https://doi.org/10.3897/zookeys.365.6027 (2013).

  • 50.

    Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R. F1000Research, 2 https://doi.org/10.12688/f1000research.2-191.v2 (2013).


  • Source: Ecology - nature.com

    Solarizing networks

    Light limitation regulates the response of autumn terrestrial carbon uptake to warming