The internal transcribed spacer 2 (ITS2) is a widely used phylogenetic marker. In the past, it has mainly been used for species level classifications. Nowadays, a wider applicability becomes apparent. Here, the conserved structure of the RNA molecule plays a vital role. We have developed the ITS2 Database (http://its2.bioapps.biozentrum.uni-wuerzburg.de) which holds information about sequence, structure and taxonomic classification of all ITS2 in GenBank. In the new version, we use Hidden Markov models (HMMs) for the identification and delineation of the ITS2 resulting in a major redesign of the annotation pipeline. This allowed the identification of more than 160 000 correct full length and more than 50 000 partial structures. In the web interface, these can now be searched with a modified BLAST considering both sequence and structure, enabling rapid taxon sampling. Novel sequences can be annotated using the HMM based approach and modelled according to multiple template structures. Sequences can be searched for known and newly identified motifs. Together, the database and the web server build an exhaustive resource for ITS2 based phylogenetic analyses.

The internal transcribed spacer 2 (ITS2) is a well-established marker for phylogenetic analyses in eukaryotes. A reliable resource for reference sequences and their secondary structures is the ITS2 database (http://its2.bioapps.biozentrum.uni-wuerzburg.de/). However, the database was last updated in 2011. Here, we present a major update of the underlying data almost doubling the number of entities. This increases the number of taxa represented within all major eukaryotic clades. Moreover, additional data has been added to underrepresented groups and some new groups have been added. The broader coverage across the tree of life improves phylogenetic analyses and the capability of ITS2 as a DNA barcode.

Identification of the species origin of pollen has many applications, including assessment of plant–pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and has few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.

The identification of pollen plays an important role in ecology, palaeo‐climatology, honey quality control and other areas. Currently, expert knowledge and reference collections are essential to identify pollen origin through light microscopy. Pollen identification through molecular sequencing and DNA barcoding has been proposed as an alternative approach, but the assessment of mixed pollen samples originating from multiple plant species is still a tedious and error‐prone task. Next‐generation sequencing has been proposed to avoid this hindrance. In this study we assessed mixed pollen probes through next‐generation sequencing of amplicons from the highly variable, species‐specific internal transcribed spacer 2 region of nuclear ribosomal DNA. Further, we developed a bioinformatic workflow to analyse these high‐throughput data with a newly created reference database. To evaluate the feasibility, we compared results from classical identification based on light microscopy from the same samples with our sequencing results. We assessed in total 16 mixed pollen samples, 14 originated from honeybee colonies and two from solitary bee nests. The sequencing technique resulted in higher taxon richness (deeper assignments and more identified taxa) compared to light microscopy. Abundance estimations from sequencing data were significantly correlated with counted abundances through light microscopy. Simulation analyses of taxon specificity and sensitivity indicate that 96% of taxa present in the database are correctly identifiable at the genus level and 70% at the species level. Next‐generation sequencing thus presents a useful and efficient workflow to identify pollen at the genus and species level without requiring specialised palynological expert knowledge.