References:
1. Bohmann, K., et al., Environmental DNA for wildlife biology and biodiversity monitoring. Trends in ecology & evolution, 2014.29 (6): p. 358-367.
2. Deiner, K., H. Yamanaka, and L. Bernatchez, The future of biodiversity monitoring and conservation utilizing environmental DNA.Environmental DNA, 2021. 3 (1): p. 3-7.
3. Harper, L.R., et al., Prospects and challenges of environmental DNA (eDNA) monitoring in freshwater ponds. Hydrobiologia, 2019.826 (1): p. 25-41.
4. McGee, K.M., C.V. Robinson, and M. Hajibabaei, Gaps in DNA-Based Biomonitoring Across the Globe. Frontiers in Ecology and Evolution, 2019. 7 (337).
5. Schenekar, T., et al., Reference databases, primer choice, and assay sensitivity for environmental metabarcoding: Lessons learnt from a re-evaluation of an eDNA fish assessment in the Volga headwaters. River Research and Applications, 2020. 36 (7): p. 1004-1013.
6. Folmer, O., et al., DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 1994.3 (5): p. 294–299.
7. China Plant BOL Group, Comparative analysis of a large dataset indicates that internal transcribed spacer (ITS) should be incorporated into the core barcode for seed plants. Proceedings of the National Academy of Sciences, 2011. 108 (49): p. 19641-19646.
8. Ratnasingham, S. and P.D.N. Hebert, BOLD : The barcode of life data system. Molecular Ecology Notes, 2007. 7 : p. 355–364.
9. Marques, V., et al., GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding. Diversity and Distributions.
10. Luo, A., et al., Potential efficacy of mitochondrial genes for animal DNA barcoding: a case study using eutherian mammals. BMC Genomics, 2011. 12 : p. 84.
11. Gold, Z., et al., Improving metabarcoding taxonomic assignment: A case study of fishes in a large marine ecosystem.Molecular Ecology Resources, 2021. 21 (7): p. 2546-2564.
12. D’Ercole, J., S.W.J. Prosser, and P.D.N. Hebert, A SMRT approach for targeted amplicon sequencing of museum specimens (Lepidoptera)—patterns of nucleotide misincorporation. PeerJ, 2021.9 : p. e10420.
13. Winker, K., Natural History Museums in a Postbiodiversity Era. BioScience, 2004. 54 (5): p. 455.
14. Wandeler, P., P.E. Hoeck, and L.F. Keller, Back to the future: museum specimens in population genetics. Trends Ecol Evol, 2007.22 (12): p. 634-42.
15. Hebert, P.D.N., et al., A DNA ‘Barcode Blitz’: Rapid Digitization and Sequencing of a Natural History Collection. PLOS ONE, 2013. 8 (7): p. e68535.
16. Shokralla, S., et al., Pyrosequencing for Mini-Barcoding of Fresh and Old Museum Specimens. PLoS ONE, 2011. 6 (7): p. e21252.
17. Boyer, S., et al., Sliding Window Analyses for Optimal Selection of Mini-Barcodes, and Application to 454-Pyrosequencing for Specimen Identification from Degraded DNA. PLoS ONE, 2012.7 (5): p. e38215.
18. Lindahl, T., Instability and decay of the primary structure of DNA. Nature, 1993. 362 (6422): p. 709-715.
19. Batovska, J., et al., Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches. Scientific Reports, 2021. 11 (1): p. 7946.
20. Carew, M.E., R.A. Coleman, and A.A. Hoffmann, Can non-destructive DNA extraction of bulk invertebrate samples be used for metabarcoding? PeerJ, 2018. 6 : p. e4980.
21. Wong, W.H., et al., ‘Direct PCR’ optimization yields a rapid, cost-effective, nondestructive and efficient method for obtaining DNA barcodes without DNA extraction. Molecular Ecology Resources, 2014.14 (6): p. 1271-1280.
22. Prosser, S.W.J., et al., DNA barcodes from century-old type specimens using next-generation sequencing. Molecular Ecology Resources, 2016. 16 (2): p. 487-497.
23. Shokralla, S., et al., Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform. Scientific Reports, 2015. 5 (1): p. 9687.
24. de Santana, C.D., et al., The critical role of natural history museums in advancing eDNA for biodiversity studies: a case study with Amazonian fishes. Sci Rep, 2021. 11 (1): p. 18159.
25. Moinet, G.Y.K., et al., Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Global Change Biology, 2021.
26. Toju, H., et al., Priority effects can persist across floral generations in nectar microbial metacommunities. Oikos, 2018.127 (3): p. 345-352.
27. Hamady, M., et al., Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nature Methods, 2008.5 (3): p. 235-237.
28. Tanabe, A.S. and H. Toju, Two New Computational Methods for Universal DNA Barcoding: A Benchmark Using Barcode Sequences of Bacteria, Archaea, Animals, Fungi, and Land Plants. PLoS ONE, 2013.8 (10): p. e76910.
29. Zhang, J., et al., PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics, 2014. 30 (5): p. 614-620.
30. Rognes, T., et al., VSEARCH: a versatile open source tool for metagenomics. PeerJ, 2016. 4 : p. e2584.
31. Rice, P., I. Longden, and A. Bleasby, EMBOSS: The European Molecular Biology Open Software Suite. Trends in Genetics, 2000.16 (6): p. 276-277.
32. Katoh, K. and D.M. Standley, MAFFT multiple sequence alignment software version 7: Improvements in performance and usability.Molecular Biology and Evolution, 2013. 30 (4): p. 772–780.
33. Price, M.N., P.S. Dehal, and A.P. Arkin, FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS ONE, 2010. 5 (3): p. e9490.
34. Yu, G., et al., ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution, 2017. 8 (1): p. 28-36.
35. Hebert, P.D.N., et al., A Sequel to Sanger: amplicon sequencing that scales. BMC Genomics, 2018. 19 .
36. Sire, L., et al., The Challenge of DNA Barcoding Saproxylic Beetles in Natural History Collections—Exploring the Potential of Parallel Multiplex Sequencing With Illumina MiSeq. Frontiers in Ecology and Evolution, 2019. 7 : p. 495.
37. Sint, D., L. Raso, and M. Traugott, Advances in multiplex PCR: balancing primer efficiencies and improving detection success. Methods in Ecology and Evolution, 2012. 3 (5): p. 898-905.
38. Hajibabaei, M., et al., A minimalist barcode can identify a specimen whose DNA is degraded. Molecular Ecology Notes, 2006.6 (4): p. 959-964.
39. Paniagua Voirol, L.R., et al., How the ‘kitome’ influences the characterization of bacterial communities in lepidopteran samples with low bacterial biomass. Journal of Applied Microbiology, 2021.130 (6): p. 1780-1793.
40. Sicard, M., M. Bonneau, and M. Weill, Wolbachia prevalence, diversity, and ability to induce cytoplasmic incompatibility in mosquitoes. Current Opinion in Insect Science, 2019. 34 : p. 12-20.
41. Potapov, V. and J.L. Ong, Examining Sources of Error in PCR by Single-Molecule Sequencing. PloS one, 2017. 12 (1): p. e0169774-e0169774.
42. Song, H., et al., Many species in one: DNA barcoding overestimates the number of species when nuclear mitochondrial pseudogenes are coamplified. Proc Natl Acad Sci U S A, 2008.105 (36): p. 13486-91.
43. Minich, J.J., et al., Quantifying and Understanding Well-to-Well Contamination in Microbiome Research. mSystems, 2019.4 (4): p. e00186-19.
44. Schnell, I.B., K. Bohmann, and M.T.P. Gilbert, Tag jumps illuminated – reducing sequence-to-sample misidentifications in metabarcoding studies. Molecular Ecology Resources, 2015.15 (6): p. 1289-1303.
45. Eisenhofer, R., et al., Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations. Trends Microbiol, 2019.27 (2): p. 105-117.
46. Bohmann, K., et al., Strategies for sample labelling and library preparation in DNA metabarcoding studies. Molecular Ecology Resources. n/a (n/a).
47. Sze, M.A. and P.D. Schloss, The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. mSphere, 2019. 4 (3): p. e00163-19.
48. Elbrecht, V., et al., Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ, 2019. 7 : p. e7745.
49. Lobo, J., et al., Enhanced primers for amplification of DNA barcodes from a broad range of marine metazoans. BMC Ecology, 2013.13 (1): p. 34.
50. Geller, J., et al., Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol Ecol Resour, 2013. 13 (5): p. 851-61.
51. Gibson, J.F., et al., Large-Scale Biomonitoring of Remote and Threatened Ecosystems via High-Throughput Sequencing. PLOS ONE, 2015.10 (10): p. e0138432.