Molecular Identification of Fungi: Rationale, Philosophical Concerns, and the UNITE Database

Abstract

Fungi form a ubiquitous group of largely inconspicuous organisms that play key ecological roles in terrestrial nutrient cycling. The typically subterranean or otherwise unnoticeable nature of fungal life has left mycology with a partial understanding of fungal biology, and progress is hampered by factors such as the difficulty to delimit species and individuals of fungi and the sparsity of discriminatory morphological and physiological characters for reliable species identification. These and other complications have paved the way for DNA sequence data as an important source of information in mycology, and there are now twenty years' worth of fungal sequence data available for scientific purposes. However, issues of data reliability and generality impede the use of publicly available fungal DNA sequences. The UNITE database for molecular identification of fungi (http://unite.ut.ee) was built as a response to the difficulties facing anyone seeking to identify environmental samples of fungi to species level using molecular data and the major international sequence databases. The present study describes the UNITE database and examines the role of UNITE in the light of emerging sequencing technologies, notably massively parallel ("454") pyrosequencing. Environmental sampling of fungi is discussed from a taxonomic perspective.

Large‐scale fungal diversity assessment in the Andean Yungas forests reveals strong community turnover among forest types along an altitudinal gradient

“…Although there is no universal cut-off value for species delimitation in fungi due to a substantial variability in nucleotide substitution rates and ages of species across fungal lineages, it has been shown that 2-3% ITS sequence divergence usually delimits different species in many basidiomycete lineages (Hughes et al 2009) and a 97% sequence similarity cut-off value is routinely used in fungal community studies (O'Brien et al 2005;Bjorbaekmo et al 2010;Bellemain et al 2013;Brown et al 2013;Lindahl et al 2013). Therefore, we clustered the quality-filtered sequences into OTUs based on 97% sequence similarity using OTUPIPE (Edgar et al 2011) while simultaneously removing 248 064 putatively chimeric sequences using a curated data set of fungal ITS sequences of Nilsson et al (2011) as reference data set. Representative sequences of the OTUs were subjected to two independent similarity searches.…”

Long‐term increase in snow depth leads to compositional changes in arctic ectomycorrhizal fungal communities

“…The quality-filtered sequences were normalized following Gihring et al (2012) by random subsampling so that each sample contained equal number of sequences. We then clustered the sequences into operational taxonomic units (OTUs) using OTUpipe (Edgar, 2010) with the simultaneous removal of putatively chimeric sequences using de novo and reference-based filtering using the curated dataset of fungal ITS sequences of Nilsson et al (2011), with the default settings. We used a 97% sequence similarity clustering threshold following many other fungal ecology studies (e.g.…”

A review of melanized (black) fungal contamination in pharmaceutical products-incidence, drug recall and control measures

“…There are two major sequencing targets for fungal identification. These are the D1/D2 region of the large ribosome subunit (LSU) and the internal transcribed spacer regions (ITS1/ITS2) (Nelsson et al 2011). These methods are very expensive and need technically skilled personnel and thus they cannot be performed in basic level laboratories.…”