The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered.

UNITE (https://unite.ut.ee) is a web-based database and sequence management environment for molecular identification of eukaryotes. It targets the nuclear ribosomal internal transcribed spacer (ITS) region and offers nearly 10 million such sequences for reference. These are clustered into ∼2.4M species hypotheses (SHs), each assigned a unique digital object identifier (DOI) to promote unambiguous referencing across studies. UNITE users have contributed over 600 000 third-party sequence annotations, which are shared with a range of databases and other community resources. Recent improvements facilitate the detection of cross-kingdom biological associations and the integration of undescribed groups of organisms into everyday biological pursuits. Serving as a digital twin for eukaryotic biodiversity and communities worldwide, the latest release of UNITE offers improved avenues for biodiversity discovery, precise taxonomic communication and integration of biological knowledge across platforms.

Best practices in metabarcoding of fungi: From experimental design to results.

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Assessing Biotic and Abiotic Interactions of Microorganisms in Amazonia through Co-Occurrence Networks and DNA Metabarcoding.

Species may co-occur due to responses to similar environmental conditions, biological associations, or simply because of coincident geographical distributions. Disentangling patterns of co-occurrence and potential biotic and abiotic interactions is crucial to understand ecosystem function. Here, we used DNA metabarcoding data from litter and mineral soils collected from a longitudinal transect in Amazonia to explore patterns of co-occurrence. We compared data from different Amazonian habitat types, each with a characteristic biota and environmental conditions. These included non-flooded rainforests (terra-firme), forests seasonally flooded by fertile white waters (várzeas) or by unfertile black waters (igapós), and open areas associated with white sand soil (campinas). We ran co-occurrence network analyses based on null models and Spearman correlation for all samples and for each habitat separately. We found that one third of all operational taxonomic units (OTUs) were bacteria and two thirds were eukaryotes. The resulting networks were nevertheless mostly composed of bacteria, with fewer fungi, protists, and metazoans. Considering the functional traits of the OTUs, there is a combination of metabolism modes including respiration and fermentation for bacteria, and a high frequency of saprotrophic fungi (those that feed on dead organic matter), indicating a high turnover of organic material. The organic carbon and base saturation indices were important in the co-occurrences in Amazonian networks, whereas several other soil properties were important for the co-exclusion. Different habitats had similar network properties with some variation in terms of modularity, probably associated with flooding pulse. We show that Amazonian microorganism communities form highly interconnected co-occurrence and co-exclusion networks, which highlights the importance of complex biotic and abiotic interactions in explaining the outstanding biodiversity of the region.

Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker.

Motivation: Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data. Results: We evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality. Availability and implementation: Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/. Supplementary information: Supplementary data are available at Bioinformatics online.

Biodiversity assessments in the 21st century: the potential of insect traps to complement environmental samples for estimating eukaryotic and prokaryotic diversity using high-throughput DNA metabarcoding 1.

The rapid loss of biodiversity, coupled with difficulties in species identification, call for innovative approaches to assess biodiversity. Insects make up a substantial proportion of extant diversity and play fundamental roles in any given ecosystem. To complement morphological species identification, new techniques such as metabarcoding make it possible to quantify insect diversity and insect-ecosystem interactions through DNA sequencing. Here we examine the potential of bulk insect samples (i.e., containing many non-sorted specimens) to assess prokaryote and eukaryote biodiversity and to complement the taxonomic coverage of soil samples. We sampled 25 sites on three continents and in various ecosystems, collecting insects with SLAM traps (Brazil) and Malaise traps (South Africa and Sweden). We then compared our diversity estimates with the results obtained with biodiversity data from soil samples from the same localities. We found a largely different taxonomic composition between the soil and insect samples, testifying to the potential of bulk insect samples to complement soil samples. Finally, we found that non-destructive DNA extraction protocols, which preserve insect specimens for morphological studies, constitute a promising choice for cost-effective biodiversity assessments. We propose that the sampling and sequencing of insect samples should become a standard complement for biodiversity studies based on environmental DNA.

Protax-fungi: a web-based tool for probabilistic taxonomic placement of fungal internal transcribed spacer sequences.

Incompleteness of reference sequence databases and unresolved taxonomic relationships complicates taxonomic placement of fungal sequences. We developed Protax-fungi, a general tool for taxonomic placement of fungal internal transcribed spacer (ITS) sequences, and implemented it into the PlutoF platform of the UNITE database for molecular identification of fungi. With empirical data on root- and wood-associated fungi, Protax-fungi reliably identified (with at least 90% identification probability) the majority of sequences to the order level but only around one-fifth of them to the species level, reflecting the current limited coverage of the databases. Protax-fungi outperformed the Sintax and Rdb classifiers in terms of increased accuracy and decreased calibration error when applied to data on mock communities representing species groups with poor sequence database coverage. We applied Protax-fungi to examine the internal consistencies of the Index Fungorum and UNITE databases. This revealed inconsistencies in the taxonomy database as well as mislabelling and sequence quality problems in the reference database. The according improvements were implemented in both databases. Protax-fungi provides a robust tool for performing statistically reliable identifications of fungi in spite of the incompleteness of extant reference sequence databases and unresolved taxonomic relationships.

Toward a Self-Updating Platform for Estimating Rates of Speciation and Migration, Ages, and Relationships of Taxa.

Rapidly growing biological data-including molecular sequences and fossils-hold an unprecedented potential to reveal how evolutionary processes generate and maintain biodiversity. However, researchers often have to develop their own idiosyncratic workflows to integrate and analyze these data for reconstructing time-calibrated phylogenies. In addition, divergence times estimated under different methods and assumptions, and based on data of various quality and reliability, should not be combined without proper correction. Here we introduce a modular framework termed SUPERSMART (Self-Updating Platform for Estimating Rates of Speciation and Migration, Ages, and Relationships of Taxa), and provide a proof of concept for dealing with the moving targets of evolutionary and biogeographical research. This framework assembles comprehensive data sets of molecular and fossil data for any taxa and infers dated phylogenies using robust species tree methods, also allowing for the inclusion of genomic data produced through next-generation sequencing techniques. We exemplify the application of our method by presenting phylogenetic and dating analyses for the mammal order Primates and for the plant family Arecaceae (palms). We believe that this framework will provide a valuable tool for a wide range of hypothesis-driven research questions in systematics, biogeography, and evolution. SUPERSMART will also accelerate the inference of a "Dated Tree of Life" where all node ages are directly comparable. [Bayesian phylogenetics; data mining; divide-and-conquer methods; GenBank; multilocus multispecies coalescent; next-generation sequencing; palms; primates; tree calibration.].

Habitat conditions and phenological tree traits overrule the influence of tree genotype in the needle mycobiome-Picea glauca system at an arctic treeline ecotone.

Plant-associated mycobiomes in extreme habitats are understudied and poorly understood. We analysed Illumina-generated ITS1 sequences from the needle mycobiome of white spruce (Picea glauca) at the northern treeline in Alaska (USA). Sequences were obtained from the same DNA that was used for tree genotyping. In the present study, fungal metabarcoding and tree microsatellite data were compared for the first time. In general, neighbouring trees shared more fungal taxa with each other than trees growing in further distance. Mycobiomes correlated strongly with phenological host traits and local habitat characteristics contrasting a dense forest stand with an open treeline site. Genetic similarity between trees did not influence fungal composition and no significant correlation existed between needle mycobiome and tree genotype. Our results suggest the pronounced influence of local habitat conditions and phenotypic tree traits on needle-inhabiting fungi. By contrast, the tree genetic identity cannot be benchmarked as a dominant driver for needle-inhabiting mycobiomes, at least not for white spruce in this extreme environment.

Sequence-based classification and identification of Fungi.

Fungal taxonomy and ecology have been revolutionized by the application of molecular methods 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 metagenomic and metatranscriptomic studies, which limits understanding of the taxonomic diversity and metabolic 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. Improvements in sequencing methods, including long-read and single-cell technologies, will empower fungal molecular ecologists to look beyond ITS and current shotgun metagenomics approaches. Data quality and accessibility will be enhanced by attention to data and metadata standards and rigorous enforcement of policies for deposition of data and workflows. Taxonomic communities will need to develop best practices for molecular characterization in their focal clades, while also contributing to globally useful datasets including ITS. Changes to nomenclatural rules are needed to enable validPUBLICation of sequence-based taxon descriptions. Finally, cultural shifts are necessary to promote adoption of SBCI and to accord professional credit to individuals who contribute to community resources.

EUKARYOME: the rRNA gene reference database for identification of all eukaryotes.

Molecular identification of micro- and macroorganisms based on nuclear markers has revolutionized our understanding of their taxonomy, phylogeny and ecology. Today, research on the diversity of eukaryotes in global ecosystems heavily relies on nuclear ribosomal RNA (rRNA) markers. Here, we present the research community-curated reference database EUKARYOME for nuclear ribosomal 18S rRNA, internal transcribed spacer (ITS) and 28S rRNA markers for all eukaryotes, including metazoans (animals), protists, fungi and plants. It is particularly useful for the identification of arbuscular mycorrhizal fungi as it bridges the four commonly used molecular markers-ITS1, ITS2, 18S V4-V5 and 28S D1-D2 subregions. The key benefits of this database over other annotated reference sequence databases are that it is not restricted to certain taxonomic groups and it includes all rRNA markers. EUKARYOME also offers a number of reference long-read sequences that are derived from (meta)genomic and (meta)barcoding-a unique feature that can be used for taxonomic identification and chimera control of third-generation, long-read, high-throughput sequencing data. Taxonomic assignments of rRNA genes in the database are verified based on phylogenetic approaches. The reference datasets are available in multiple formats from the project homepage, http://www.eukaryome.org.

Fungal community analysis by high-throughput sequencing of amplified markers--a user's guide.

Novel high-throughput sequencing methods outperform earlier approaches in terms of resolution and magnitude. They enable identification and relative quantification of community members and offer new insights into fungal community ecology. These methods are currently taking over as the primary tool to assess fungal communities of plant-associated endophytes, pathogens, and mycorrhizal symbionts, as well as free-living saprotrophs. Taking advantage of the collective experience of six research groups, we here review the different stages involved in fungal community analysis, from field sampling via laboratory procedures to bioinformatics and data interpretation. We discuss potential pitfalls, alternatives, and solutions. Highlighted topics are challenges involved in: obtaining representative DNA/RNA samples and replicates that encompass the targeted variation in community composition, selection of marker regions and primers, options for amplification and multiplexing, handling of sequencing errors, and taxonomic identification. Without awareness of methodological biases, limitations of markers, and bioinformatics challenges, large-scale sequencing projects risk yielding artificial results and misleading conclusions.

Nutrient enrichment increased species richness of leaf litter fungal assemblages in a tropical forest.

Microbial communities play a major role in terrestrial ecosystem functioning, but the determinates of their diversity and functional interactions are not well known. In this study, we explored leaf litter fungal diversity in a diverse Panama lowland tropical forest in which a replicated factorial N, P, K and micronutrient fertilization experiment of 40 × 40 m plots had been ongoing for nine years. We extracted DNA from leaf litter samples and used fungal-specific amplification and a 454 pyrosequencing approach to sequence two loci, the nuclear ribosomal internal transcribed spacer (ITS) region and the nuclear ribosomal large subunit (LSU) D1 region. Using a 95% sequence similarity threshold for ITS1 spacer recovered a total of 2523 OTUs, and the number of unique ITS1 OTUs per 0.5-1.0 g leaf litter sample ranged from 55 to 177. Ascomycota were the dominant phylum among the leaf litter fungi (71% of the OTUs), followed by Basidiomycota (26% of the OTUs). In contrast to our expectations based on temperate ecosystems, long-term addition of nutrients increased, rather than decreased, species richness relative to controls. Effect of individual nutrients was more subtle and seen primarily as changes in community compositions especially at lower taxonomic levels, rather than as significant changes in species richness. For example, plots receiving P tended to show a greater similarity in community composition compared to the other nutrient treatments, the +PK, +NK and +NPK plots appeared to be more dominated by the Nectriaceae than other treatments, and indicator species for particular nutrient combinations were identified.

Towards a unified paradigm for sequence-based identification of fungi.

The nuclear ribosomal internal transcribed spacer (ITS) region is the formal fungal barcode and in most cases the marker of choice for the exploration of fungal diversity in environmental samples. Two problems are particularly acute in the pursuit of satisfactory taxonomic assignment of newly generated ITS sequences: (i) the lack of an inclusive, reliable public reference data set and (ii) the lack of means to refer to fungal species, for which no Latin name is available in a standardized stable way. Here, we report on progress in these regards through further development of the UNITE database (http://unite.ut.ee) for molecular identification of fungi. All fungal species represented by at least two ITS sequences in the international nucleotide sequence databases are now given a unique, stable name of the accession number type (e.g. Hymenoscyphus pseudoalbidus|GU586904|SH133781.05FU), and their taxonomic and ecological annotations were corrected as far as possible through a distributed, third-party annotation effort. We introduce the term 'species hypothesis' (SH) for the taxa discovered in clustering on different similarity thresholds (97-99%). An automatically or manually designated sequence is chosen to represent each such SH. These reference sequences are released (http://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and in the QIIME pipeline. The system and the data will be updated automatically as the number of public fungal ITS sequences grows. We invite everybody in the position to improve the annotation or metadata associated with their particular fungal lineages of expertise to do so through the new Web-based sequence management system in UNITE.

How, not if, is the question mycologists should be asking about DNA-based typification.

Fungal metabarcoding of substrates such as soil, wood, and water is uncovering an unprecedented number of fungal species that do not seem to produce tangible morphological structures and that defy our best attempts at cultivation, thus falling outside the scope of the International Code of Nomenclature for algae, fungi, and plants. The present study uses the new, ninth release of the species hypotheses of the UNITE database to show that species discovery through environmental sequencing vastly outpaces traditional, Sanger sequencing-based efforts in a strongly increasing trend over the last five years. Our findings challenge the present stance of some in the mycological community - that the current situation is satisfactory and that no change is needed to "the code" - and suggest that we should be discussing not whether to allow DNA-based descriptions (typifications) of species and by extension higher ranks of fungi, but what the precise requirements for such DNA-based typifications should be. We submit a tentative list of such criteria for further discussion. The present authors hope for a revitalized and deepened discussion on DNA-based typification, because to us it seems harmful and counter-productive to intentionally deny the overwhelming majority of extant fungi a formal standing under the International Code of Nomenclature for algae, fungi, and plants.

DivBayes and SubT: exploring species diversification using Bayesian statistics.

SUMMARY: DivBayes is a program to estimate diversification rates from species richness and ages of a set of clades. SubT estimates diversification rates from node heights within a clade. Both programs implement Bayesian statistics and provide the ability to account for uncertainty in the ages of taxa in the underlying data, an improvement over more commonly used maximum likelihood methods. AVAILABILITY: DivBayes and SubT are released as C++ source code under the GNU GPL v. 3 software license in Supplementary information 1 and 2, respectively, and at http://web.utk.edu/~kryberg/. They have been successfully compiled on various Linux, MacOS X and Windows systems. CONTACT: kryberg@utk.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dnabarcoder: An open-source software package for analysing and predicting DNA sequence similarity cutoffs for fungal sequence identification.

The accuracy and precision of fungal molecular identification and classification are challenging, particularly in environmental metabarcoding approaches as these often trade accuracy for efficiency given the large data volumes at hand. In most ecological studies, only a single similarity cutoff value is used for sequence identification. This is not sufficient since the most commonly used DNA markers are known to vary widely in terms of inter- and intraspecific variability. We address this problem by presenting a new tool, dnabarcoder, to predict local similarity cutoffs and measure the resolving powers of a biomarker for sequence identification for different clades of fungi. It was shown that the predicted similarity cutoffs varied significantly between the clades of a recently released ITS DNA barcode data set from the CBS culture collection of the Westerdijk Fungal Biodiversity Institute. When classifying a large public fungal ITS data set-the UNITE database-against the barcode data set, the local similarity cutoffs assigned fewer sequences than the traditional cutoffs used in metabarcoding studies. However, the obtained accuracy and precision were significantly improved. Our study showed that it might be better to extract the ITS region from the ITS barcodes to optimize taxonomic assignment accuracy. Furthermore, 15.3, 25.6, and 26.3% of the fungal species of the barcode data set were indistinguishable by full-length ITS, ITS1, and ITS2, respectively. Except for these indistinguishable species, the resolving powers of full-length ITS, ITS1, and ITS2 sequences were similar at the species level. Nevertheless, the complete ITS region had a better resolving power at higher taxonomic levels.

Evidence for further non-coding RNA genes in the fungal rDNA region.

Non-coding RNA (ncRNA) genes play important, but incompletely understood, roles in various cellular processes, notably translation and gene regulation. A recent report on the detection of the ncRNA Signal Recognition Particle gene in the nuclear ribosomal internal transcribed spacer region of several species of three genera of ectomycorrhizal basidiomycetes prompted a more thorough bioinformatics search for additional ncRNA genes in the full fungal ribosomal operon. This study reports on the detection of three ncRNA genes hitherto not known from the fungal ribosomal region: nuclear RNase P RNA, RNase MRP RNA, and a possible snoRNA U14 in a total of five species of Auricularia and Inocybe. We verified their presence through resequencing of independent specimens. Two completed Auricularia genomes were found to lack these ncRNAs elsewhere than in the ribosomal operon, suggesting that these are functional genes. It seems clear that ncRNA genes play a larger role in fungal ribosomal genetics than hitherto thought.

Limited decadal growth of mountain birch saplings has minor impact on surrounding tundra vegetation.

Temperatures over the Arctic region are increasing at three times the rate of the global average. Consequently, Arctic vegetation is changing and trees are encroaching into the tundra. In this study, we examine the establishment and growth of mountain birch (Betula pubescens ssp. tortuosa), which forms the treeline in subarctic Europe, and its impact on community composition across the treeline ecotone nearby Abisko, Sweden. Birch advancement along elevational gradients was studied by comparing data collected in 2016 with data collected 10 and 15 years previously. Species identity, cover, and phylogenetic relatedness were used to assess the impact of birch encroachment on community composition. Our results show that birch occurrence above the treeline did not affect plant community composition, probably owing to the observed lack of significant growth due to herbivore browsing, nitrogen limitation, or a reduction in snow cover. Independent of birch performance, the tundra community structure shifted toward a novel community dissimilar from the forest plant community found below the treeline. Taken together, our findings are explained by species-specific responses to climate change, rather than by a linear forest advance. Future treeline advancements are likely more restricted than previously expected.

The curse of the uncultured fungus.

The international DNA sequence databases abound in fungal sequences not annotated beyond the kingdom level, typically bearing names such as "uncultured fungus". These sequences beget low-resolution mycological results and invite further deposition of similarly poorly annotated entries. What do these sequences represent? This study uses a 767,918-sequence corpus of public full-length fungal ITS sequences to estimate what proportion of the 95,055 "uncultured fungus" sequences that represent truly unidentifiable fungal taxa - and what proportion of them that would have been straightforward to annotate to some more meaningful taxonomic level at the time of sequence deposition. Our results suggest that more than 70% of these sequences would have been trivial to identify to at least the order/family level at the time of sequence deposition, hinting that factors other than poor availability of relevant reference sequences explain the low-resolution names. We speculate that researchers' perceived lack of time and lack of insight into the ramifications of this problem are the main explanations for the low-resolution names. We were surprised to find that more than a fifth of these sequences seem to have been deposited by mycologists rather than researchers unfamiliar with the consequences of poorly annotated fungal sequences in molecular repositories. The proportion of these needlessly poorly annotated sequences does not decline over time, suggesting that this problem must not be left unchecked.

Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets.

The ribosomal small subunit (SSU) rRNA gene has emerged as an important genetic marker for taxonomic identification in environmental sequencing datasets. In addition to being present in the nucleus of eukaryotes and the core genome of prokaryotes, the gene is also found in the mitochondria of eukaryotes and in the chloroplasts of photosynthetic eukaryotes. These three sets of genes are conceptually paralogous and should in most situations not be aligned and analyzed jointly. To identify the origin of SSU sequences in complex sequence datasets has hitherto been a time-consuming and largely manual undertaking. However, the present study introduces Metaxa ( http://microbiology.se/software/metaxa/ ), an automated software tool to extract full-length and partial SSU sequences from larger sequence datasets and assign them to an archaeal, bacterial, nuclear eukaryote, mitochondrial, or chloroplast origin. Using data from reference databases and from full-length organelle and organism genomes, we show that Metaxa detects and scores SSU sequences for origin with very low proportions of false positives and negatives. We believe that this tool will be useful in microbial and evolutionary ecology as well as in metagenomics.