Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK)

Nilsson, R. Henrik; Taylor, Andy F. S.; Adams, Rachel I.; Baschien, Christiane; Bengtsson‐Palme, Johan; Cangren, Patrik; Coleine, Claudia; Daniel, Heide-Marie; Glassman, Sydney I.; Hirooka, Y.; Irinyi, László; Iršėnaitė, Reda; Martin‐Sanchez, Pedro M.; Meyer, Wieland; Oh, Se‐jin; Sampaio, José Paulo; Seifert, Keith A.; Sklenář, František; Stubbe, Dirk; Suh, Sung-Oui; Summerbell, Richard C.; Svantesson, Sten; Unterseher, Martin; Visagie, C.M.; Weiß, Michael; Woudenberg, J.H.C.; Wurzbacher, Christian; Wyngaert, Silke Van den; Yılmaz, Neriman; Yurkov, Andrey; Kõljalg, Urmas; Abarenkov, Kessy

doi:10.3897/mycokeys.28.20887

Cited by 30 publications

(30 citation statements)

References 47 publications

(56 reference statements)

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“…In guise of a conclusion, we would like to translate our experience into a number of recommendations for good practice, several of which are repeating recent conclusions on the taxonomic annotation of public fungal ITS sequences from the built environment (Nilsson et al 2018). Nearly all of these recommendations are specifically addressing fungal taxonomists:…”

Section: Recommendations For Fungal Taxonomistsmentioning

confidence: 99%

The unbearable lightness of sequenced-based identification

Hofstetter

Buyck

Eyssartier³

et al. 2019

Fungal Diversity

102

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Using the basic GenBank local alignment search tool program (BLAST) to identify fungi collected in a recently protected beech forest at Montricher (Switzerland), the number of ITS sequences associated to the wrong taxon name appears to be around 30%, even higher than previously estimated. Such results rely on the in-depth re-examination of BLAST results for the most interesting species that were collected, viz. first records for Switzerland, rare or patrimonial species and problematic species (when BLAST top scores were equally high for different species), all belonging to Agaricomycotina. This paper dissects for the first time a number of sequence-based identifications, thereby showing in every detail-particularly to the user community of taxonomic information-why sequence-based identification in the context of a fungal inventory can easily go wrong. Our first conclusion is that in-depth examination of BLAST results is too time consuming to be considered as a routine approach for future inventories: we spent two months on verification of approx. 20 identifications. Apart from the fact that poor taxon coverage in public depositories remains the principal impediment for successful species identification, it can be deplored that even very recent fungal sequence deposits in GenBank involve an uncomfortably high number of misidentifications or errors with associated metadata. While checking the original publications associated with top score sequences for the few examples that were here reexamined , a positive consequence is that we uncovered over 80 type sequences that were not annotated as types in GenBank. Advantages and pitfalls of sequence-based identification are discussed, particularly in the light of undertaking fungal inventories. Recommendations are made to avoid or reduce some of the major problems with sequence-based identification. Nevertheless, the prospects for a more reliable sequence-based identification of fungi remain quite dim, unless authors are ready to check and update the metadata associated with previously deposited sequences in their publications.

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Section: Recommendations For Fungal Taxonomistsmentioning

confidence: 99%

The unbearable lightness of sequenced-based identification

Hofstetter

Buyck

Eyssartier³

et al. 2019

Fungal Diversity

102

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“…The taxonomy of representative 16S rRNA gene sequences was determined using Python Nearest Alignment Space Termination (PyNAST) with the Greengenes 13_8 database as the reference, and multiple alignments of representative sequences were performed using PyNAST (Al-Hebshi et al, 2015). The taxonomy of representative ITS sequences was determined using the UNITE database (Nilsson et al, 2018). Representative 16S rRNA gene or ITS1 sequences were classified into specific taxa using the Ribosome Database Project (RDP) classifier (Whelan and Surette, 2017).…”

Section: Sequence Processing and Statistical Analysismentioning

confidence: 99%

The Interactions of Airway Bacterial and Fungal Communities in Clinically Stable Asthma

Liu

Liang

et al. 2020

Front. Microbiol.

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Dysbiotic airway microbiota play important roles in the inflammatory progression of asthma, and exploration of airway microbial interactions further elucidates asthma pathogenesis. However, little is known regarding the airway bacterial-fungal interactions in asthma patients. We conducted a cross-sectional survey of the sputum bacterial and fungal microbiota from 116 clinically stable asthma patients and 29 healthy controls using 16S rRNA gene and ITS1 sequencing. Compared with healthy individuals, asthma patients exhibited a significantly altered microbiota and increased bacterial and fungal alpha diversities in the airway. Microbial genera Moraxella, Capnocytophaga, and Ralstonia (bacteria) and Schizophyllum, Candida, and Phialemoniopsis (fungi) were more abundant in the asthma airways, while Rothia, Veillonella and Leptotrichia (bacteria) and Meyerozyma (fungus) were increased in healthy controls. The Moraxellaceae family and their genus Moraxella were significantly enriched in asthma patients compared with healthy controls (80.5-fold, P = 0.007 and 314.7-fold, P = 0.027, respectively). Moreover, Moraxellaceae, along with Schizophyllum, Candida, and Aspergillus (fungal genera), were positively associated with fungal alpha diversity. Correlation networks revealed 3 fungal genera (Schizophyllum, Candida, and Aspergillus) as important airway microbes in asthma that showed positive correlations with each other and multiple co-exclusions with other common microbiota. Moraxellaceae members were positively associated with asthma-enriched fungal taxa but negatively related to several healthyenriched bacterial taxa. Collectively, our findings revealed an altered microbiota and complex microbial interactions in the airways of asthma patients. The Moraxellaceae family and their genus Moraxella, along with 3 important fungal taxa, showed significant interactions with the airway microbiota, providing potential insights into the novel pathogenic mechanisms of asthma.

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“…DNA metabarcoding has become a commonly used tool because it provides an estimate of biodiversity, including that of taxa that cannot be cultured, and identification relies on relatively stable genetic information rather than often variable and subtle phenotypic characters. Limitations of the method include the fact that marker regions and PCR primers must be selected a priori to detect the taxa of interest and that the variability of the marker region, and how well the taxa are represented within a given reference database, determines the success of the identification of the members of an assemblage (Nilsson et al, ).…”

Section: Introductionmentioning

confidence: 99%

Long‐read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments

Heeger

Bourne

Baschien

et al. 2018

Molecular Ecology Resources

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108

103

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DNA metabarcoding is widely used to study prokaryotic and eukaryotic microbial diversity. Technological constraints limit most studies to marker lengths below 600 base pairs (bp). Longer sequencing reads of several thousand bp are now possible with third-generation sequencing. Increased marker lengths provide greater taxonomic resolution and allow for phylogenetic methods of classification, but longer reads may be subject to higher rates of sequencing error and chimera formation. In addition, most bioinformatics tools for DNA metabarcoding were designed for short reads and are therefore unsuitable. Here, we used Pacific Biosciences circular consensus sequencing (CCS) to DNA-metabarcode environmental samples using a ca. 4,500 bp marker that included most of the eukaryote SSU and LSU rRNA genes and the complete ITS region. We developed an analysis pipeline that reduced error rates to levels comparable to short-read platforms. Validation using a mock community indicated that our pipeline detected 98% of chimeras de novo. We recovered 947 OTUs from water and sediment samples from a natural lake, 848 of which could be classified to phylum, 397 to genus and 330 to species. By allowing for the simultaneous use of three databases (Unite, SILVA and RDP LSU), long-read DNA metabarcoding provided better taxonomic resolution than any single marker. We foresee the use of long reads enabling the cross-validation of reference sequences and the synthesis of ribosomal rRNA gene databases. The universal nature of the rRNA operon and our recovery of >100 nonfungal OTUs indicate that long-read DNA metabarcoding holds promise for studies of eukaryotic diversity more broadly.

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Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK)

Cited by 30 publications

References 47 publications

The unbearable lightness of sequenced-based identification

The unbearable lightness of sequenced-based identification

The Interactions of Airway Bacterial and Fungal Communities in Clinically Stable Asthma

Long‐read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments

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