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Comparing COI and ITS as DNA Barcode Markers for Mushrooms and Allies (Agaricomycotina)

  • Bryn T. M. Dentinger ,

    b.dentinger@kew.org

    Affiliations Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada, Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada, Jodrell Laboratory, Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom

  • Maryna Y. Didukh,

    Affiliations Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada, Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

  • Jean-Marc Moncalvo

    Affiliations Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada, Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

Comparing COI and ITS as DNA Barcode Markers for Mushrooms and Allies (Agaricomycotina)

  • Bryn T. M. Dentinger, 
  • Maryna Y. Didukh, 
  • Jean-Marc Moncalvo
PLOS
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Abstract

DNA barcoding is an approach to rapidly identify species using short, standard genetic markers. The mitochondrial cytochrome oxidase I gene (COI) has been proposed as the universal barcode locus, but its utility for barcoding in mushrooms (ca. 20,000 species) has not been established. We succeeded in generating 167 partial COI sequences (∼450 bp) representing ∼100 morphospecies from ∼650 collections of Agaricomycotina using several sets of new primers. Large introns (∼1500 bp) at variable locations were detected in ∼5% of the sequences we obtained. We suspect that widespread presence of large introns is responsible for our low PCR success (∼30%) with this locus. We also sequenced the nuclear internal transcribed spacer rDNA regions (ITS) to compare with COI. Among the small proportion of taxa for which COI could be sequenced, COI and ITS perform similarly as a barcode. However, in a densely sampled set of closely related taxa, COI was less divergent than ITS and failed to distinguish all terminal clades. Given our results and the wealth of ITS data already available in public databases, we recommend that COI be abandoned in favor of ITS as the primary DNA barcode locus in mushrooms.

Introduction

DNA barcoding is an approach to rapidly identify species using short, standard genetic markers [1], [2], [3], [4], [5]. The DNA barcoding approach to documenting diversity is particularly useful for groups of cryptic organisms like bacteria and fungi. Indeed, the challenge of documenting the 712 K to >15 million species of fungi, 90–95% of which remain undescribed [5], [6], [7], [8], [9], [10], is more tractable with molecular than with traditional methods, which add only about 1000 new species a year [11]. However, until recently, there has been very little effort to standardize the methods for molecular identification of fungal species and no one marker has been formally selected as a DNA barcode region in fungi.

To qualify as a DNA barcode region, a locus should be easy to amplify from most or all species in the target group using universal primers and show low intra-specific and high inter-specific divergence (creating a “barcode gap”). For animals, the mitochondrial cytochrome oxidase I (COI) locus appears to satisfy these criteria for most groups [1], [12], [13], [14]. However, COI has not been shown to be very effective outside of animals, although studies are limited. The plant barcoding initiative (http://www.barcoding.si.edu) has identified several alternative loci that may be used together for a multilocus barcode for land plants [15], [16], [17], [18], [19]. In fungi, the nuclear internal transcribed spacers of the ribosomal repeats (ITS), and less so divergent domains D1–D2 of the largest subunit of the ribosomal RNA (LSU), have long been used for this purpose [5], but length variation in these regions make sequence alignment difficult across divergent taxa, and there is still a lack of procedure standardization. COI may provide an advantage over these loci because alignment of this locus across a divergent set of taxa is trivial.

Although the idea of DNA barcoding is both essential to and already an informal part of much current research with fungi, only a few studies exist that examined the effectiveness of COI as a DNA barcode [20], [21], [22], [23], [24]. In the Ascomycota, COI was shown to be more effective than ITS, but less than beta tubulin A, for distinguishing among species of Penicillium subgenus Penicillium [21] while in Neohumicola COI and ITS provided similar resolution [22]. In contrast, Geiser et al. [20] and Gilmore et al. [23] reported low divergence between COI homologs in Aspergillus and Fusarium, respectively, and more critically, detected COI paralogs. In the Basidiomycota, Vialle et al. [24] were unable to amplify and sequence COI from most of the rust fungi (Urediniomycetes) they examined due to the presence of introns in the priming and sequencing regions and, when they did obtain sequences, variation was inferior to ITS and LSU. At least four introns occur in the COI coding region of the mushroom Agrocybe aegerita [25], 15 in Trametes cingulata [26], 19 in Agaricus bisporus [27], and either one (Agrocybe aegerita), two (Crinipellis perniciosa), or eight (A. bisporus) introns have been reported from the 600 bp “barcoding region” at the 5′-end of the gene [21], [27], suggesting that introns may also occur in other mushrooms and could be problematic as in the rust fungi. Overall, the utility of COI as a DNA barcode for species-level identification of mushrooms remains to be investigated comprehensively.

Here, we set out to facilitate the formalization of a DNA barcoding effort in mushrooms and their relatives (Basidiomycota:Agaricomycotina), representing about one-third of all known species of fungi [∼20,000 described species; 11], by evaluating COI as a DNA barcode for the group. The criteria we used included the ability to design universal primers, amplification success, and capacity to diagnose species using a phylogenetic approach. We also compared COI with ITS, the latter being an informal “standard” marker for species resolution in Agaricomycotina.

Materials and Methods

Ethics Statement

Newly collected specimens used in this study were obtained with permission from public and private lands. The permit for collecting specimens in Catoctin Mountain National Park was provided by the United States National Park Service to Steven Stephenson (CATO-2006-SCI-0005) and the permit for collecting specimens in Algonquin Provincial Park was provided by Ontario Parks to Jean-Marc Moncalvo (s.n.).

Taxon Sampling

Approximately six hundred and fifty mushrooms in three classes of Agaricomycotina (Tremellomycetes, Dacrymycetes, Agaricomycetes) were newly collected from Ontario, Québec, and Maryland for this study. Species identifications were made comparing macro-and micro-morphological features with species descriptions in the relevant taxonomic literature (too numerous to list). Vouchers of all newly collected specimens are deposited in the fungal herbarium of the Royal Ontario Museum (TRTC). Additional specimens used in this study were borrowed from other herbaria (MIN, HSC, BUF, LIP, SFSU, WTU, KUN, TMI, UC, USJ, and NY).

DNA extraction and sequencing

DNA was extracted following various standard protocols [28], [29] or using a new rapid DNA isolation protocol developed in our lab [30]. Approximately 530-666 bp of exon from the 5′ end of the COI gene was amplified by designing taxonomically nonspecific primers (Table 1 and Figure 1) based on an alignment of fungal COI sequences in GenBank (Cryptococcus neoformans AY560609, Moniliophthora perniciosa NC005927, Schizophyllum commune AF402141, Agrocybe aegerita AF010257). PCR amplification was achieved using a cycling program with an initial denaturation of 95C for 2 min, followed by 30 cycles of denaturation at 94 C for 45 sec, annealing at 60 C for 45 sec, and extension at 72 C for 1 min 10 sec, followed by a final extension at 72 C for 7 min and an indefinite refrigeration at 4 C. New primers “COXBOL1-F” and “COXBOL1-R” were also designed for improved amplification of COI from genomic DNA for Boletales (Table 1) based on an initial alignment of Boletales sequences obtained using general primers. A touchdown program was used with the Boletales-specific primers: initial denaturation step for 2 min at 94 C, followed by 5 cycles of 94 C (30 sec), 55 C (30 sec), and 72 C (1 min), followed by 25 cycles of 94 C (30 sec), 50 C (30 sec), and 72 C (1 min), and ending with a final extension step at 72 C for 7 min. The ITS region was amplified and sequenced with primers ITS1F and ITS4 using standard protocols [31], [32] or with primers ITS8F and ITS6R using a new high-throughput protocol [30]. PCR products were visualized by UV fluorescence after running out 3-25 µL PCR products in a 1% agarose gel containing 0.005% ethidium bromide. Prior to sequencing, positive PCRs were cleaned one of two ways: 1) by incubating samples for 15 min at 37C then 15 min at 80 C after adding 0.4 volumes of a mixture containing shrimp alkaline phosphatase (0.05 units/µL) and exonuclease I (0.05 units/µL) in water, 2) loading the entire PCR in a single lane, running the gel at 90 V for ca. 1 h, cutting the band from the gel using a clean razor, placing the excised gel in the top of a disposable pipette tip with a filter and trimmed to ∼1 cm in length, placing the pipette tip in a 1.5 mL microfuge tube, and collecting the liquid in the gel by spinning the tube for 10 min at 10,000 g. Unidirectional dye-terminator sequencing used the ABI BigDye kit (Foster City, CA) and reactions were run on an ABI PRISM 3100 DNA Analyzer in the Department of Natural History at the Royal Ontario Museum, Toronto, after ethanol precipitation and resuspension in HiDi formamide. Sequences were edited using Sequencher3.1 (GeneCodes, Ann Arbor, MI) and are available as a project on the Barcode of Life Data Systems online database [“Evaluating COI in mushrooms and allies (Agaricomycotina)” Project code YYY; www.barcodinglife.org; 33] and through GenBank [Accession numbers JN020964-JN021114 (ITS), JN029360-JN029526 (COI); 34]. Additional sequences obtained from GenBank that were used in this study include the following accession numbers: EU231946, EU231948, EU231949, EU231954, EU231958, EU231966, EU231969, EU231971, EU231980, EU231982, EU231983, EU231984, EU231985, EU231990, EU231991, EU231992.

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Figure 1. Positions of the four most successful PCR primers and introns encountered in mushroom cytochrome oxidase I (COI) genes relative to COI exon regions of Agrocybe aegerita (GenBank Accession AF010257).

https://doi.org/10.1371/journal.pone.0025081.g001

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Table 1. Primers designed for amplification of COI from Agaricomycotina.

https://doi.org/10.1371/journal.pone.0025081.t001

Alignment and Phylogenetic Analyses

COI.

Intron sequences from COI were removed before aligning the exon regions using ClustalW2 [34] as implemented in SeaView v4.2.9 [36], [37]. The best-fitting evolutionary model for the data was selected using the AIC in jModelTest v0.1.1 [38]. Phylogenetic analysis under the maximum likelihood framework was conducted using PhyML 3.0 [39] as implemented in SeaView v4.2.9 from 10 random starting trees and selecting the best of NNI and SPR branch swapping tree searching strategies. Branch support was estimated using the approximate likelihood ratio test (aLRT) function [40] implemented in PhyML 3.0. Intra- and inter-specific divergences were calculated using the Kimura two-parameter model (K2P)-corrected distances using the x86 version of PAUP*v4.0d90 [41].

ITS.

Complete ITS sequences were trimmed to begin and end with the conserved motifs 5′-(...gat)CATTA— and —GACCT(caaa...)-3′ to facilitate alignment. These motifs correspond to the conserved 3′ and 5′ termini of the flanking SSU and LSU genes, respectively. Because ITS sequences are length-variable regions with rapid substitution rates, a global alignment cannot be unambiguously constructed. Therefore, in order to ensure proper assessment of character homology between sequences, we identified “alignment groups” of sequences with 80% or greater similarity, a threshold that enabled unambiguous alignment. Any sequences that were greater than 20% dissimilar to each other were assumed to represent different species (intraspecific ITS variability in fungi was calculated to be 2.51% with a standard deviation of 4.57 [42]). To quickly identify these alignment groups, we utilized the “contig assembly” algorithm in Sequencher 4.10.1 (GeneCodes, Ann Arbor, MI). We first identified (based on annotated GenBank records) and removed the 5.8S subunit that separates the ITS1 and ITS2 regions to eliminate problems associated with high homology in this highly conserved ribosomal region but low homology in the flanking ITS regions. Second, we imported a FASTA file containing the concatenated sequences of ITS regions 1 and 2 sequences into Sequencher. Next, we generated alignment groups using the “Assemble Automatically” procedure with “Minimum Match Percentage” set to 80, “Minimum Overlap” set to 100 bp, and the contig consensus type set to “Consensus Inclusively.” The sequences in the “contig” folders were then exported and aligned using the default options in MUSCLE v3.8.31 [43] as implemented in SeaView v4.2.9. Of these aligned files, those with 3 taxa were clustered in PAUP*4.0b10 based on K2P-corrected distances using the BioNJ algorithm and the “Break Ties” option set to random. For all alignments with >3 taxa, maximum likelihood searches under the GTR+G+I model were conducted using SeaView as described above. Intra- and inter-specific divergences were calculated using the Kimura two-parameter model (K2P)-corrected distances in PAUP*.

For direct comparison of intra- and inter-specific divergences, genetic distances were calculated in PAUP*v4b10 [41] using pairwise distances corrected using the Kimura 2-Parameter (K2P) model for all sequences within the ITS alignment groups and their corresponding COI sequences. For each dataset, the mean, median, and maximum intra-specific distances and the mean, median, and minimum inter-specific distances were determined. Inter-specific distances were calculated by comparing the minimum distance between each species and its sister taxon. Ratios of minimum inter- to maximum intra-specific distances were plotted in Excel (Microsoft, Seattle, WA).

Results

Taxon Sampling

Our sampling included ca. 184 species in 73 genera (as identified by morphological traits). More than 90% of specimens (144 spp. in 49 genera) are members of the principal gilled-mushroom subclass Agaricomycetidae. Of the remaining, ca. 40 species belong to the Tremellomycetes, Dacrymycetes, and other orders of Agaricomycetes excluding the subclass Phallomycetidae [44].

DNA extraction and sequencing

Only 204 specimens (31.4%) yielded COI sequences, representing 57.7% and 61.3% of all species and genera sampled, respectively. The most successful primer combinations for COI amplification were 11F/2eR (615 bp amplicon), 12F/4R (529 bp amplicon), and 8F/2eR (666 bp amplicon). Most COI sequences used for this study were generated using combinations 11F/2eR or 12F/4R and trimmed to approximately 450 bp. Introns were found in 14 sequences (∼7% of those that amplified) in 18 species from 12 genera. Introns occurred in nine different locations (Fig. 1). Identical insertion/deletion sites occurred in sequences from specimens of different genera (e.g., Marasmius androsaceus, and Lepiota clypeolaria have an intron inserted at position 4), but these locations were not conserved within genera (e.g., Laccaria). In addition, presence/absence of intron was not always consistent between multiple specimens of the same species (e.g., Amanita fulva).

Alignment and Phylogenetic Analyses

A total of 167 specimens from 102 morphospecies of Agaricomycetidae, including 97 taxa belonging to the Boletales and Agaricales and 5 taxa belonging to the Polyporales (Ganoderma, Merulius, Polyporus), Russulales (Albatrellus), and Thelephorales (Sarcodon), were selected to compare COI with ITS. Thirty-six of the 102 morphospecies were represented by more than one sequence (2–9 sequences/species). Phylogenetic analysis of the COI dataset resulted in a moderately supported tree (aLRT≥0.95) and generally reflects the known topology of the Agaricomycetidae, except for the placement of Polyporus badius near Suillus and a non-monophyletic Boletales.

Intra- and inter-specific divergence was calculated for 30 species represented by more than one sequence in both the COI and ITS datasets based on global alignments of each gene (Table 2). For COI, the mean±standard error intra-specific divergence was 0.0009±0.0004 (0.0000 median, 0.0064 maximum) and mean±standard error inter-specific divergence was 0.0569±0.0079 (0.0428 median, 0.0043 minimum). For ITS, the mean±standard error intra-specific divergence was 0.0063±0.0030 (0.0011 median, 0.0747 maximum). In our calculation, inter-specific divergence for ITS was set at 21% when the divergence was greater than 20% (a minimum estimate of divergence); mean±standard error inter-specific divergence was 0.1499±0.0156 (0.2100 median, 0.0258 minimum).

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Table 2. Comparison of COI and ITS divergences using maximum intra-specific and minimum inter-specific divergences for the set of ITS alignment groups identified using Sequencher.

https://doi.org/10.1371/journal.pone.0025081.t002

A side-by-side comparison reveals that COI and ITS distances are qualitatively similar (Table 2). Except for three cases in Boletus spp., all ratios of inter- to intra-specific divergences for COI and ITS were greater than one, the minimum threshold for a barcode locus (Table 2). In general, intra-specific divergences were slightly higher for ITS than COI. Inter-specific divergence was also higher for ITS than COI except between Amanita flavoconia and A. rubescens, where minimum inter-specific divergence was great for COI than ITS. The highest maximum intra-specific divergence was 7.47% for ITS (Hygrocybe miniata) and 0.64% for COI (Entoloma sp. parasite on T. focale and Hygrocybe miniata). The lowest minimum inter-specific divergence for ITS was 0.96% (Boletus regineus) and 0.20% for COI (Boletus spp.).

COI and ITS largely agree on terminal taxa, which corresponds well with morphospecies (Fig. 2). Discrepancy between terminal taxa recovered by COI and ITS was limited to the porcini group of Boletus where intraspecific sampling was most extensive. In one case, ITS recovers B. nobilissimus as distinct from B. quercophilus, but COI does not. Similarly, ITS recovers a monophyletic B. edulis sensu stricto [45], whereas COI groups B. rex-veris with one specimen of B. edulis.

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Figure 2. PhyML phylogram of 167 partial COI (left) and PhyML or Neighbor-joining distance phylograms of full and partial ITS (right) sequences from ∼100 species of mushrooms.

Thickened lines represent branches that received ≥0.95 aLRT. Stars indicate COI sequences with an intron in the barcode region. Superscript numbers next to stars correspond to the position of the intron (Fig. 1). Branching for ITS sequences reflect ML- or NJ-based analysis of contigs formed using Sequencher; branching is absent where inter-specific distances are greater than the 20% cutoff value used in the Sequencher contig assembly. Letters at nodes in the genus Pluteus represent the phylogenetic species (reciprocally monophyletic with posterior probability of ≥0.95) of the Pluteus cervinus complex.

https://doi.org/10.1371/journal.pone.0025081.g002

Discussion

Amplification of COI

Like Vialle et al. [24] in a different group of basidiomycetes (rusts), we encountered low PCR success with COI in mushrooms. Even using multiple combinations of primers, our success rate was poor. Post-PCR processing of COI resulted in even worse success rates because multiple, large introns made sequencing difficult. In our set of sequences, COI exons may be interrupted by one or more large introns (∼1500 bp) with variable positions that are not phylogenetically conserved (Fig. 1, 2). Thus, even after considerable effort we succeeded in generating COI sequences for only ∼30% of the specimens, a rate much lower than typically observed for ITS [e.g, 30]. We also encountered possible paralogous copies of COI from some specimens that were sequenced twice or more (data not shown), confirming in mushrooms similar problems recently reported with this locus for Fusarium [23]. Moreover, unlike the ITS, designing universal COI primers for mushrooms (let alone all fungi) is not possible. Thus, each order, family, or even genus would probably require extensive troubleshooting and primer optimization, which is time-consuming, costly, antithetical to high-throughput data acquisition, and impractical for detection of fungi from environmental samples.

Performance of COI versus ITS as barcodes

Overall, the performances of COI and ITS as barcode markers for mushrooms are similar. Both generally exhibit low intra-specific and high inter-specific divergences in our dataset and both were able to distinguish most of the morphospecies we sampled (Table 2; Fig. 2). However, COI failed to recover two morphospecies (Boletus edulis and B. nobilissimus) that are recovered as reciprocally monophyletic by ITS, in agreement with a recent multigene phylogeny [45]. Moreover, the ratio of maximum intraspecific to minimum interspecific distance was greater for ITS than COI in 27/33 comparisons (82%; Table 2), indicating that ITS has a more pronounced “gap” between species. Amplification and sequencing notwithstanding, ITS appears to be better than COI as a primary DNA barcode locus for mushrooms.

Alignability of ITS

The inability to produce a reliable alignment of ITS sequences across a divergent set of taxa is a major disadvantage of using ITS as a barcode. We avoided relying on a global alignment of ITS sequences by first identifying “alignment groups” where assessment of homologous characters for a set of sequences is trivial using widely used alignment software. Our approach relied on the contig assembly algorithm employed by Sequencher, which is normally used to assemble complementary sequence trace files output from ABI sequencers. By eliminating the ∼120 bp of the largely invariable 5.8S RNA subunit, increasing the minimum overlap to the maximum (100 bp), and using a threshold of 80% similarity, we avoided the problem of underestimating sequence divergence among a set of sequences, thereby minimizing ambiguous homology assessment. However, there are two related problems with this method: 1) contig folders must contain sets of sequences with 80% or better similarity, but some sequences may be 80% similar to two or more sets of sequences and could legitimately reside in more than one alignment group, and 2) the contig assembly algorithm is likely to be order-dependent (the assembly algorithm in Sequencher is proprietary and therefore unknown), so when a sequence could reside in two folders it may always preferentially be placed in the first contig folder encountered even if its closest relative is in another folder. To explore whether or not the algorithm employed by Sequencher was biasing our contig assembly, we added to the ITS dataset dummy sequences where we modified the number of substitutions (randomly distributed) and gaps (randomly and evenly distributed or at a single site of extension) of the sequence with the greatest inter-specific divergence (Entoloma aff. sericeum ALG-07-108) from one of the contig folders. Our dummy sequences ranged from 1–20% divergent from the original. Except for two situations, where gaps are randomly distributed across over 15% or more of the sequence and when gaps are evenly distributed across 12% or more of the sequences (an extra base inserted every 8th position), Sequencher always correctly placed the two closest sequences together. Although the two cases where Sequencher did not correctly assign the two closest sequences together is of some concern, these synthetic scenarios are not very likely to be encountered with real biological material and we did not observe any misleading bias using the real data.

Ideally, a global alignment would be possible for phylogenetic-based methods of identification and this is still a characteristic that may be sought in alternative barcoding loci for mushrooms. But the competency of ITS as a barcode demonstrated here and elsewhere, as well as the wealth of ITS data that can be mined from publicly accessible databases such as GenBank and UNITE [46], further recommends it over COI for mushrooms and other fungi [5].

Conclusion

The barcode locus that was been chosen for animals, COI, will not work for mushrooms, rusts [24], and probably most other fungi [5], primarily due to the variable and unpredictable presence of large introns in the barcode region. Alternatively, the widely used ITS regions work well for identifying mushroom species, as has been determined empirically by mycologists and other fungal researchers for over a decade. Although ITS has limitations [42], [47], there is to date no better single molecular marker for barcoding mushroom species, and its versatility makes it possible to survey and discover new fungi from environment samples. With the development of high-throughput methods for producing ITS barcodes from mushrooms [30] and the relatively low cost of traditional (Sanger) sequencing, coupled with a cross-discipline desire for reliable molecular tools for the identification of environmental samples [48], the foundations have been laid for a worldwide fungal barcoding campaign. It is time to move forward with a global fungal barcoding initiative that adheres to standard protocols, from processing of new field collections to generating barcodes to integrating barcodes with taxonomy.

Acknowledgments

We thank the Wildlife Research Center in Algonquin Provincial Park, Ontario, the National Park Service staff at Catoctin Mountain National Park, Maryland, and Gilbert, André Fortin, and the Cree First Nation in Chisasibi, Québec, for facilitating fieldwork for this project. We thank Dennis Desjardin, Roy Halling, David McLaughlin, Pierre-Arthur Moreau, Eiji Nagasawa, and Andy Taylor for providing valuable specimens of boleti. We also thank Simona Margaritescu and Kristen Choffe at the ROM for help with the lab procedures and Pauline Fung at CAGEF (University of Toronto) for operating the DNA sequencer.

Author Contributions

Conceived and designed the experiments: BTMD MYD J-MM. Performed the experiments: BTMD MYD. Analyzed the data: BTMD. Wrote the paper: BTMD JM-M.

References

  1. 1. Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proc Natl Acad Sci U S A 270: 313–321.
  2. 2. Hajibabaei M, Singer GAC, Hebert PDN, Hickey DA (2007) DNA barcoding: how it complements taxonomy, molecular phylogenetics and population genetics. Trends Genet 23: 167–172.
  3. 3. Kress WJ, Erickson D (2008) DNA barcoding — a windfall for tropical biology? Biotropica 40: 405–408.
  4. 4. Janzen DH, Hallwachs W, Blandin P, Burns JM, Cadiou JM, et al. (2009) Integration of DNA barcoding into an ongoing inventory of complex tropical biodiversity. Mol Ecol Res 9(s1): 1–26.
  5. 5. Seifert KA (2009) Progress towards DNA barcoding of fungi. Mol Ecol Res 9(s1): 83–89.
  6. 6. Hawksworth DL (2001) The magnitude of fungal diversity: the 1.5 million species estimate revisited. Mycol Res 105: 1422–1432.
  7. 7. Hawksworth DL (2004) Fungal diversity and its implications for genetic resource collections. Stud Mycol 50: 9–18.
  8. 8. O'Brien HE, Parrent JL, Jackson JA, Moncalvo JM, Vilgalys R (2005) Fungal community analysis by large-scale sequencing of environmental samples. Appl Environ Microb 71: 5544–5550.
  9. 9. Schmitt JP, Mueller GM (2007) An estimate of the lower limit of global fungal diversity. Biodivers Conserv 16: 99–111.
  10. 10. Blackwell M (2011) The Fungi: 1, 2, 3 ... 5.1 million species? Am J Bot 98: 426–438.
  11. 11. Kirk PM, Cannon PF, Minter DW, Stalpers JA (2008) Dictionary of the Fungi, 10th Edition. Wallingford: CABI Publishing. 784 p.
  12. 12. Smith MA, Fisher BL, Hebert PDN (2005) DNA barcoding for effective biodiversity assessment of a hyperdiverse arthropod group: the ants of Madagascar. Phil Trans R Soc B 360: 1825–1834.
  13. 13. Vences M, Thomas M, Bonett RM, Vieites DR (2005) Deciphering amphibian diversity through DNA barcoding: chances and challenges. Phil Trans R Soc B 360: 1859–1868.
  14. 14. Ward RD, Zemlak TS, Innes BH, Last PR, Hebert PDN (2005) DNA barcoding Australia's fish species. Phil Trans R Soc B 360: 1847–1857.
  15. 15. Chase MW, Cowan RS, Hollingsworth PM, van den Berg C, Madriñan S, et al. (2007) A proposal for a standardised protocol to barcode all land plants. Taxon 56: 295–299.
  16. 16. Kress WJ, Erickson D (2007) A two-locus global DNA barcode for land plants: The coding rbcL gene complements the non-coding trnH-psbA spacer region. PLoS ONE 2: e508.
  17. 17. Newmaster SG, Fazekas AJ, Ragupathy S (2006) DNA barcoding in land plants: evaluation of rbcL in a multigene tiered approach. Can J Bot 84: 335–341.
  18. 18. Lahaye R, van der Bank M, Bogarin D, Warner J, Pupulin F, et al. (2008) DNA barcoding the floras of biodiversity hotspots. Proc Natl Acad Sci U S A 105: 2923–2928.
  19. 19. Hollingsworth ML, Clark A, Forrest LL, Richardson JE, Pennington RT, et al. (2009) Selecting barcoding loci for plants: evaluation of seven candidate loci with species-level sampling in three divergent groups of land plants. Mol Ecol Res 9: 439–457.
  20. 20. Geiser DM, Klich MA, Frisvad JC, Peterson SW, Varga J, et al. (2007) The current status of species recognition and identification in Aspergillus. Stud Mycol 59: 1–10.
  21. 21. Seifert KA, Samson RA, Dewaard JR, Houbraken J, Lévesque CA, et al. (2007) Prospects for fungus identification using CO1 DNA barcodes, with Penicillium as a test case. Proc Natl Acad Sci U S A 104: 3901–3906.
  22. 22. Nguyen HDT, Seifert KA (2008) Description and DNA barcoding of three new species of Leohumicola from South Africa and the United States. Persoonia 21: 57–69.
  23. 23. Gilmore SR, Gräfenhan T, Louis-Seize G, Seifert KA (2009) Multiple copies of cytochrome oxidase 1 in species of the fungal genus Fusarium. Mol Ecol Res 9(s1): 90–98.
  24. 24. Vialle A, Feau N, Allaire M, Didukh M, Martin M, et al. (2009) In silico evaluation of mitochondrial genes as DNA barcode for Basidiomycota. Mol Ecol Res 9(s1): 99–113.
  25. 25. Gonzalaz P, Barroso G, Labarère J (1998) Molecular analysis of the split cox1 gene from the Basidiomycota Agrocybe aegerita: relationship of its introns with homologous Ascomycota introns and divergence levels from common ancestral copies. Gene 220: 45–53.
  26. 26. Haridas S, Gantt JS (2010) The mitochondrial genome of the wood-degrading basidiomycete Trametes cingulata. FEMS Microbiol Lett 308: 29–34.
  27. 27. Férandon C, Moukha S, Callac P, Benedetto JP, Castroviejo M, et al. (2011) The Agaricus bisporus cox1 gene: The longest mitochondrial gene and the largest reservoir of mitochondrial group I introns. PLoS One 5: e14048.
  28. 28. Lee SB, Taylor JW (1990) Isolation of DNA from fungal mycelia and single spores. In: Innis M, Gelfand D, Sninsky J, White T, editors. PCR Protocols: A Guide to Methods and Applications. Orlando: Academic Press. pp. 282–287.
  29. 29. Zolan ME, Pukkila PJ (1986) Inheritance of DNA methylation in Coprinus cinereus. Mol Cell Biol 6: 195.
  30. 30. Dentinger BTM, Margaritescu S, Moncalvo JM (2010) Rapid and reliable high-throughput methods of DNA extraction for use in barcoding and molecular systematics of mushrooms. Mol Ecol Res 10: 628–633.
  31. 31. White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenies. In: Innis M, Gelfand D, Sninsky J, White T, editors. PCR Protocols: A Guide to Methods and Applications. Orlando: Academic Press. pp. 315–322.
  32. 32. Gardes M, Bruns TD (1993) ITS primers with enhanced specificity for basidiomycetes - application to the identification of mycorrhizae and rusts. Mol Ecol 2: 113–118.
  33. 33. Ratnasingham S, Hebert PDN (2007) BOLD : The Barcode of Life Data System. Mol Ecol Notes 7: 355–364.
  34. 34. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2005) GenBank. Nucleic Acids Res 33: D34–D38.
  35. 35. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948.
  36. 36. Galtier N, Gouy M, Gautier C (1996) SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny. Comput Appl Biosci 12: 543–548.
  37. 37. Gouy M, Guindon S, Gascuel O (2010) SeaView version 4 : a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol 27: 221–224.
  38. 38. Posada D (2008) jModelTest: Phylogenetic Model Averaging. Mol Biol Evol 25: 1253–1256.
  39. 39. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52: 696–704.
  40. 40. Anisimova M, Gascuel O (2006) Approximate likelihood ratio test for branches: A fast, accurate and powerful alternative. Syst Biol 55: 539–552.
  41. 41. Swofford DL (2003) PAUP* Phylogenetic Analysis Using Parsimony (* and other methods), Version 4. Sunderland: Sinauer.
  42. 42. Nilsson RH, Kristiansson E, Ryberg M, Hallenberg N, Larsson KH (2008) Intraspecific ITS variability in the Kingdom Fungi as expressed in the international sequence databases and its implications for molecular species identification. Evol Bioinform Online 4: 193–201.
  43. 43. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32: 1792–97.
  44. 44. Hibbett DS, Binder M, Bischoff JF, Blackwell M, Cannon PF, et al. (2007) A higher-level phylogenetic classification of the Fungi. Mycol Res 111: 509–547.
  45. 45. Dentinger BTM, Ammirati JF, Both EE, Desjardin DE, Halling RE, et al. (2010) Molecular phylogenetics of porcini mushrooms (Boletus section Boletus). Mol Phylogenet Evol 57: 1276–1292.
  46. 46. Kõljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, et al. (2005) UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166: 1063–1068.
  47. 47. Begerow D, Nilsson H, Unterseher M, Maier W (2010) Current state and perspectives of fungal DNA barcoding and rapid identification procedures. Appl Microbiol Biot 87: 99–108.
  48. 48. Bruns TD, Arnold AE, Hughes KW (2008) Fungal networks made of humans: UNITE, FESIN, and frontiers in fungal ecology. New Phytol 177: 586–588.