Distinct approaches for the detection and removal of chimeric 16S rRNA sequences can significantly affect the outcome of between-site comparisons

Aquatic Microbial Ecology

Gorazd Avguštin¹  Blaž Stres¹  Nika Zajec¹ 

关键词: 16S rRNA;    Chimera;    Beta diversity;    Clustering;    Pairwise significance;    UniFrac;    Classifier;   

DOI  :  10.3354/ame01510

学科分类：生物科学（综合）

来源: Inter-Research

【 摘 要 】

ABSTRACT: Comparative analyses of 16S rRNA clone libraries represent a standard tool in microbial ecology. Chimeric sequences are generally removed prior to such comparisons. A literature survey revealed a general pattern: (1) most commonly a single chimera identification approach (CIA) has been used; (2) putative chimeras have routinely been discarded without manual examination; (3) chimera filtered datasets have been submitted to repositories. To explore the effects of various CIAs on the study of microbial β-diversity relationships using complete primary data, 4 bacterial and 4 archaeal clone libraries were generated from a submarine spring and analyzed together with 3 bacterial and 3 archaeal published primary datasets. The primary datasets were compared with their 8 different CIA filtered datasets using Chimera_check, CCODE, Pintail, Chimera Slayer and Bellerophon, the last with 4 different settings. When CIA filtered datasets were pooled according to the CIA used, no significant differences between them could be observed, although there was not complete congruency between the different CIAs. When CIA filtered datasets of the same clone library were compared, generally no significant differences could be observed. In contrast, when CIA filtered datasets of different clone libraries were compared, the statistical significance of the relationships shifted from significant to insignificant or vice-versa in many cases depending on the CIA used. This precludes a correct identification of β-diversity. To solve this problem, we treated all CIA filtered datasets and primary data of a single clone library as CIA replicates in non-parametric MANOVA. This enabled unambiguous delineation of environmental samples by taking into account all CIA introduced data modifications.

【 授权许可】

Unknown