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Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Organic identifications via DNA barcodes. Proceedings of the Royal Society of London. Sequence B: Organic Sciences 270, 313–321, https://doi.org/10.1098/rspb.2002.2218 (2003).
Google Scholar
Li, D.-Z. et al. Comparative evaluation of a big dataset signifies that inner transcribed spacer (ITS) ought to be included into the core barcode for seed crops. Proc. Natl. Acad. Sci. (PNAS) 108, 19641–19646, https://doi.org/10.1073/pnas.1104551108 (2011).
Google Scholar
Schoch, C. L. et al. Nuclear ribosomal inner transcribed spacer (ITS) area as a common DNA barcode marker for Fungi. Proc. Natl. Acad. Sci. (PNAS) 109, 6241–6246, https://doi.org/10.1073/pnas.1117018109 (2012).
Google Scholar
Kress, W. J., Wurdack, Okay. J., Zimmer, E. A., Weigt, L. A. & Janzen, D. H. Use of DNA barcodes to establish flowering crops. Proc. Natl. Acad. Sci. (PNAS) 102, 8369–8374, https://doi.org/10.1073/pnas.0503123102 (2005).
Google Scholar
Newmaster, S. G., Fazekas, A. J., Steeves, R. A. D. & Janovec, J. Testing candidate plant barcode areas within the Myristicaceae. Mol. Ecol. Resour. 8, 480–490, https://doi.org/10.1111/j.1471-8286.2007.02002.x (2008).
Google Scholar
Lahaye, R. et al. DNA barcoding the floras of biodiversity hotspots. Proc. Natl. Acad. Sci. USA (PNAS) 105, 2923–2928, https://doi.org/10.1073/pnas.0709936105 (2008).
Google Scholar
Hollingsworth, P. M. et al. A DNA barcode for land crops. Proc. Natl. Acad. Sci. (PNAS) 106, 12794–12797, https://doi.org/10.1073/pnas.0905845106 (2009).
Google Scholar
Li, X. et al. Plant DNA barcoding: from gene to genome. Biol. Rev. 90, 157–166, https://doi.org/10.1111/brv.12104 (2015).
Google Scholar
Ruppert, Okay. M., Kline, R. J. & Rahman, M. S. Previous, current, and future views of environmental DNA (eDNA) metabarcoding: A scientific assessment in strategies, monitoring, and functions of worldwide eDNA. Glob. Ecol. Conserv. 17, https://doi.org/10.1016/j.gecco.2019.e00547 (2019).
Bell, Okay. L. et al. Crops, pollinators and their interactions below world ecological change: The function of pollen DNA metabarcoding. Mol. Ecol. https://doi.org/10.1111/mec.16689 (2022).
Google Scholar
Bell, Okay. L. et al. Pollen DNA barcoding: present functions and future prospects. Genome 59, 629–640, https://doi.org/10.1139/gen-2015-0200 (2016).
Google Scholar
Dubois, B. et al. An in depth workflow to develop QIIME2-formatted reference databases for taxonomic evaluation of DNA metabarcoding knowledge. BMC Genom. Information 23, 53, https://doi.org/10.1186/s12863-022-01067-5 (2022).
Google Scholar
Frigerio, J. et al. DNA-Primarily based Natural Teas’ Authentication: An ITS2 and psbA-trnH Multi-Marker DNA Metabarcoding Strategy. Crops 10, https://doi.org/10.3390/plants10102120 (2021).
Zhang, G. X. et al. Tracing the Edible and Medicinal Plant Pueraria montana and Its Merchandise within the Market Yields Subspecies Stage Distinction Utilizing DNA Barcoding and DNA Metabarcoding. Entrance. Pharmacol. 11, https://doi.org/10.3389/fphar.2020.00336 (2020).
Anthoons, B. et al. Metabarcoding reveals low constancy and presence of poisonous species in brief chain-of-commercialization of natural merchandise. J Meals Compost Anal. 97, https://doi.org/10.1016/j.jfca.2020.103767 (2021).
Moorhouse-Gann, R. J. et al. New common ITS2 primers for high-resolution herbivory analyses utilizing DNA metabarcoding in each tropical and temperate zones. Sci. Rep. 8, 8542, https://doi.org/10.1038/s41598-018-26648-2 (2018).
Google Scholar
Wang, B. et al. Differences due to the season within the plant weight loss program of the Chinese language Monal revealed by fecal DNA metabarcoding evaluation. Avian Res. 13, https://doi.org/10.1016/j.avrs.2022.100034 (2022).
Fujii, T., Ueno, Okay., Shirako, T., Nakamura, M. & Minami, M. Identification of Lagopus muta japonica meals plant assets within the Northern Japan Alps utilizing DNA metabarcoding. PLoS One 17, https://doi.org/10.1371/journal.pone.0252632 (2022).
König, S., Krauss, J., Keller, A., Bofinger, L. & Steffan-Dewenter, I. Phylogenetic relatedness of meals crops reveals highest insect herbivore specialization at intermediate temperatures alongside a broad climatic gradient. Glob. Change Biol. 28, 4027–4040, https://doi.org/10.1111/gcb.16199 (2022).
Google Scholar
Bell, Okay. L. et al. Making use of pollen DNA metabarcoding to the research of plant–pollinator interactions. Appl. Plant Sci. 5, 1600124, https://doi.org/10.3732/apps.1600124 (2017).
Google Scholar
Arstingstall, Okay. A. et al. Capabilities and limitations of utilizing DNA metabarcoding to review plant-pollinator interactions. Mol. Ecol. 30, 5266–5297, https://doi.org/10.1111/mec.16112 (2021).
Google Scholar
Encinas-Viso, F. et al. Pollen DNA metabarcoding reveals cryptic range and excessive spatial turnover in alpine plant-pollinator networks. Mol. Ecol. https://doi.org/10.1111/mec.16682 (2022).
Google Scholar
Bell, Okay. L. et al. Crops, pollinators and their interactions below world ecological change: The function of pollen DNA metabarcoding. Mol. Ecol., 1–18, https://doi.org/10.1111/mec.16689 (2022).
Hawkins, J. et al. Utilizing DNA Metabarcoding to Establish the Floral Composition of Honey: A New Instrument for Investigating Honey Bee Foraging Preferences. PLoS One 10, e0134735, https://doi.org/10.1371/journal.pone.0134735 (2015).
Google Scholar
Milla, L., Schmidt-Lebuhn, A., Bovill, J. & Encinas-Viso, F. Monitoring of honey bee floral assets with pollen DNA metabarcoding as a complementary software to vegetation surveys. Ecol. Solut. Evid. 3, https://doi.org/10.1002/2688-8319.12120 (2022).
Khansaritoreh, E. et al. Using DNA metabarcoding to find out the geographical origin of honey. Heliyon 6, https://doi.org/10.1016/j.heliyon.2020.e05596 (2020).
Korpelainen, H. & Pietilainen, M. Biodiversity of pollen in indoor air samples as revealed by DNA metabarcoding. Nord. J. Bot. 35, 602–608, https://doi.org/10.1111/njb.01623 (2017).
Google Scholar
Omelchenko, D. O. et al. Evaluation of ITS1, ITS2, 5 ‘-ETS, and trnL-F DNA Barcodes for Metabarcoding of Poaceae Pollen. Variety 14, https://doi.org/10.3390/d14030191 (2022).
Fahner, N. A., Shokralla, S., Baird, D. J. & Hajibabaei, M. Giant-Scale Monitoring of Crops via Environmental DNA Metabarcoding of Soil: Restoration, Decision, and Annotation of 4 DNA Markers. PLoS One 11, https://doi.org/10.1371/journal.pone.0157505 (2016).
Vasconcelos, S. et al. Unraveling the plant range of the Amazonian canga via DNA barcoding. Ecol. Evol. 11, 13348–13362, https://doi.org/10.1002/ece3.8057 (2021).
Google Scholar
Timpano, E. Okay., Scheible, M. Okay. R. & Meiklejohn, Okay. A. Optimization of the second inner transcribed spacer (ITS2) for characterizing land crops from soil. PLoS One 15, https://doi.org/10.1371/journal.pone.0231436 (2020).
Yau, S. et al. Mantoniella beaufortii and Mantoniella baffinensis sp. nov. (Mamiellales, Mamiellophyceae), two new inexperienced algal species from the excessive arctic(1). J. Phycol. 56, 37–51, https://doi.org/10.1111/jpy.12932 (2020).
Google Scholar
Liu, Y. L., Xu, C., Dong, W. P., Yang, X. Y. & Zhou, S. L. Dedication of a felony suspect utilizing environmental plant DNA metabarcoding expertise. Forensic Sci. Int. 324, https://doi.org/10.1016/j.forsciint.2021.110828 (2021).
Higashi, Y., Hirota, S. Okay., Suyama, Y. & Yahara, T. Geographical and seasonal variation of plant taxa detected in faces of Cervus nippon yakushimae primarily based on plant DNA evaluation in Yakushima Island. Ecol. Res. 37, 582–597, https://doi.org/10.1111/1440-1703.12319 (2022).
Google Scholar
Fox, G. et al. Advanced city environments present Apis mellifera with a richer plant forage than suburban and extra rural landscapes. Ecol. Evol. 12, https://doi.org/10.1002/ece3.9490 (2022).
Quaresma, A. et al. Preservation strategies of honey bee-collected pollen aren’t a supply of bias in ITS2 metabarcoding. Environ. Monit. Assess. 193, https://doi.org/10.1007/s10661-021-09563-4 (2021).
Benson, D. A. et al. GenBank. Nucleic Acids Res. 45, D37–D42, https://doi.org/10.1093/nar/gkw1070 (2017).
Google Scholar
Banchi, E. et al. PLANiTS: a curated sequence reference dataset for plant ITS DNA metabarcoding. Database 2020, https://doi.org/10.1093/database/baz155 (2020).
Keller, A. et al. BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. Bioinformatics 36, 2630–2631, https://doi.org/10.1093/bioinformatics/btz960 (2020).
Google Scholar
Kraaijeveld, Okay. et al. Environment friendly and delicate identification and quantification of airborne pollen utilizing subsequent‐era DNA sequencing. Mol. Ecol. Resour. 15, 8–16, https://doi.org/10.1111/1755-0998.12288 (2015).
Google Scholar
Keller, A. et al. Evaluating multiplexed subsequent‐era sequencing as a way in palynology for combined pollen samples. Plant Biol. 17, 558–566, https://doi.org/10.1111/plb.12251 (2015).
Google Scholar
Richardson, R. T. et al. Rank-based characterization of pollen assemblages collected by honey bees utilizing a multi-locus metabarcoding method. Appl. Plant Sci. 3, 1500043, https://doi.org/10.3732/apps.1500043 (2015).
Google Scholar
Edwards, C. E., Swift, J. F., Lance, R. F., Minckley, T. A. & Lindsay, D. L. Evaluating the efficacy of pattern assortment approaches and DNA metabarcoding for figuring out the variety of crops utilized by nectivorous bats. Genome 62, 19–29, https://doi.org/10.1139/gen-2018-0102 (2019).
Google Scholar
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A flexible open supply software for metagenomics. PeerJ (2016).
Edgar, R. C. Search and clustering orders of magnitude sooner than BLAST. Bioinformatics 26, 2460–2461, https://doi.org/10.1093/bioinformatics/btq461 (2010).
Google Scholar
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Primary native alignment search software. J. Mol. Biol. 215, 403–410, https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).
Google Scholar
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian Classifier for Fast Task of rRNA Sequences into the New Bacterial Taxonomy. Appl Environ Microbiol 73, 5261–5267, https://doi.org/10.1128/AEM.00062-07 (2007).
Google Scholar
Edgar, R. C. SINTAX: a easy non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv, 074161, https://doi.org/10.1101/074161 (2016).
Schultz, J. et al. The inner transcribed spacer 2 database—an internet server for (not solely) low stage phylogenetic analyses. Nucleic Acids Res. 34, W704–W707, https://doi.org/10.1093/nar/gkl129 (2006).
Google Scholar
Ankenbrand, M. J., Keller, A., Wolf, M., Schultz, J. & Förster, F. ITS2 Database V: Twice as A lot. Mol. Biol. Evol. 32, 3030–3032, https://doi.org/10.1093/molbev/msv174 (2015).
Google Scholar
Sickel, W. et al. Elevated effectivity in figuring out combined pollen samples by meta-barcoding with a dual-indexing method. BMC Ecology 15, 1–9, https://doi.org/10.1186/s12898-015-0051-y (2015).
Google Scholar
Bell, Okay. L., Loeffler, V. M. & Brosi, B. J. An rbcL reference library to help within the identification of plant species mixtures by DNA metabarcoding. Appl. Plant Sci. 5, https://doi.org/10.3732/apps.1600110 (2017).
Wirta, H., Abrego, N., Miller, Okay., Roslin, T. & Vesterinen, E. DNA traces the origin of honey by figuring out crops, micro organism and fungi. Sci. Rep. 11, https://doi.org/10.1038/s41598-021-84174-0 (2021).
Bell, Okay. L. et al. Evaluating whole-genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures. Ecol. Evol. 11, 16082–16098, https://doi.org/10.1002/ece3.8281 (2021).
Google Scholar
Curd, E. E. et al. Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets. Strategies Ecol. Evol. 10, 1469–1475, https://doi.org/10.1111/2041-210x.13214 (2019).
Google Scholar
Richardson, R. T., Sponsler, D. B., McMinn-Sauder, H. & Johnson, R. M. MetaCurator: A hidden Markov model-based toolkit for extracting and curating sequences from taxonomically-informative genetic markers. Strategies Ecol. Evol. 11, 181–186, https://doi.org/10.1111/2041-210x.13314 (2020).
Google Scholar
Keck, F., Couton, M. & Altermatt, F. Navigating the seven challenges of taxonomic reference databases in metabarcoding analyses. Mol. Ecol. Resour., https://doi.org/10.1111/1755-0998.13746.
R: A language and setting for statistical computing (Vienna, Austria 2013).
Hackl, T., Hedrich, R., Schultz, J. & Förster, F. proovread: large-scale high-accuracy PacBio correction via iterative brief learn consensus. Bioinformatics 30, 3004–3011, https://doi.org/10.1093/bioinformatics/btu392 (2014).
Google Scholar
Deshpande, V. et al. Fungal identification utilizing a Bayesian classifier and the Warcup coaching set of inner transcribed spacer sequences. Mycologia 108, 1–5, https://doi.org/10.3852/14-293 (2016).
Google Scholar
Chen, S. et al. Validation of the ITS2 area as a novel DNA barcode for figuring out medicinal plant species. PLoS One 5, e8613, https://doi.org/10.1371/journal.pone.0008613 (2010).
Google Scholar
White, T. J., Bruns, T., Lee, S. J. W. T. & Taylor, J. in PCR protocols: a information to strategies functions (ed Gelfand, D. H. Innis, M. A., Sninsky, J. J., White, T. J.) 315-322 (Educational Press, 1990).
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, Okay. MEGA X: molecular evolutionary genetics evaluation throughout computing platforms. Mol. Biol. Evol. 35, 1547–1549, https://doi.org/10.1093/molbev/msy096 (2018).
Google Scholar
Quaresma, A. et al. ITS2 International database. Zenodo https://doi.org/10.5281/zenodo.7968519 (2023).
Quaresma, A. et al. ITS2 Crop database. Zenodo https://doi.org/10.5281/zenodo.7969940 (2023).
Quaresma, A. et al. ITS2 European international locations. Zenodo https://doi.org/10.5281/zenodo.7970046 (2023).
Cheng, T. et al. Barcoding the dominion Plantae: new PCR primers for ITS areas of crops with improved universality and specificity. Mol. Ecol. Resour. 16, 138–149, https://doi.org/10.1111/1755-0998.12438 (2016).
Google Scholar
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