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R E S E A R C H A R T I C L E Open Access
Effectiveness of ITS and sub-regions as
DNA barcode markers for the identification
of Basidiomycota (Fungi)
Fernanda Badotti
1
, Francislon Silva de Oliveira
2
, Cleverson Fernando Garcia
1
, Aline Bruna Martins Vaz
3,4
,
Paula Luize Camargos Fonseca
3
, Laila Alves Nahum
2,5
, Guilherme Oliveira
2,6
and Aristóteles Góes-Neto
3*
Abstract
Background: Fungi are among the most abundant and diverse organisms on Earth. However, a substantial amount
of the species diversity, relationships, habitats, and life strategies of these microorganisms remain to be discovered
and characterized. One important factor hindering progress is the difficulty in correctly identifying fungi.
Morphological and molecular characteristics have been applied in such tasks. Later, DNA barcoding has emerged as a
new method for the rapid and reliable identification of species. The nrITS region is considered the universal barcode of
Fungi, and the ITS1 and ITS2 sub-regions have been applied as metabarcoding markers. In this study, we performed a
large-scale analysis of all the available Basidiomycota sequences from GenBank. We carried out a rigorous trimming of
the initial dataset based in methodological principals of DNA Barcoding. Two different approaches (PCI and barcode
gap) were used to determine the performance of the complete ITS region and sub-regions.
Results: For most of the Basidiomycota genera, the three genomic markers performed similarly, i.e., when one was
considered a good marker for the identification of a genus, the others were also; the same results were observed when
the performance was insufficient. However, based on barcode gap analyses, we identified genomic markers that had a
superior identification performance than the others and genomic markers that were not indicated for the identification
of some genera. Notably, neither the complete ITS nor the sub-regions were useful in identifying 11 of the 113
Basidiomycota genera. The complex phylogenetic relationships and the presence of cryptic species in some genera are
possible explanations of this limitation and are discussed.
Conclusions: Knowledge regarding the efficiency and limitations of the barcode markers that are currently used for
the identification of organisms is crucial because it benefits research in many areas. Our study provides information
that may guide researchers in choosing the most suitable genomic markers for identifying Basidiomycota species.
Keywords: ITS, ITS1, ITS2, Probable correct identification, Barcode gap, Basidiomycota
Background
Fungi are one of the major eukaryotic lineages that are
equivalent in species number to animals but exceed that
of plants [1]. Fungi are among the most important organ-
isms in the world because of their vital roles in decompos-
ition, nutrient cycling, and obligate mutualistic symbioses
with plants, algae, and cyanobacteria [2]. Fungi also have
great economic importance for industrial fermentation,
pharmaceutical, and biotechnological industries [3]. They
may also cause food spoilage and diseases in plants and
animals [4]. The diversity of activities is reflected in the
high number of taxa, morphologies, habitats, and life
strategies used by this group of organisms. Further studies
are necessary to better understand their complex interac-
tions with other organisms and environments.
The phylum Basidiomycota is the second largest of
the Fungi kingdom and comprises approximately 30%
of all described fungal species [5]. This diverse phylum
includes primarily macroscopic but also microscopic
fungi, such as mushrooms and basidiomycotan yeasts,
* Correspondence: arigoesneto@icb.ufmg.br
3
Universidade Federal de Minas Gerais, Departamento de Microbiologia, Av.
Antônio Carlos, Belo Horizonte 6627, 31270-901, MG, Brazil
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Badotti et al. BMC Microbiology (2017) 17:42
DOI 10.1186/s12866-017-0958-x
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
respectively [6, 7]; saprotrophs, such as wood-decaying
fungi [8]; pathogens of plants [3] and animals [9, 10];
and mycorrhizal symbionts [11]. Basidiomycota species
are grouped into the following subphyla: Agaricomyco-
tina, Pucciniomycotina, and Ustilaginomycotina. The
first is the largest subphylum with approximately one-third
of all described fungal species [5, 12]. Thus, a substantial
amountofthedatathatiscurrentlyavailableondiversity,
distribution, and sequencing has targeted Agaricomycotina,
particularly in the orders of Agaricales, Polyporales, and
Boletales. This subphylum is primarily composed of wood
decayers, litter decomposers, and ectomycorrhizal fungi, as
well as pathogens and poisonous, hallucinogenic, or edible
species [13].
The identification of fungi at the species level is crit-
ical to many research areas, such as health sciences and
agriculture, where the determination of causal agents of
diseases is central to the definition of the suitable treat-
ment, elucidation of outbreaks, and transmission mecha-
nisms [14, 15]. Furthermore, the understanding of the
specific roles of microorganisms in an ecosystem, their
abundance, and their community composition in eco-
logical and biodiversity studies can only be attained
through their reliable identification [16]. However, dis-
covering and describing all extant fungal species appears
challenging. According to the Dictionary of Fungi, only
approximately 100,000 species have been described thus
far [12], and the estimated diversity ranges from 1.5 to
5.1 million [1, 17, 18].
Morphological characteristics are useful for species de-
scription; however, they may be limited because many
macroscopic structures are produced infrequently and
temporarily [19], and many taxa often harbor cryptic
species complexes [20]. Molecular tools complementing
morphological ones are very promising in identifying
species and can be used to rapidly and reliably evaluate
biological diversity. These markers have been applied to
the identification of fungal species since the 1990s [21,
22]; however, the strategy based on the sequencing of
standardized genomic fragments (DNA barcoding) was
recognized afterwards [23]. The primary difference be-
tween molecular identification tools and the “DNA
barcode”approach is that the latter involves the use of
a standard DNA region that is specific for a taxonomic
group. The use of a segment of the mitochondrial gene
encoding the cytochrome c oxidase subunit I (COI) has
been proposed for animals [24]. For plants, various loci
combinations have been proposed [25]; however, a
study conducted by the Consortium for the Barcode of
Life (CBOL) Plant Working Group agreed that the
combination of sequences of two plastid genes, matK
and rbcL, is the most promising plant barcode [26]. In
2012, the study conducted by Schoch and colleagues
compared six DNA regions as promising universal
barcodes for fungi. Mitochondrial COI and other
protein-coding nuclear gene regions were excluded as
potential markers for various reasons such as difficul-
ties in amplifying DNA and insufficient variability. The
nuclear ribosomal RNA internal transcribed spacer
(ITS) region exhibited the highest probability of correct
identification (PCI) for a wide number of fungal line-
ages analyzed and the most clearly defined barcode gap
[27]. Since then, the ITS region has been accepted as
the standard barcode marker for fungi. However, a
thorough study of ITS sequences in the International
Nucleotide Sequence Database (INSD: GenBank, EMBL
and DDBJ) revealed that this region is not equally variable
in all groups of fungi [28]. Notably, for some genera of
Ascomycota, including Alternaria [29], Aspergillus [30],
Cladosporium [31], Penicillium [30], and Fusarium [32],
identification using the ITS barcode has been difficult.
One advantage of using the ITS region as a standard
marker is that most fungal species have been identified
based on this genomic region. GenBank [33] is the
most comprehensive and widely used sequence reposi-
tory in the field. A database specific for fungal se-
quences, the UNITE (User-friendly Nordic ITS
Ectomycorrhiza Database) has been developed [34].
UNITE aims to unify the fungal taxonomic identifica-
tion and correct the annotations associated with the
taxonomic names to the greatest extent possible. The
BarcodeofLifeDataSystem-BOLD[35]represents
another bioinformatics platform; however, fungi remain
underrepresented in it. BOLD supplies tools for the
storage, quality warranty, and analysis of specimens and
sequences to validate a barcode library. To obtain a
barcode status on BOLD, sequences must fulfill some
requirements, such as voucher data, collection record,
and trace files. In the last few years, the scientific com-
munity has observed the rapid improvement of DNA
sequencing technologies and the huge volume of data
generated. Trimming and identifying this enormous
amount of data requires bioinformatics tools, such as
automated pipelines and various programs. However,
the success of the analysis greatly depends on the cor-
rect taxonomic identification of sequences. Specifically,
in the case of publicly available fungal ITS sequences,
the reliability and technical quality vary significantly
[34, 36]. Schoch and colleagues [27] estimate that only
approximately 50% of the ITS sequences that are de-
posited in public databases are annotated at the species
level. Moreover, Nilsson and colleagues [37] estimated
that more than 10% of these fully identified fungal ITS
sequences are incorrectly annotated at the species level.
On the other hand, excellent initiatives, such as UNITE
and that from NCBI that include a tool which allows
flagging a GenBank sequence with type material [38]
have emerged to minimize such a problem.
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The ITS region comprises two sections (ITS1 and
ITS2) that flank the conserved 5.8S region. The identifi-
cation of multiple species from environmental samples
(the DNA metabarcode) requires the use of high-
throughput technologies, which may have limitations in
sequencing read lengths [39]. For such approaches, only
a portion of the ITS region is usually used, the ITS1 or
the ITS2. The efficiency of these sub-regions in the iden-
tification of species in many fungal lineages has been
evaluated, and some authors claim that ITS1 is more
variable than ITS2 [28, 40–42]. Others have found op-
posite results [43] or that both the sub-regions are suit-
able as metabarcoding markers [44, 45]. In a recent
work, Guarnica and colleagues [46] demonstrated that
the ITS1 region is not more variable than the ITS2
region for Cortinarius. Furthermore, the complete ITS
region is highly effective in discriminating among species
in this highly sampled genus of Basidiomycota.
In the present study, an extensive comparative analysis
based on the probability of correct identification (PCI)
and barcode gap analyses was performed using a
trimmed dataset composed of all Basidiomycota se-
quences deposited in GenBank. We evaluated the most
widely used genomic markers for Fungi (the complete
ITS region and the ITS1 and ITS2 sub-regions) to deter-
mine which is the most suitable for the identification of
Basidiomycota species. Issues related to the need of add-
itional molecular barcode markers as well as the taxo-
nomic complexities within the subphyla are discussed.
Methods
Data acquisition and filtering
In this study, only sequences with complete nuclear
ribosomal ITS from permanent collections whose
taxonomic identifications were curated by specialists
(voucher specimens) and deposited in GenBank [33]
were used. Taxonomic information regarding the spec-
imens was enriched, when available, from the UNITE
database [34]. This step was used after downloading
sequences from GenBank and before logical and qual-
ity filters were applied. For this enrichment, we firstly
downloaded the FASTA sequence files from UNITE,
and then we generated a tabular file with the UNITE
data, keeping only the access numbers that corresponded
to our specimens. Then, we retrieved the information re-
lated to sampling area and fungal classification from
UNITE. Finally, we used the UNITE information to enrich
the GenBank information.
Quality filters removed sequences with one or more
IUB/IUPAC ambiguous characters, and logic filters en-
sured that the sequences were suitable for DNA barcode
study in accordance with Barcode of Life recommenda-
tions (http://www.barcodeoflife.org/). The first logic fil-
ter guaranteed that only sequences identified at the
species level were maintained in the database. Therefore,
species with inconclusive names ('sp.', 'aff.', 'cf.', and 'un-
cultured') were removed. FungalITSExtractor [47] was
used to guarantee that only sequences with complete
ITS regions were maintained in the database. More than
99% of fungal complete ITS sequences deposited in Gen-
Bank are shorter than 800 or longer than 400 pb; thus
all sequences outside of this interval were excluded from
the dataset. The low representativeness together with
the potential to distort the multiple sequence alignment
justified this filter. Only species with specimens collected
from at least three different localities were included to
guarantee that only distinct and geographically distant
specimens were evaluated and to avoid the possibility of
working with genetically identical specimens. The list of
all species used to perform the analyses of this study is
provided (Additional file 1). All filters were performed
using custom scripts written in the Perl programming
language and are available upon request. The FungalIT-
SExtractor software was used to identify and extract the
ITS, ITS1, and ITS2 regions.
Data Analysis
The ITS, ITS1, and ITS2 datasets were partitioned in
several sub-datasets, each containing sequences belong-
ing to only one genus. Sequences from each sub-dataset
were aligned using MUSCLE (version 3.8.31) with de-
fault parameters [48]. Distance matrices were generated
using an uncorrected p-distance because it is simple and
without any biological assumptions [49]. To evaluate the
discriminative power of the three genomic markers, the
probability of correct identification (PCI) was calculated
as the ratio of species successfully identified per total
number of species. A species was considered successfully
identified if the minimum interspecific distance was
larger than its maximum intraspecific distance [50].
Custom Perl scripts were written to calculate the dis-
tance matrices and the PCI values. Boxplots were plotted
in R language.
Two statistical analyses were performed to graphically
represent the data, a scatter plot and a dot plot. The
scatter plot aimed to evaluate the correlations between
the PCI values for the genomic regions pairwise combi-
nations (ITS versus ITS1, ITS versus ITS2, and ITS1
versus ITS2), and the Spearman correlation coefficient
was determined. The dot plot was used to compare the
PCI with the barcode gap analyses. For this purpose,
the PCI values for the four groups previously defined
from the barcode gap analyses (Groups 1 to 4) were
represented for each genomic region. All data and
graphics were generated using Minitab (Minitab Statistical
Software, version 17.3.1, State College, Pennsylvania:
Minitab Inc., 2016).
Badotti et al. BMC Microbiology (2017) 17:42 Page 3 of 12
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Results
Our primary dataset was comprised of all complete ITS
(ITS1 + 5.8S + ITS2) sequences of Basidiomycota and con-
sisted of 37,699 sequences. The exclusion of sequences
without the field ‘specimen_voucher’in GenBank file re-
duced the number to 37,342. Removing sequences with
ambiguous nucleotides led to 27,459 sequences, and re-
moving sequences with inconclusive species names resulted
in 21,238 sequences. After applying FungalITSExtractor,
19,578 sequences remained. ITS sequences with less than
400 bp and more than 800 bp were also excluded from the
dataset, as well as ITS1 and ITS2 sequences less than
100 bp, leaving 19,149 sequences. The last filter was used
to ensure that only species with at least three sequences
collected from different geographic locations were retained
in the dataset. Because most of the sequences did not in-
clude information regarding their origin, our final dataset
had this number reduced to 7,731 sequences from 112
countries from six continents. This dataset was used to
perform all subsequent analyses and represented three sub-
phyla, five classes, 25 orders, 73 families, 211 genera, and
936 species (Additional file 2). This dataset has 167 se-
quences whose DNA were originated from biological speci-
mens considered as type material. Many sequences from
type materials were not included in our dataset only be-
cause they did not pass in quality and logic filters.
Although GenBank is known to be the most complete
available public database, the amount of sequences is
biased in our trimmed dataset as follows: 93.1% (7,197
sequences) belong to species of Agaricomycotina,
whereas only 5.7% (442 sequences) come from Puccinio-
mycotina and 1.2%. (92 sequences) from Ustilaginomy-
cotina. When other taxonomic ranks were analyzed, a
similar distribution was observed with the vast majority
of species belonging to Agaricomycotina (Fig. 1). Inside
the subphyla, the imbalance in the amount of sequences
is also enormous. For example, in Agaricomycotina, we
found very well represented taxa (such as Cortinarius,
Fig. 1 Pie charts represent abundance (number) of sequences (a) and species (b) for the three subphyla represented in the dataset used in this
study. The histograms show the number of species and sequences distributed for genera belonging to Agaricomycotina (c), Pucciniomycotina (d)
and Ustilagomycotina (e) phylum
Badotti et al. BMC Microbiology (2017) 17:42 Page 4 of 12
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with 829 sequences from 124 species) and others that
were poorly represented (such as Auriscalpium, with
only one species represented by three sequences). Most
of the genera from the Agaricomycotina dataset were
underrepresented; 126 of 194 had 20 or fewer sequences,
whereas only 16 genera were represented by more than
100 sequences (Fig. 1 and Additional file 2).
The probability of correct identification (PCI) for the
three genomic regions under study was estimated using
our trimmed dataset (7,028 sequences from 113 genera).
The number of genera analyzed decreased compared
with the original dataset (211 genera) because we needed
at least two species to estimate intraspecific and inter-
specific distances. Moreover, the sequences identified as
type material are distributed in 27 distinct genera (23.9%
of total) (Additional File 3), and only 25 sequences with
RefSeq accessions interchangeably with GenBank num-
bers were identified (Additional File 4). This represented
approximately only 0.36% of the sequences that com-
prised the dataset used to estimate PCI and barcode gap
indices.
The mean PCI value for the complete ITS region was
63%, those for the sub-regions were slightly smaller as
follows: 59% for ITS1 and 58% for ITS2. For the ITS re-
gion, 53.1% of the genera had PCI values higher than the
mean, whereas for ITS1 and ITS2, these values were
46% and 48%, respectively (Table 1). The pairwise correl-
ation between the three markers (ITS versus ITS1, ITS
versus ITS2 and ITS1 versus ITS2) was estimated con-
sidering the PCI values of all genera composing the
dataset. The comparisons between complete ITS and the
sub-regions showed most of the data on or near the re-
gression line, meaning that most of the PCI values were
similar for the genera (Spearman correlation factor for
ITS versus ITS1 = 0.8825 and for ITS versus ITS2 =
0.9102). When the sub-regions were associated (ITS1
versus ITS2), the distribution of data had a different pro-
file and a lower correlation was observed (0.8158)
(Fig. 2). The pairwise correlation between the genomic
regions was carried out at the subphylum level; however,
there were no observable patterns at this taxonomic
level.
Based on the analysis of the barcode gaps, we assessed
and compared the efficiency of the three genomic
markers for the identification of Basidiomycota. Thus,
we classified the marker performance into the following
three distinct categories: good, intermediate,orpoor.
When a clear barcode gap was present (e.g., Agaricus,
Fig. 3a), we conventionally stated that the identification
was good, even if outliers were overlapping. The genomic
markers were considered intermediate if the whiskers
from an intraspecific distance overlap those from an in-
terspecific distance (e.g., Hebeloma, Fig. 3b), and poor if
the boxes overlap or the intraspecific distance values
were superior to those of interspecific distance (e.g., Lac-
tarius, Fig. 3c). For most of the genera (91.5%) evaluated,
the three genomic regions performed similarly, i.e., when
the identification is good for one region, it is also good
for the others. The same occurred when the perform-
ance was intermediate or poor. However, for some gen-
era, we found some genomic regions with superior
identification performance than others. For instance, the
complete ITS had a clearer barcode gap for the genera
Auricularia, Flammulina,Lentinellus,Microbotryum,
Parasola,andTuberculina compared with the ITS1 or
ITS2 sub-regions. ITS1 performed better than the other
regions in the identification of species from the genera
Hygrophorus and Stephanospora, as well as ITS2 for the
species belonging to the genera Amanita, Amyloporia,
Fomitopsis,Scleroderma, and Strobilurus (Table 2,
Group 2).In some instances, one of the three genetic
markers performed worse than the other(s). The ITS1
sub-region is not sufficient to differentiate the species of
the genera Collybia and Pleurotus, and the ITS2 is not a
good marker for Sebacina,Hydnellum,orVuilleminia.
Finally, it is important to note that for 11 out of the 113
genera evaluated (Botyriboletus, Clavulina, Crepidotus,
Hohenbuehelia, Hydnum, Laccaria,Lactarius, Mucidula,
Peniophorella,Phaeocollybia,andPisolithus), none of
the complete ITS, ITS1 or ITS2 sub-regions could be
used to differentiate the species based on the barcode
gap analyses (Table 2, Group 4).For a detailed classifica-
tion of genera considering their barcodes, see Table 2
and Additional File 5, where the boxplots for all ana-
lyzed genera are shown.
The results of barcode gap analyses were compared
with the PCI values for each genus using a dot plot
(Fig. 4). For the genera for which the three genomic
markers were classified as good in barcode gap analyses
(Group 1, Table 2), most of the genera exhibited PCI
above the mean value (63%); however, some disagree-
ments were found. Some genera within this group had a
PCI equal to zero (Datronia,Hygrocybe,Tecaphora, and
Telephora) or between 20 and 50% (Chroogomphus,
Coprinopsis,Lactifluus,Melampsora,Phellinus,Pilo-
derma,Puccinia,Russula,Tilletia,Xerocomus, and Xer-
omphalina) (Fig. 4a). When the group for which one or
two genomic regions showing a clearer barcode gap
(Group 2, Table 2) was compared with the PCI, most of
the genera had a PCI below the mean value (Fig. 4b).
When the group for which most of the genomic regions
showed an intermediate barcode gap (Group 3, Table 2),
only Lentinus and Hyphoderma had higher PCI than
mean value (both for the ITS2 region, Fig. 4c). When
the groups for which all three genomic regions were
classified as poor markers considering the barcode gap
(Group 4, Table 2), most of the genera also had a PCI
below the mean value (Fig. 4b) with the exception of
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Table 1 Probable Correct Identification (PCI) values (%) for all of
the Basidiomycota genera from our trimmed dataset. The PCI
values were estimated for the three genomic regions studied,
the complete ITS region (ITS1 + 5.8S + ITS2) and the sub-regions
ITS1 and ITS2
Genera ITS (ITS1 + 5.8S+ ITS2) ITS1 ITS2
Agaricus 100 100 100
Alnicola 44 33 33
Amanita 24 27 27
Amyloporia 25 25 25
Antherospora 100 100 100
Antrodia 70 70 70
Antrodiella 75 50 75
Armillaria 20 20 20
Auricularia 80 40 60
Boletus 32 26 47
Butyriboletus 67 33 67
Calvatia 000
Ceriporiopsis 100 100 100
Chlorophyllum 100 100 100
Chroogomphus 50 50 50
Clavaria 100 100 100
Clavulina 33 33 33
Collybia 50 50 50
Coprinopsis 50 50 50
Cortinarius 36 45 37
Crepidotus 50 50 50
Cystoderma 50 50 33
Cystodermella 100 100 100
Datronia 0500
Endoraecium 100 100 100
Entoloma 100 86 93
Entyloma 100 100 100
Exobasidium 100 100 100
Favolus 100 100 100
Fibroporia 100 100 67
Flammulina 100 50 75
Fomitopsis 50 25 50
Fuscoporia 100 100 100
Ganoderma 43 43 43
Geastrum 100 67 67
Gloeophyllum 100 100 100
Gymnopilus 100 100 50
Gymnopus 67 67 60
Hebeloma 42 37 32
Helicobasidium 100 75 100
Hohenbuehelia 000
Table 1 Probable Correct Identification (PCI) values (%) for all of
the Basidiomycota genera from our trimmed dataset. The PCI
values were estimated for the three genomic regions studied,
the complete ITS region (ITS1 + 5.8S + ITS2) and the sub-regions
ITS1 and ITS2 (Continued)
Hydnellum 56 56 56
Hydnum 50 50 50
Hygrocybe 000
Hygrophorus 67 67 33
Hymenopellis 100 100 100
Hyphoderma 60 60 80
Hyphodermella 100 100 100
Hypholoma 000
Inocybe 30 19 28
Laccaria 0013
Lactarius 37 37 41
Lactifluus 50 50 50
Lentinellus 43 43 29
Lentinus 57 57 71
Lepiota 92 83 75
Lepista 100 100 100
Leucoagaricus 90 100 80
Leucopaxillus 100 100 100
Lycoperdon 100 100 100
Lyomyces 100 100 100
Lyophyllum 100 100 67
Macrolepiota 50 50 33
Megacollybia 83 83 33
Melampsora 40 40 20
Melanoleuca 38 38 25
Microbotryum 70 80 50
Mucidula 000
Mycena 22 22 33
Neofavolus 100 100 100
Octaviania 100 100 100
Oligoporus 100 100 100
Parasola 100 67 67
Paxillus 0033
Peniophorella 50 50 50
Phaeocollybia 000
Phanerochaete 67 67 67
Phellinus 100 33 67
Phellodon 86 86 71
Piloderma 50 50 50
Pisolithus 000
Pleurotus 29 43 14
Pluteus 27 27 23
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Butyriboletus (with a PCI value above the mean for ITS
and ITS2, Fig. 4d).
Discussion
The accepted DNA barcode for Fungi is the rDNA ITS
region [27]. ITS is recognized as a fungal barcode be-
cause it is the most sequenced region of fungi and is
routinely used for systematics, phylogenetics, and identi-
fication [51, 52]. In this study, we downloaded all
complete ITS sequences of species belonging to the
phylum Basidiomycota from GenBank. Although this is
the most complete repository of available ITS sequences,
misidentifications or low-quality sequencing have been
encountered in this public database [37]. However, some
authors think that it is unrealistic that future databases
or even a barcode database could be more reliable than
GenBank because misidentified sequences would be as
common as they are currently and because vouchers will
not be re-identified by taxonomic experts (for a wide
discussion, see [53–55]). To overcome this drawback, lo-
gical and quality filters were applied to our original data-
set to obtain the most reliable results possible. The
restrictiveness in the filtering step aimed to create a
high-quality dataset (accurate taxonomic annotation and
presence of relevant metadata) that would meet the theor-
etical assumptions of the biological system of identifica-
tion via DNA barcode and the principles recommended in
BOLD Systems [24, 35].
More than 90% of our trimmed dataset belonged to
the subphylum Agaricomycotina. This result is not sur-
prising because it reflects the high diversity of this taxon
compared with the other subphyla, which is widely men-
tioned in the literature [5, 12]. Kirk and colleagues [12]
estimated that one-fifth of all known fungal species de-
scribed belong to the Agaricomycete clade; this diversity
is considered to be underestimated because new taxa are
continually being described [1, 56]. This discrepancy in
the amount of species and sequences from the subphyla
may reflect a natural event or may occur due to the spe-
cific interests of the scientific community in Agaricomy-
cotina species.
Some criteria have been traditionally used to test the
DNA barcoding efficacy to classify and/or identify speci-
mens at the species level, such as similarity measures,
tree-based techniques, and identification based on direct
sequence comparison [57, 58]. However, all of these ap-
proaches present several issues (see [55] for a detailed
discussion). Similarity measures are generally used to
cluster sequences in “molecular operational taxonomic
units”; however, the choice of the threshold value for
distinguishing intraspecific and interspecific distances is
largely arbitrary [58, 59]. An important and acceptable
measure of the efficacy of a genetic marker should re-
flect the probability of correctly identifying a species.
This concept has emerged as the probability of correct
identification (PCI) [50, 53, 55, 60]. However, there is no
consensus for the definition and calculation of PCI,
which currently embraces a broad class of measures. In
this work, we assume the concept described by Hollings-
worth and colleagues [50] in which the authors consid-
ered the “discrimination as successful if the minimum
uncorrected interspecific p-distance involving a species
was larger than its maximum intraspecific distance”to
measure the PCI for each genus included in our dataset.
Furthermore, the use of genetic distances enables the
observation of the ‘barcoding gap’, which is possible by
plotting the intraspecific and interspecific distances.
Table 1 Probable Correct Identification (PCI) values (%) for all of
the Basidiomycota genera from our trimmed dataset. The PCI
values were estimated for the three genomic regions studied,
the complete ITS region (ITS1 + 5.8S + ITS2) and the sub-regions
ITS1 and ITS2 (Continued)
Polyporus 100 100 86
Porodaedalea 100 100 100
Postia 50 50 50
Psathyrella 100 100 100
Psilocybe 100 100 100
Puccinia 43 43 43
Pycnoporellus 100 100 100
Ramaria 33 33 33
Resinicium 100 100 100
Rhizopogon 27 27 27
Rhodocollybia 100 100 100
Rigidoporus 100 100 50
Russula 38 27 36
Sarcodon 86 86 86
Scleroderma 33 33 33
Sebacina 33 0 33
Stephanospora 80 80 20
Strobilurus 67 67 67
Suillus 83 83 83
Thecaphora 100 100 100
Thelephora 000
Tilletia 100 33 100
Tomentella 25 50 25
Trametes 42 42 42
Tricholoma 43 57 29
Tricholomopsis 100 100 100
Tuberculina 67 0 67
Vuilleminia 33 33 33
Xerocomus 100 50 100
Xeromphalina 100 100 50
Badotti et al. BMC Microbiology (2017) 17:42 Page 7 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Therefore, an ideal barcode marker would reveal intra-
specific divergences lower than interspecific divergences
[61].
In this study, we aimed to identify the most suitable
genomic marker (complete ITS, ITS1 or ITS2) to iden-
tify fungal species belonging to Basidiomycota. Our find-
ings, based on PCI and barcode gap analyses, indicated
that for most of the genera, the three genomic regions
perform similarly, i.e., when one genomic region was
considered a good marker (a PCI above the mean value
or the presence of a clear barcode gap) the other regions
were also; the same was observed when the performance
of genomic markers was considered insufficient. When
the performance of the genomic markers was individu-
ally evaluated, barcode gap analyses provided a more op-
timistic view than PCI values. Approximately half of the
genera exhibited PCI values lower than the mean (63%);
however, the three genomic regions were classified as
good for most of the genera (Table 2) when the barcode
gap is taken into account. Accordingly, the comparison
between barcode gap and PCI for each genus showed
some disagreements. This was primarily observed for
Fig. 2 Pairwise correlations (a, ITS X ITS1, b, ITS X ITS2 and c, ITS1 X ITS2) between PCI values of all genera from our dataset
Fig. 3 Examples of the barcode gap performance classifications used in this study. a. Clear barcode gap (identification performance classified as
good) for the genera Agaricus,b.Intermediate separation between the intra- and interspecific distances for Hebeloma and c.Apoor barcode gap
for Lactarius
Badotti et al. BMC Microbiology (2017) 17:42 Page 8 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
some of the genera that showed good identification per-
formance using the barcode gap but had low PCI values
(Fig. 4a). The opposite, i.e., high PCI values and poor
identification performance via barcode gap, was ob-
served for only one genus, Botyriboletus (Fig. 4d).
Initially, the low PCI values found for some genera
(such as Calvatia,Datronia,Hygrocybe, Hohenbuehelia,
Hypholoma,Mucidula, and Pisolithus) could be ex-
plained by dataset features, such as the low number of
species (genera represented by sequences from only two
species) and/or by the high number of outliers, which
would have distorted the PCI estimates. Additionally,
the taxonomy appears very complex for many of the
genera for which the identification performance using
ITS and sub-regions were insufficient. Taxonomy issues
for two genera (Hygrocybe and Thelephora) for which
PCI values were low and three genera (Hypholoma,
Phaeocollybia, and Pisolithus) for which both PCI and
Fig. 4 PCI values for the genera classified in the Group 1 (a), Group 2 (b), Group 3 (c) and Group 4 (d) are represented for the ITS, ITS1 and ITS2
genomic region
Table 2 Grouping of Basidiomycota genera based on the barcode gap analyses (See Additional file 5 for more details)
Group 1 Group 2 Group 3 Group 4
Genera for which all the three
genetic regions are good markers
Genera for which one or two genetic
regions showed a clearer barcode
gap and are recommended over
the other (s)
Genera for which most of the
genetic regions showed intermediate
barcode gap and their use should
be carefully evaluated
Genera for which all three
genetic regions are poor
markers
Agaricus, Antherospora, Antrodia,
Ceriporiopsis, Chroogomphus, Clavaria,
Coprinopsis, Cystodermella, Datronia,
Edoraecium, Entoloma, Entyloma,
Exobasidium, Favolus, Fibroporia,
Fuscoporia, Geastrum, Gloeophyllum,
Helicobasidium, Hygrocybe,
Hymenopellis, Hyphodermella, Lactifluus,
Lepiota, Lepista, Leucopaxillus,
Lycoperdon, Lyomyces, Lyophyllum,
Melampsora, Neofavolus, Octaviania,
Oligoporus, Phanerochaete, Phellinus,
Phellodon, Piloderma, Polyporus,
Porodaedalea, Psathyrella, Psilocybe,
Puccinia, Pycnoporellus, Resinicium,
Rhodocollybia, Russula, Sarcodon,
Suillus, Telephora, Thecaphora, Tilletia,
Tricholomopsis, Xerocomus,
Xeromphalina
Amanita (ITS2), Amyloporia (ITS2),
Antrodiella (ITS and ITS2), Auricularia
(ITS), Calvatia (ITS and ITS1),
Chlorophyllum (ITS and ITS1),
Flammulina (ITS), Fomitopsis (ITS2),
Ganoderma (ITS and ITS1), Gymnopilus
(ITS and ITS1), Gymnopus (ITS and
ITS1), Hygrophorus (ITS1), Lentinellus
(ITS), Leucoagaricus (ITS and ITS1),
Macrolepiota (ITS and ITS1),
Megacollybia (ITS and ITS1),
Microbotryum (ITS), Parasola (ITS),
Postia (ITS and ITS2), Rigidoporus (ITS
and ITS1), Scleroderma (ITS2),
Stephanospora (ITS1), Strobilurus (ITS2),
Tuberculina (ITS)
Alnicola, Armillaria, Boletus, Collybia,
Cortinarius, Cystoderma, Hebeloma,
Hydnellum, Hyphoderma, Hypholoma,
Inocybe, Lentinus, Melanoleuca,
Mycena, Paxillus, Pleurotus, Pluteus,
Ramaria, Rhizopogon, Sebacina,
Tomentella, Tricholoma, Trametes,
Vulleminia
Butyriboletus, Clavulina,
Crepidotus, Hohenbuehelia,
Hydnum, Laccaria, Lactarius,
Mucidula, Peniophorella,
Phaeocollybia, Pisolithus
Badotti et al. BMC Microbiology (2017) 17:42 Page 9 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
barcode gap analyses proved that ITS, ITS1 and ITS2
are not sufficient markers for the identification of spe-
cies are discussed below based on pertinent literature.
Hygrocybe species exhibit extremely high variability in
the ITS region, with sequences diverging by more than
25%. Thus, the use of additional DNA barcode markers
has been proposed to re-evaluate the taxonomy of this
genus [62, 63]. Moreover, significant changes in the clas-
sification of Hygrocybe, such as its division, are expected
[64].
The phylogenetic relationships between and within
species of Thelephora are also doubtful with ITS. The
existence of cryptic species was described, and the im-
portance of integrating morphological and molecular
data, as well as employing a meaningful number of sam-
ples for the accurate identification is highlighted [65].
Hypholoma has been poorly studied. However, a recent
study based on the morphological and molecular aspects
of H. cinnabarinum samples showed that this species is
not a member of the genus Hypholoma but belongs in-
stead to Agaricus [66]. The ecological role of Phaeocolly-
bia is uncertain. Smith [67] argues that the genus
harbors both saprobes and mycorrhiza formers. Singer
[68] considered that members of the genus were not
obligatorily ectomycorrhizal, whereas Norvell [69] pre-
sented evidence for the consideration of Phaeocollybia
as a mycorrhizal genus. At the taxonomic level, the
complexity remains, as may be exemplified in Norvell
[70]. The author proposed the re-evaluation of the genus
Phaeocollybia by revealing four new agaric species mor-
phologically similar to Phaeocollybia kauffmanii.The
wide genetic divergence among Pisolithus ITS sequences
[71–73] indicates significant evolutionary divergence and
suggests that this genus encompasses a species complex.
This hypothesis was reinforced by Kope and Fortin [74]
who separated three groups of Pisolithus using incom-
patibility tests and basidiospore spine morphology.
According to Bickford and colleagues [20], cryptic
species are two or more distinct species that are erro-
neously classified under one species name. Large intra-
specific genetic distances associated with morphological
and geographical discrete differences have revealed a
broad range of cryptic species for many organisms and
habitats [75, 76]. Although our knowledge of fungal
species remains limited, the presence of cryptic species
inside the group is well recognized [20] and was subse-
quently described for many of the genera covered in
this study.
The use of molecular techniques, primarily DNA se-
quences, generates information to re-evaluate classifica-
tions and provides more accurate species delimitations
[77]. Currently, the utility of DNA barcoding is evident.
However, a universal barcode for the clear identification
of all fungal species does not appear feasible, and
secondary barcodes for Fungi have already been pro-
posed [78]. In addition to the known limitations of ITS
barcodes for some genera of Ascomycota, our results
indicated that for some genera of Basidiomycota, such
as Hygrocybe and Pisolithus, additional barcode markers
may contribute to a clear elucidation of the complex re-
lationships between and within species. The failure to
correctly identify biological species hampers the efforts
of the scientific community to conserve, study, or
utilize them. Future research in this field should in-
clude discovering characteristics that natural selection
acts upon [20].
Conclusions
Progress in many research areas fundamentally depends
on the rapid and reliable identification of biological spe-
cies. Most fungal diversity is unknown, and issues re-
lated to the conservation of these organisms are urgent;
thus, studies related to species identification are crucial.
Knowledge regarding the efficiency and limitations of
the barcode markers that are currently used for specific
groups of organisms optimize the work of many studies.
Therefore, the present study contributes to the rational
selection of barcode markers of species belonging to the
phylum Basidiomycota.
Additional files
Additional file 1: List of species used in this study, their accession and
taxon ID in GenBank and taxonomic affiliations. (XLSX 345 kb)
Additional file 2: Number of species and sequences (specimens)
recovered to each genus and their taxonomic affiliations. Data were
compiled from our trimmed dataset. (DOCX 36 kb)
Additional file 3: List of genera with sequences originated from type
specimens and their PCI values (ITS, ITS1, ITS 2) and groups according to
the barcode gap analysis. (DOCX 61 kb)
Additional file 4: List of sequences with RefSeq accessions
interchangeably with GenBank numbers. (DOCX 110 kb)
Additional file 5: Barcode gap of all the 113 genera studied for ITS, ITS1
and ITS2 genomic regions by plotting intra- and interspecific distances.
(DOCX 9035 kb)
Abbreviations
BOLD: The barcode of life data system; CBOL: Consortium for the barcode of
life; INSD: International nucleotide sequence database; ITS: Nuclear ribosomal
RNA internal transcribed spacer region; MUSCLE: Multiple sequence
comparison by log-expectation; PCI: Probability of correct identification;
UNITE: User-friendly nordic its ectomycorrhiza database
Acknowledgements
We thank all who contributed directly or indirectly to this work, especially
the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico),
FIOCRUZ-MG (Fundação Oswaldo Cruz, Minas Gerais), CEFET-MG (Centro
Federal de Educação Tecnológica de Minas Gerais), Vale Institute of Technology,
and the Graduate Programs of Microbiology and Bioinformatics of the
Universidade Federal de Minas Gerais (UFMG).
Funding
This work was supported by grants from Conselho Nacional de Desenvolvimento
Científico e Tecnológico (CNPq, 308148/2013-4 and 564944/2010-6).
Badotti et al. BMC Microbiology (2017) 17:42 Page 10 of 12
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Availability of data and material
The data set supporting the results of this article is presented in the main
paper or as additional files. Moreover, the reader can contact the
corresponding author to get the information needed.
Authors’contributions
FB analyzed the data and drafted the manuscript. FSO wrote the scripts,
downloaded and filtered the dataset. CFG and ABMV worked on statistical
analyses. PLCF and LN assisted with the data analyses. GO and AGN
designed the analyses, analyzed and discussed the data. All authors read and
approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Author details
1
Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG),
Departamento de Química, 30.421-169 Belo Horizonte, MG, Brazil.
2
Fundação
Oswaldo Cruz (FIOCRUZ), Centro de Pesquisas René Rachou –CPqRR,
30190-002 Belo Horizonte, MG, Brazil.
3
Universidade Federal de Minas Gerais,
Departamento de Microbiologia, Av. Antônio Carlos, Belo Horizonte 6627,
31270-901, MG, Brazil.
4
Faculdade de Minas (FAMINAS), 66055-090 Belo
Horizonte, MG, Brazil.
5
Faculdade Promove de Tecnologia, 30140-061 Belo
Horizonte, MG, Brazil.
6
Instituto Tecnológico Vale, 66055-090 Belém, PA, Brazil.
Received: 30 November 2016 Accepted: 14 February 2017
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