Online sequence databases such as NCBI GenBank serve as a tremendously useful platform for researchers to share and reuse published data. However, submission systems lack control for errors such as organism misidentification, which once entered in the database can be propagated and mislead downstream analyses. Here we present an illustrating case of misidentification of Candida albicans from a clinical sample as Naumovozyma dairenensis based on whole-genome shotgun data. Analyses of phylogenetic markers, read mapping and single nucleotide polymorphisms served to correct the identification. We propose that the routine use of such analyses could help to detect misidentifications arising from unsupervised analyses and correct them before they enter the databases. Finally, we discuss broader implications of such misidentifications and the difficulty of correcting them once they are in the records.

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska‐Curie grant agreement no. H2020‐MSCA‐ITN‐2014‐642095, ‘OPATHY’. T.G. acknowledges support from the Spanish Ministry of Economy and Competitiveness grant BFU2015‐67107 cofounded by European Regional Development Fund; from the European Union and ERC Seventh Framework Programme (FP7/2007‐2013) under grant agreement ERC‐2012‐StG‐310325; from the Catalan Research Agency (AGAUR) SGR857; and from the CERCA Programme/Generalitat de Catalunya. The authors declare that there is no conflict of interest.