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Mycologia
ISSN: 0027-5514 (Print) 1557-2536 (Online) Journal homepage: http://www.tandfonline.com/loi/umyc20
Sequence-based classification and identification of
Fungi
David Hibbett, Kessy Abarenkov, Urmas Kõljalg, Maarja Öpik, Benli Chai,
James Cole, Qiong Wang, Pedro Crous, Vincent Robert, Thorunn Helgason,
Joshua R. Herr, Paul Kirk, Shiloh Lueschow, Kerry O’Donnell, R. Henrik
Nilsson, Ryoko Oono, Conrad Schoch, Christopher Smyth, Donald M. Walker,
Andrea Porras-Alfaro, John W. Taylor & David M. Geiser
To cite this article: David Hibbett, Kessy Abarenkov, Urmas Kõljalg, Maarja Öpik, Benli Chai,
James Cole, Qiong Wang, Pedro Crous, Vincent Robert, Thorunn Helgason, Joshua R. Herr,
Paul Kirk, Shiloh Lueschow, Kerry O’Donnell, R. Henrik Nilsson, Ryoko Oono, Conrad Schoch,
Christopher Smyth, Donald M. Walker, Andrea Porras-Alfaro, John W. Taylor & David M. Geiser
(2016) Sequence-based classification and identification of Fungi, Mycologia, 108:6, 1049-1068
To link to this article: https://doi.org/10.3852/16-130
© 2016 by The Mycological Society of
America
Published online: 30 Jan 2017.
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Sequence-based classification and identification of Fungi
David Hibbett
1
Biology Department, Clark University, Worcester,
Massachusetts 01610
Kessy Abarenkov
Urmas Kõljalg
Maarja Öpik
Institute of Ecology and Earth Sciences,
University of Tartu, 40 Lai St, Tartu 51005, Estonia
Benli Chai
James Cole
Qiong Wang
Department of Plant, Soil, and Microbial Sciences,
Michigan State University, Plant and Soil Sciences
Building, 1066 Bogue St. Room 540, East Lansing,
Michigan 48824
Pedro Crous
Vincent Robert
Centraalbureau voor Schimmelcultures Fungal Biodiversity
Centre (CBS-KNAW), 3508 AD, Utrecht, the Netherlands
Thorunn Helgason
Department of Biology, University of York,
York YO10 5DD, United Kingdom
Joshua R. Herr
Department of Plant Pathology and Center for
Plant Science Innovation, University of Nebraska,
Lincoln, Nebraska 68503
Paul Kirk
Biodiversity Informatics and Spatial Analysis, Royal Botanic
Gardens, Kew, Surrey TW9 3AF, United Kingdom
Shiloh Lueschow
Kerry O’Donnell
NCAUR ARS USDA, 1815 N. University St., Peoria,
Illinois 61604
R. Henrik Nilsson
University of Gothenburg, Department of Biological and
Environmental Sciences, Box 461, 405 30 Göteborg,
Sweden
Ryoko Oono
Department of Ecology, Evolution, and Marine Biology,
University of California Santa Barbara, Santa Barbara,
California 93106
Conrad Schoch
National Center for Biotechnology Information, National
Library of Medicine, National Institutes of Health, Bethesda,
Maryland 20892
Christopher Smyth
Department of Plant Pathology and Environmental
Microbiology, 121 Buckhout Laboratory, Penn State
University, University Park, Pennsylvania 16802
Donald M. Walker
Department of Biology, Tennessee Technological University,
1100 N. Dixie Ave., Cookeville, Tennessee 38505
Andrea Porras-Alfaro
Department of Biological Sciences, Western Illinois University,
Waggoner Hall 372, 1 University Circle Macomb,
Illinois 61455
John W. Taylor
University of California, Department of Plant and Microbial
Biology, 111 Koshland Hall, Berkeley, California 94720
David M. Geiser
Department of Plant Pathology and Environmental
Microbiology, 121 Buckhout Laboratory, Penn State
University, University Park, Pennsylvania 16802
Abstract: Fungal taxonomy and ecology have been
revolutionized by the application of molecular meth-
ods and both have increasing connections to genomics
and functional biology. However, data streams from
traditional specimen- and culture-based systematics
are not yet fully integrated with those from metage-
nomic and metatranscriptomic studies, which limits
understanding of the taxonomic diversity an d meta‐
bolic properties of fungal communities. This article
reviews current resources, needs, and opportunities
for sequence-based classification and identification
(SBCI) in fungi as well as related efforts in prokaryotes.
To realize the full potential of fungal SBCI it will be
necessary to make advances in multiple areas. Improve-
ments in sequencing methods, including long-read and
single-cell technologies, will empower fungal molecu-
lar ecologists to look beyond ITS and current shotgun
metagenomics approaches. Data quality and accessibil-
ity will be enhanced by attention to data and metadata
standards and rigorous enforcement of policies for
deposition of data and workflows. Taxonomic commu-
nities will need to develop best practices for molecular
characterization in their focal clades, while also con-
tributing to globally useful datasets including ITS.
Changes to nomenclatural rules are needed to enable
valid publication of sequence-based taxon descriptions.
Finally, cultural shifts are necessary to promote adop-
tion of SBCI and to accord professional credit to indivi-
duals who contribute to community resources.
Key words: biodiversity informatics, metagenomics,
molecular ecology, nomenclature, systematics, taxonomy
Submitted 25 Jun 2016; accepted for publication 29 Sep 2016.
1
Corresponding author. E-mail: dhibbett@clarku.edu
Mycologia, 108(6), 2016, pp. 1049–1068. DOI: 10.3852/16-130
#
2016 by The Mycological Society of America, Lawrence, KS 66044-8897
Issued 25 January 2017
1049
Published online 30 Jan 2017
INTRODUCTION
Fungi make up an underdescribed, poorly documen-
ted clade of eukaryotes that have immense ecological
and economic impacts. Many fungi are microscopic
or have cryptic life cycles that make detection difficult.
Approximately 135 000 species of fungi have been
described, but the actual diversity of the group is likely
to be in the millions of species (Blackwell 2011, Taylor
et al. 2014). Investigations into fungal diversity have
traditionally been based on fruiting bodies or cultures,
but an increasing number of studies that obtain DNA
and RNA sequences directly from “environmental”
sources—such as soil, water, air, or tissues of other
organisms—are revealing potentially new fungal spe-
cies at dramatically accelerated ra tes (Hibbett et al.
2011, Lindahl et al. 2013). For example, the Cryptomy-
cota (Rozellomycota, rozellida) (Lara et al. 2010, Jones
et al. 2011, Corsaro et al. 2014) and Archaeorhizomy-
cetes are major clades of Fungi that are known almost
entirely from environmental DNA sequences (Rosling
et al. 2011, James and Berbee 2012). Many environmen-
tal sequences can only be identified to the level of a
phylum or simply “fungi” (Nilsson et al. 2016), even in
sophisticated analyses that use rigorous phylogenetic
methods and that consider ribosomal RNA (rRNA) sec-
ondary structure (Glass et al. 2013). Thus, it is likely
that other ancient clades are waiting to be described.
Recent global or continental-scale analyses of patterns
in fungal biodiversity have been based entirely on
environmental DNA data (Amend et al. 2010, Talbot
et al. 2014, Tedersoo et al. 2014, Davison et al.
2015).
Analyses of environmental DNA and RNA sequences
may involve two complementary but distinct activities:
Sequence-based classification (SBC) and sequence-based
identification (SBI) (Herr et al. 2015). The goals of SBC
are to discover, name, and classify fungal species accord-
ing to their phylogenetic relationships. In contrast, SBI
uses the products of taxonomy to identify individuals
and determine the composition of fungal communities
with reference to existing classifications. SBI is a central
element of ecological studies, including metatranscrip-
tomic studies of community-level metabolic processes.
Collectively, sequence-based classification and identifica-
tion (SBCI) denotes the full range of activities required
to detect and characterize fungi in the environment
based on nucleic acid sequences (T
ABLE I).
New r esources for SBCI are required to fully
exploit the staggering volume of data flowing from fun-
gal molecular ecology studies using high-throughput
sequencing technologies. Huge numbers of undescribed
taxa known only from environmental sequences need to
be classified and linked to phenotypic, ecological, and
functional traits. This article aims to: (i) envision the
potential of SBCI and identify its conceptual challenges;
(ii) survey current resources for SBCI in fungi and
assess their strengths, limitations, and opportunities for
enhancement; and (iii) consider options for integrating
sequence-based fungal species into taxonomic systems
based on specimens and cultures.
G
OALS AND CONCEPTUAL CHALLENGES OF SBCI
In the ideal model of SBCI it woul d be possible to
submit sequences of any nuclei c acids from specimens
TABLE I. Terms and concepts for sequence-based classification and identification
Candidatus A provisional taxonomic category for prokaryotes that lack a type culture
Environmental nucleic acid species (ENAS) A species recognized solely with environmental molecular sequences
Environmental sequence A DNA or RNA sequence obtained directly from a microbial community
using amplicon or shotgun methods
Molecular operational taxonomic unit (MOTU) An unranked taxonomic entity recognized with environmental sequences
Nomenclature The set of rules detailed in the ICNAFP that determine the correct name
for algae, fungi, and plants
Sequence-based classification (SBC) The process by which species are discovered, named, and classified according
to their phylogenetic relationships.
Sequence-based identification (SBI) The process by which the products of taxonomy are used to identify individuals
and determine the composition of communities with reference to existing
classifications
Species hypotheses (SH) A term coined to describe taxa whose ITS rRNA gene sequences cluster at
user-defined cutoff levels i.e. 97–99%
Taxonomy The branch of science focused on naming, describing, and classifying all
forms of life
Virtual taxa (VT) Phylogenetically defined sequence groups that roughly correspond to species
1050 MYCOLOGIA
or environmental samples to appropriate databases
and retrieve lists of taxa with information on their rela-
tive abundance, phylogenetic relationships, ecological
roles, and metabolic properties. The reference data-
bases themselves would become richer as the results
from each new study were integrated, creating new
knowledge about fungal diversity, biogeogra phy, popul a-
tion structure, and functional biology (F
IG.1).However,
current methods of SBCI are based almost entirely on
analyses of PCR-amplified nuclear rRNA genes, partic-
ularly the internal transcribed spacer (ITS) region,
and they draw on incomplete taxonomic and function-
al databases. New environmental data are not systemat-
ically integrated with existing resources. Here, we list
six general challenges to achieving the model of SBCI
described above; subsequent sections describe these
challenges and the actions required to overcome
them: (i) develop community standards for taxon rec-
ognition based on sequence data; (ii) create and curate
sequence databases and analytical tools for SBCI;
(iii) link sequence data to phenotypic data, including
data from type specimens; (iv) achieve reproducibility
in studies utilizing SBCI; (v) encourage the scientific
and lay communities to adopt SBCI; and (vi) accord
professional credit for contributing resources for SBCI.
S
URVEYING THE LANDSCAPE: CURRENT RESOURCES
AND NEEDS FOR
SBCI IN FUNGI
Some of the earliest applications of comparative
molecular data in fungi were to identi fy environmental
samples that lacked the morphological characters nec-
essary for tra ditional taxonomic identification (Gardes
et al. 1991). Since then, many new web-accessible tools
have been designed specifically for SBCI. URLs for
resources described here are listed (T
ABLE II). All of
these tools attempt to deal with the problem of mis‐
identified or otherwise misleading sequences (Bridge
et al. 2003, Bidartondo 2008) and insufficiently identi-
fied sequences (Ryberg et al. 2009). These are the
so-called “dark taxa” (Parr et al. 2012, p. 2013) that
reside in the International Nucleotide Sequence Data-
base Collaboration (INSDC), with its three partners:
GenBank at the National Center for Biotechnology
Information (NCBI), the European Nucleotide Se‐
quence Archive of the European Molecular Biology
FIG. 1. Conceptual diagram of Sequence-Based Classification and Identification (SBCI) in fungi. The upper part of
the figure shows traditional mycological data streams originating with cultures and collections (green boxes), whereas the lower
part indicates environmental molecular sampling (blue boxes). SBCI is included in the integrating middle
layer (red arrows), with outputs including synthetic understanding of community composition and functional biology and
contributions to taxonomic and functional databases. Metadata not indicated in the diagram include temporal
and geographic infor mation or host information, which could contribute to fields such as ecological niche modeling,
biogeography, and epidemiology. Metaproteomics and non-molecular aspects of functional biology (e.g. morphology and
development) are also not shown, but could be integrated into SBCI and used to predict phenotypic properties of species
detected with environmental sequences.
HIBBETT ET AL.: SEQUENCE-BASED CLASSIFICATION AND IDENTIFICATION 1051
TABLE II. Databases and tools for sequence-based classification and identification
General identification tools and data repositories
BOLD http://www.boldsystems.org/
CBS-KNAW http://www.cbs.knaw.nl/Collections/BioloMICSSequences.
aspx—contains 30 BLASTn searchable databases
Dryad http://datadryad.org/
FUSARIUM-ID http://isolate.fusariumdb.org/
GreenGenes http://greengenes.lbl.gov/cgi-bin/nph-index.cgi
MaarjAM http://maarjam.botany.ut.ee/
Mothur http://www.mothur.org/
Naïve Bayesian Classifier http://aem.asm.org/content/73/16/5261.
short?rss=1&ssource=mfc
Open Tree of Life http://www.opentreeoflife.org/
QIIME http://qiime.org/
RefSeq Targeted Loci http://www.ncbi.nlm.nih.gov/refseq/targetedloci/
Ribosomal Database Project (RDP) https://rdp.cme.msu.edu/
Silva http://www.arb-silva.de/
TreeBASE https://treebase.org/
TrichoBLAST http://www.isth.info/tools/blast/
UNITE https://unite.ut.ee/
Data standards
BIOM http://biom-format.org/
MIMARKS http://www.nature.com/nbt/journal/v29/n5/
full/nbt.1823.html
Darwin Core http://rs.tdwg.org/dwc/
Genomics databases and tools
1000 Fungal Genomes Project (1KFG) http://1000.fungalgenomes.org/home/
FungiDB http://fungidb.org/fungidb/
GEBA http://jgi.doe.gov/our-science/science-programs/
microbial-genomics/phylogenetic-diversity/
MycoCosm http://genome.jgi.doe.gov/programs/fungi/index.jsf
Functional database
FUNGuild https://github.com/UMNFuN/FUNGuild
Nomenclature and nomenclatural databases and organizations
Catalogue of Life (COL) http://www.catalogueoflife.org/
Index Fungorum http://www.indexfungorum.org/
International code of nomenclature for algae,
fungi, and plants (ICNAFP)
http://www.iapt-taxon.org/nomen/main.php
International Commission on the Taxonomy of Fungi (ICTF) http://www.fungaltaxonomy.org/
List of prokaryotic names with standing in nomenclature (LPSN) http://www.bacterio.net/
MycoBank http://www.mycobank.org/
Biodiversity collections databases
Global Biodiversity Information Facility (GBIF) http://www.gbif.org/
iDigBio https://www.idigbio.org/
MycoPortal http://mycoportal.org/portal/index.php
World Federation of Culture Collections (WFCC) http://www.wfcc.info/
Citizen science resources
Encyclopedia of Life http://eol.org/
Mushroom Observer http://mushroomobserver.org/
1052 MYCOLOGIA