GlobalFungi: Global database of fungal records from high-throughput-sequencing metabarcoding studies

Větrovský, Tomáš; Morais, Daniel; Kohout, Petr; Lepinay, Clémentine; Algora, Camelia; Hollá, Sandra Awokunle; Bahnmann, Barbara Doreen; Bílohnědá, Květa; Brabcová, Vendula; D’Alò, Federica; Human, Zander Rainier; Jomura, Mayuko; Kolařík, Miroslav; Kvasničková, Jana; Lladó, Salvador; López-Mondéjar, Rubén; Martinović, Tijana; Mašínová, Tereza; Mészárošová, Lenka; Michalčíková, Lenka; Michalová, Tereza; Mundra, Sunil; Navrátilová, Diana; Odriozola, Iñaki; Piché-Choquette, Sarah; Štursová, Martina; Švec, Karel; Tláskal, Vojtěch; Urbanová, Michaela; Vlk, Lukáš; Voříšková, Jana; Žifčáková, Lucia; Baldrián, Petr

doi:10.1101/2020.04.24.060384

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…Different BLAST algorithms (megablast, discontinuous megablast and blastn) can yield different matches, depending on the length of the query and/or reference sequences, what score is observed, and whether sequences of the underlying marker, such as the ITS, were deposited in their entirety or separately, e.g. ITS1 versus ITS2 (Altschul et al 1990;Camacho et al 2009;Nilsson et al 2008;Blaalid et al 2013;Tedersoo et al 2015;Madden et al 2019;Větrovský et al 2020). This underlines the importance of the verification process.…”

Section: Verificationmentioning

confidence: 99%

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Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

et al. 2020

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True fungi (Fungi) and fungus-like organisms (e.g. Mycetozoa, Oomycota) constitute the second largest group of organisms based on global richness estimates, with around 3 million predicted species. Compared to plants and animals, fungi have simple body plans with often morphologically and ecologically obscure structures. This poses challenges for accurate and precise identifications. Here we provide a conceptual framework for the identification of fungi, encouraging the approach of integrative (polyphasic) taxonomy for species delimitation, i.e. the combination of genealogy (phylogeny), phenotype (including autecology), and reproductive biology (when feasible). This allows objective evaluation of diagnostic characters, either phenotypic or molecular or both. Verification of identifications is crucial but often neglected. Because of clade-specific evolutionary histories, there is currently no single tool for the identification of fungi, although DNA barcoding using the internal transcribed spacer (ITS) remains a first diagnosis, particularly in metabarcoding studies. Secondary DNA barcodes are increasingly implemented for groups where ITS does not provide sufficient precision. Issues of pairwise sequence similarity-based identifications and OTU clustering are discussed, and multiple sequence alignment-based phylogenetic approaches with subsequent verification are recommended as more accurate alternatives. In metabarcoding approaches, the trade-off between speed and accuracy and precision of molecular identifications must be carefully considered. Intragenomic variation of the ITS and other barcoding markers should be properly documented, as phylotype diversity is not necessarily a proxy of species richness. Important strategies to improve molecular identification of fungi are: (1) broadly document intraspecific and intragenomic variation of barcoding markers; (2) substantially expand sequence repositories, focusing on undersampled clades and missing taxa; (3) improve curation of sequence labels in primary repositories and substantially increase the number of sequences based on verified material; (4) link sequence data to digital information of voucher specimens including imagery. In parallel, technological improvements to genome sequencing offer promising alternatives to DNA barcoding in the future. Despite the prevalence of DNA-based fungal taxonomy, phenotype-based approaches remain an important strategy to catalog the global diversity of fungi and establish initial species hypotheses.

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Section: Verificationmentioning

confidence: 99%

“…Thiery et al 2016;. The above issues also depend on whether query and reference sequences represent the full ITS or only the ITS1 or ITS2 spacer regions (Nilsson et al 2008;Blaalid et al 2013;Tedersoo et al 2015;Garnica et al 2016;Badotti et al 2017;Větrovský et al 2020).…”

Section: Blast Mappingmentioning

confidence: 99%

Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

et al. 2020

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“…Mutual information approaches, based on maximal information coefficients (Reshef et al., 2011), could overcome this dichotomy although these approaches have not stood out in the inference benchmarkings done to date (Hirano & Takemoto, 2019; Weiss et al., 2016). All functional and ecological knowledge available on microorganisms needs to be gathered in databases (Louca et al., 2016; Nguyen et al., 2016; Větrovský et al, 2020) and integrated into network inference processes. In a study of trophic networks, Bohan et al.…”

Section: Discussionmentioning

confidence: 99%

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Barroso-Bergadà

Pauvert

Vallance

et al. 2020

Molecular Ecology Resources

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Environmental DNA contains information on the species interaction networks that support ecosystem functions and services. Next‐generation biomonitoring proposes the use of this data to reconstruct ecological networks in real time and then compute network‐level properties to assess ecosystem change. We investigated the relevance of this proposal by assessing: (i) the replicability of DNA‐based networks in the absence of ecosystem change, and (ii) the benefits and shortcomings of community‐ and network‐level properties for monitoring change. We selected crop‐associated microbial networks as a case study because they support disease regulation services in agroecosystems and analysed their response to change in agricultural practice between organic and conventional systems. Using two statistical methods of network inference, we showed that network‐level properties, especially β‐properties, could detect change. Moreover, consensus networks revealed robust signals of interactions between the most abundant species, which differed between agricultural systems. These findings complemented those obtained with community‐level data that showed, in particular, a greater microbial diversity in the organic system. The limitations of network‐level data included (i) the very high variability of network replicates within each system; (ii) the low number of network replicates per system, due to the large number of samples needed to build each network; and (iii) the difficulty in interpreting links of inferred networks. Tools and frameworks developed over the last decade to infer and compare microbial networks are therefore relevant to biomonitoring, provided that the DNA metabarcoding data sets are large enough to build many network replicates and progress is made to increase network replicability and interpretation.

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“…2), two of which map to SH1566388.08FU (Supplementary data le 6). Based on the globalfungi.com database (Vetrovsky et al, 2020) this SH restricted to North America, indicating that closely related taxa exist globally but that these are likely geographically separated from those captured in our dataset. While addition of publicly available environmental sequences weakened the bPTP ML support for these two PSHs, both remain intact.…”

Section: Global Perspective On Recognized Taxamentioning

confidence: 97%

Peeking into the black box – integrated taxonomy of Archaeorhizomycetes

Khan

Kluting

Tångrot

et al. 2020

Preprint

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Abstract Due to their submerged and cryptic lifestyle the vast majority of fungal species are difficult to observe and describe morphologically and many remains known to science only from sequences detected in environmental samples. The lack of rules to delimit and name most fungal species is a staggering limitation to communication and interpretation of ecology and evolution in kingdom Fungi. Here we use environmental sequence data as taxonomical evidence and take an integrated taxonomic approach by combining phylogenetic and ecological data to generate and test species hypothesis in the class Archaeorhizomycetes (Taphrinomycotina, Ascomycota). Based on environmental amplicon sequencing we recognize 68 distinct phylogenetic species hypotheses (PSHs) of Archaeorhizomycetes, including the two described species in the class, in a well-studied Swedish pine forest podzol soil. Nine of the species hypotheses, including the more abundant ones, are supported by long read data and represent 78% of the sequenced Archaeorhizomycetes community. Among well supported sister PSHs, significantly differential distribution in the soil profile provide additional ecological evidence supporting the identification of two novel species for which we provide molecular diagnostics and propose names. Our analysis indicates that frequent and abundant taxa can be phylogenetically resolved in environmental samples, while rare taxa remain un-captured at our sampling and sequencing intensity. While environmental sequences cannot be automatically translated to species hypothesis, they can be used to generate phylogenetic and ecological evidence supporting species recognition of abundant species without physical specimens.

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GlobalFungi: Global database of fungal records from high-throughput-sequencing metabarcoding studies

Cited by 8 publications

References 15 publications

Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding?

Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Peeking into the black box – integrated taxonomy of Archaeorhizomycetes

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