Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
Nazema Y. Siddiqui
Division of Urogynecology and Reconstructive Pelvic Surgery, Department of Obstetrics and Gynecology, Duke University, Durham, North Carolina, USA
Ian Fields
Division of Urogynecology, Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA
W. Thomas Gregory
Division of Urogynecology, Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA
Holly M. Simon
AnimalBiome, Oakland, California, USA
Michael A. Mooney
Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
Division of Bioinformatics and Computational Biomedicine, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
Accurate species-level identification from culture-independent techniques is of importance for microbial niches that are less well characterized, such as that of the bladder. 16S rRNA amplicon sequencing, a common culture-independent way to identify bacteria, is often critiqued for lacking species-level resolution. Here, we extensively evaluate classification schemes for species-level bacterial annotation of 16S amplicon data from bladder bacteria.
