The DOI matches its Crossref record; 152 of the 152 references it cites were found in a registry or an index.
Fungal Identification Using Molecular Tools: A Primer for the Natural Products Research Community
Open accessAbstract
Fungi are morphologically, ecologically, metabolically, and phylogenetically diverse. They are known to produce numerous bioactive molecules, which makes them very useful for natural products researchers in their pursuit of discovering new chemical diversity with agricultural, industrial, and pharmaceutical applications. Despite their importance in natural products chemistry, identification of fungi remains a daunting task for chemists, especially those who do not work with a trained mycologist. The purpose of this review is to update natural products researchers about the tools available for molecular identification of fungi. In particular, we discuss (1) problems of using morphology alone in the identification of fungi to the species level; (2) the three nuclear ribosomal genes most commonly used in fungal identification and the potential advantages and limitations of the ITS region, which is the official DNA barcoding marker for species-level identification of fungi; (3) how to use NCBI-BLAST search for DNA barcoding, with a cautionary note regarding its limitations; (4) the numerous curated molecular databases containing fungal sequences; (5) the various protein-coding genes used to augment or supplant ITS in species-level identification of certain fungal groups; and (6) methods used in the construction of phylogenetic trees from DNA sequences to facilitate fungal species identification. We recommend that, whenever possible, both morphology and molecular data be used for fungal identification. Our goal is that this review will provide a set of standardized procedures for the molecular identification of fungi that can be utilized by the natural products research community.
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
MUSCLE: multiple sequence alignment with high accuracy and high throughput
Edgar, R. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh340 · Cited by 44,662
Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics
White, T. J., Bruns, T., Lee, S., & Taylor, J. (1990) · PCR Protocols · doi:10.1016/b978-0-12-372180-8.50042-1 · Cited by 36,648
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
Stamatakis, A. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl446 · Cited by 15,582
Evolutionary trees from DNA sequences: A maximum likelihood approach.
Felsenstein, J. (1981) · Journal of Molecular Evolution · doi:10.1007/bf01734359 · Cited by 14,571
Biological identifications through DNA barcodes
Hebert, P. D. N., Cywinska, A., Ball, S. L., & deWaard, J. R. (2003) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.2002.2218 · Cited by 13,376
PCR protocols — A guide to methods and applications
(1990) · Trends in Genetics · doi:10.1016/0168-9525(90)90186-a · Cited by 11,781
Creating the CIPRES Science Gateway for inference of large phylogenetic trees
Miller, M. A., Pfeiffer, W., & Schwartz, T. (2010) · 2010 Gateway Computing Environments Workshop (GCE) · doi:10.1109/gce.2010.5676129 · Cited by 11,228
jModelTest: Phylogenetic Model Averaging
Posada, D. (2008) · Molecular Biology and Evolution · doi:10.1093/molbev/msn083 · Cited by 10,499
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Current challenges and best-practice protocols for microbiome analysis
Bharti, R., & Grimm, D. G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbz155 · Cited by 592
Senanayake, I. (2020) · Mycosphere · doi:10.5943/mycosphere/11/1/20 · Cited by 592
Deep learning approach to describe and classify fungi microscopic images
Zieliński, B., Sroka-Oleksiak, A., Rymarczyk, D., Piekarczyk, A., & Brzychczy-Włoch, M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0234806 · Cited by 126
Metadata
- Authors
- Raja, H. A., Miller, A. N., Pearce, C. J., & Oberlies, N. H.
- Venue
- Journal of Natural Products
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-02-15
- Volume
- 80
- Issue
- 3
- Pages
- 756-770
- ISSN
- 0163-3864 (ISSN-L)
- 1520-6025
- OpenAlex
- W2588207325
- Cited by
- 931
- References
- 152
- FWCI
- 30.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
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