Misidentification of genome assemblies in public databases: the case of Naumovozyma dairenensis and proposal of a protocol to correct misidentifications

TY - JOUR

T1 - Misidentification of genome assemblies in public databases

T2 - the case of Naumovozyma dairenensis and proposal of a protocol to correct misidentifications

AU - Stavrou, Aimilia A

AU - Mixão, Verónica

AU - Boekhout, Teun

AU - Gabaldón, Toni

N1 - This article is protected by copyright. All rights reserved.

PY - 2018/1/10

Y1 - 2018/1/10

N2 - 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 (SNP) served to correct the identification. We propose that the routine use of some 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.

AB - 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 (SNP) served to correct the identification. We propose that the routine use of some 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.

KW - Journal Article

U2 - 10.1002/yea.3303

DO - 10.1002/yea.3303

M3 - Article

C2 - 29320804

SN - 0749-503X

JO - Yeast (Chichester, England)

JF - Yeast (Chichester, England)

M1 - doi/10.1002/yea.3303

ER -