Taxonomic misidentification in public DNA databases

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There is a growing problem of taxonomic misidentification in public DNA databases, and this issue is highlighted by Bridge et al. (pp. 43–48). DNA sequences are becoming the primary currency by which we measure and study microbial biodiversity (Tautz et al., 2003). The sheer volume of sequence data places enormous pressure on public sequence databases (such as GenBank and EMBL), which must curate and annotate an ever-growing catalogue of genomic and environmental sequences. Like a library in which books are sometimes mistakenly assigned a wrong call number, sequence errors inevitably end up in public databases. A significant portion of the data in public databases is known to contain random as well as systematic sequencing errors (Clark & Whittam, 1992; Harris, 2003). The exponential proliferation of environmental sequences (e.g. directly from soil and other environmental clone banks) has also resulted in the proliferation of an important class of data for which voucher specimens are not available. While environmental sequences can provide useful information about microbial diversity (Vandenkoornhuyse et al., 2002), their relevance in DNA databases depends on comparison with reference material from voucher-based taxonomic studies.

‘Upwards of 20% of the named sequences in public databases may be misidentified’

Bridge et al. retrieved multiple sequences for ribosomal DNA sequences from the EMBL server for three relatively well-studied groups of fungi (Phoma, Amanita and the Helotiales). Using a combination of standard bioinformatics search tools (FASTA, BLAST) as well as phylogenetic analyses of aligned sequences, they identified numerous cases of obvious or apparent mistaken identity in each group of fungi. The problems in each data set are different. In the case of Amanita, most problems are attributable to the use of misidentified cultures (presumably from well reputed culture collections). In the case of Helotiales, the problem stems from the diversity of investigators who work with these groups, and might also reflect differences in taxonomy between specialists. The most common causes of errors they cite include misidentification or mislabeling of original materials, contamination by other fungi during culture, or other PCR-based errors including chimaeric sequences. Although their study primarily addresses ribosomal DNA sequences (the most common type of data used for molecular systematics), it applies to any genes for which comparative sequence data may be collected. By their estimate, upwards of 20% of the named sequences in public databases may be misidentified in some fungi. This is a serious problem, which threatens the utility of public sequence databases as archives of biodiversity.

By their inclusivity, public databases sometimes become a home for junk data (like the rest of the internet). Unless curated and annotated, mistakes can proliferate and reduce the ability of correct entries to serve as references for new data. For this reason, Bridge et al. recommend that protocols be adopted by public databases similar to those used by taxonomic and culture collections. Like forensic biologists who must adhere to fixed standards for handling of DNA evidence, natural historians who study DNA must also apply appropriate controls to guarantee the identity of strains, collections, and even DNA samples. One solution they propose is the annotation of questionable database sequences by taxonomic experts who work with the public databases.

How serious is the threat of misidentified sequences? As DNA databases grow, the most blatant taxonomic misidentifications are often sorted out by nonspecialists (e.g. a basidiomycete sequence that erroneously groups with ascomycete sequences). The inclusivity of public databases may also be their greatest asset. The prospects for better taxonomic accuracy of databases may not be so bad. In most instances, it is still too hard to tell if a sequence has been misidentified with certainty. For most fungi, taxonomic coverage in public databases is still relatively poor (< 1% of the estimated 1.5 million fungal species are represented in public databases). Although a complete taxonomic scaffolding is not yet in place, programs have recently been established to populate the fungal ‘tree of life’ with useful sequences (e.g. the Deep Hypha research coordination network supported by the U.S. National Science Foundation, http://ocid.NACSE.ORG/research/deephyphae/). By working together, the greater community of systematists can help plug the larger gaping holes that exist in our taxonomic coverage of known fungi, and also help in the discovery of unknown groups (Vandenkoornhuyse et al., 2002). Another solution is for the systematics community to develop special-purpose databases for taxonomic identification, such as the Ribosomal Database Project (http://rdp.cme.msu.edu), with tools necessary for accurate sequence identification and analysis. New sequences will always need to be compared against standard reference data sets as well as special aligned sequence data sets for ribosomal DNA-based identification in mycorrhizal fungi (Bruns et al., 1998), yeasts (Kurtzman & Robnett, 1997; Scorzetti et al., 2002), and agarics (Moncalvo et al., 2002).

It will always be the responsibility of users to check the identity of specimens and the integrity of their sequence data. As with all systematics research, responsible vouchering is also essential (Agerer et al., 2000).

Ancillary

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