Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
30 May 2024
30 May 2024
Historique:
received:
02
01
2024
accepted:
21
05
2024
medline:
31
5
2024
pubmed:
31
5
2024
entrez:
30
5
2024
Statut:
epublish
Résumé
Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m
Identifiants
pubmed: 38816458
doi: 10.1038/s41597-024-03410-0
pii: 10.1038/s41597-024-03410-0
doi:
Substances chimiques
DNA, Fungal
0
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
561Subventions
Organisme : EC | EC Seventh Framework Programm | FP7 Ideas: European Research Council (FP7-IDEAS-ERC - Specific Programme: "Ideas" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013))
ID : 856506
Organisme : Academy of Finland (Suomen Akatemia)
ID : 336212, 345110
Informations de copyright
© 2024. The Author(s).
Références
Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. Nat Rev Microbiol 14, 434–447 (2016).
doi: 10.1038/nrmicro.2016.59
pubmed: 27296482
Halme, P., Heilmann-Clausen, J., Rämä, T., Kosonen, T. & Kunttu, P. Monitoring fungal biodiversity – towards an integrated approach. Fungal Ecol 5, 750–758 (2012).
doi: 10.1016/j.funeco.2012.05.005
Lindahl, B. D. et al. Fungal community analysis by high‐throughput sequencing of amplified markers – a user’s guide. New Phytologist 199, 288–299 (2013).
doi: 10.1111/nph.12243
pubmed: 23534863
Sato, H., Tsujino, R., Kurita, K., Yokoyama, K. & Agata, K. Modelling the global distribution of fungal species: new insights into microbial cosmopolitanism. Mol Ecol 21, 5599–5612 (2012).
doi: 10.1111/mec.12053
pubmed: 23062148
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (1979) 346, (2014).
Barberán, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. PNAS 112, 5756–5761 (2015).
doi: 10.1073/pnas.1420815112
pubmed: 25902536
pmcid: 4426398
Větrovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat Commun 10, 5142 (2019).
doi: 10.1038/s41467-019-13164-8
pubmed: 31723140
pmcid: 6853883
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science (1979) 349, 970–973 (2015).
Hawksworth, D. L. & Lücking, R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol Spectr 5, (2017).
Tedersoo, L. et al. The Global Soil Mycobiome consortium dataset for boosting fungal diversity research. Fungal Divers 111, 573–588 (2021).
doi: 10.1007/s13225-021-00493-7
Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Cons Biol 33, 1187–1192 (2019).
doi: 10.1111/cobi.13311
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat Ecol Evol 2, 1042–1043 (2018).
doi: 10.1038/s41559-018-0573-8
pubmed: 29867100
pmcid: 6027986
Baldrian, P., Větrovský, T., Lepinay, C. & Kohout, P. High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers 114, 539–547 (2022).
doi: 10.1007/s13225-021-00472-y
Abrego, N. et al. Give me a sample of air and I will tell which species are found from your region: Molecular identification of fungi from airborne spore samples. Mol Ecol Resour 18, 511–524 (2018).
doi: 10.1111/1755-0998.12755
pubmed: 29330936
Abrego, N. et al. Fungal communities decline with urbanization—more in air than in soil. ISME J 14, 2806–2815 (2020).
doi: 10.1038/s41396-020-0732-1
pubmed: 32759974
pmcid: 7784924
Bohmann, K. & Lynggaard, C. Transforming terrestrial biodiversity surveys using airborne eDNA. Trends Ecol Evol 38, 119–121 (2023).
doi: 10.1016/j.tree.2022.11.006
pubmed: 36513529
Ovaskainen, O. et al. Monitoring fungal communities with the global spore sampling project. Front Ecol Evol 7 (2020).
Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. PNAS 109, 6241–6246 (2012).
doi: 10.1073/pnas.1117018109
pubmed: 22454494
pmcid: 3341068
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 11, 2639–2643 (2017).
doi: 10.1038/ismej.2017.119
pubmed: 28731476
pmcid: 5702726
Somervuo, P., Koskela, S., Pennanen, J., Nilsson, H. R. & Ovaskainen, O. Unbiased probabilistic taxonomic classification for DNA barcoding. Bioinformatics 32, 2920–2927 (2016).
doi: 10.1093/bioinformatics/btw346
pubmed: 27296980
Abarenkov, K. et al. Protax‐fungi: a web‐based tool for probabilistic taxonomic placement of fungal internal transcribed spacer sequences. New Phytologist 220, 517–525 (2018).
doi: 10.1111/nph.15301
pubmed: 30035303
Blaxter, M. et al. Defining operational taxonomic units using DNA barcode data. Philos T Roy Soc B 360, 1935–1943 (2005).
doi: 10.1098/rstb.2005.1725
Chen, S. et al. Validation of the ITS2 Region as a Novel DNA Barcode for Identifying Medicinal Plant Species. PLoS One 5, e8613 (2010).
doi: 10.1371/journal.pone.0008613
pubmed: 20062805
pmcid: 2799520
White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. in PCR Protocols 315–322, https://doi.org/10.1016/B978-0-12-372180-8.50042-1 (Elsevier, 1990).
Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).
doi: 10.7717/peerj.4925
pubmed: 29868296
pmcid: 5978393
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17, 10 (2011).
doi: 10.14806/ej.17.1.200
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016).
doi: 10.1038/nmeth.3869
pubmed: 27214047
pmcid: 4927377
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
doi: 10.7717/peerj.2584
pubmed: 27781170
pmcid: 5075697
Abarenkov, K. et al. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Res https://doi.org/10.1093/nar/gkad1039 (2023).
Abarenkov, K. Supporting files for EOSC-Nordic service (SH matching analysis v2.0.0). Version 3, 2022-11-29. Available at, https://app.plutof.ut.ee/filerepository/view/5582954 . (2022).
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
doi: 10.1093/bioinformatics/btt509
pubmed: 24008419
pmcid: 3810854
Fish, J. A. et al. FunGene: the functional gene pipeline and repository. Front Microbiol 4 (2013).
Kauserud, H. ITS alchemy: On the use of ITS as a DNA marker in fungal ecology. Fungal Ecol 65, 101274 (2023).
doi: 10.1016/j.funeco.2023.101274
Vu, D., Nilsson, R. H. & Verkley, G. J. M. Dnabarcoder: An open‐source software package for analysing and predicting DNA sequence similarity cutoffs for fungal sequence identification. Mol Ecol Resour 22, 2793–2809 (2022).
doi: 10.1111/1755-0998.13651
pubmed: 35621380
pmcid: 9542245
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
doi: 10.1093/bioinformatics/btq461
pubmed: 20709691
Landau, W. The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing. J Open Source Softw 6, 2959 (2021).
doi: 10.21105/joss.02959
Ovaskainen, O. et al. Data from: Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA. Zenodo https://doi.org/10.5281/zenodo.10435615 (2024).
ENA European Nucleotide Archive. https://identifiers.org/ena.embl:PRJEB65748 (2024).
Floudas, D. & Hibbett, D. S. Revisiting the taxonomy of Phanerochaete (Polyporales, Basidiomycota) using a four gene dataset and extensive ITS sampling. Fungal Biol 119, 679–719 (2015).
doi: 10.1016/j.funbio.2015.04.003
pubmed: 26228559
de Sousa Lira, C. R., dos Santos Chikowski, R., de Lima, V. X., Gibertoni, T. B. & Larsson, K.-H. Allophlebia, a new genus to accomodate Phlebia ludoviciana (Agaricomycetes, Polyporales). Mycol Prog 21, 47 (2022).
doi: 10.1007/s11557-022-01781-5
Geml, J., Davis, D. D. & Geiser, D. M. Systematics of the genus Sphaerobolus based on molecular and morphological data, with the description of Sphaerobolus ingoldii sp. nov. Mycologia 97, 680–694 (2005).
doi: 10.1080/15572536.2006.11832798
pubmed: 16392256
Tikhonov, G. et al. Joint species distribution modelling with the R‐package Hmsc. Methods Ecol Evol 11, 442–447 (2020).
doi: 10.1111/2041-210X.13345
pubmed: 32194928
pmcid: 7074067
Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. https://doi.org/10.1017/9781108591720 (Cambridge University Press, 2020).