Stavrou Aimilia A, Mixão Verónica, Boekhout Teun, Gabaldón Toni
Westerdijk Fungal Biodiversity Institute, 3584, Utrecht, The Netherlands.
Institute for Biodiversity and ecosystem Dynamics, University of Amsterdam, 1012, WX, Amsterdam, The Netherlands.
Yeast. 2018 Jun;35(6):425-429. doi: 10.1002/yea.3303. Epub 2018 Feb 22.
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.
诸如NCBI基因库之类的在线序列数据库，为研究人员共享和重用已发表的数据提供了极为有用的平台。然而，提交系统缺乏对诸如生物误识别等错误的控制，一旦这些错误录入数据库，就可能传播并误导下游分析。在此，我们展示一个基于全基因组鸟枪法数据将临床样本中的白色念珠菌误识别为大连瑙莫酵母的案例。系统发育标记分析、读段比对和单核苷酸多态性分析有助于纠正这一误识别。我们建议，常规使用此类分析有助于检测无监督分析产生的误识别，并在其进入数据库之前予以纠正。最后，我们讨论了此类误识别的更广泛影响以及一旦记录在案就难以纠正的问题。
