Many microbiome studies employ reference-based operational taxonomic unit (OTU)-picking methods, which in general, rely on databases cataloguing reference OTUs identified through clustering full-length 16S rRNA genes. Given that the rate of accumulation of mutations are not uniform throughout the length of a 16S rRNA gene across different taxonomic clades, results of OTU identification or taxonomic classification obtained using 'short-read' sequence queries (as generated by next-generation sequencing platforms) can be inconsistent and of suboptimal accuracy. De novo OTU clustering results too can significantly vary depending upon the hypervariable region (V-region) targeted for sequencing. As a consequence, comparison of microbiomes profiled in different scientific studies becomes difficult and often poses a challenge in analysing new findings in context of prior knowledge. The OTUX approach of reference-based OTU-picking proposes to overcome these limitations by using 'customized' OTU reference databases, which can cater to different sets of short-read sequences corresponding to different 16S V-regions. The results obtained with OTUX-approach (which are in terms of OTUX-OTU identifiers) can also be 'mapped back' or represented in terms of other OTU database identifiers/taxonomy, e.g. Greengenes, thus allowing for easy cross-study comparisons. Validation with simulated datasets indicates more efficient, accurate, and consistent taxonomic classifications obtained using OTUX-approach, as compared with conventional methods.

中文翻译：

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OTUX：特定于V区域的OTU数据库，用于改进16S rRNA OTU采摘和微生物组的高效跨研究分类学比较。

许多微生物组研究采用基于参考的操作生物分类单位（OTU）挑选方法，这些方法通常依赖于对通过对全长16S rRNA基因进行聚类确定的参考OTU进行分类的数据库。鉴于跨不同分类进化枝的16S rRNA基因的全长突变积累率不一致，使用“短读”序列查询（由下一代测序平台生成）获得OTU鉴定或分类分类的结果可能不一致并且准确性不佳。从头OTU聚类结果也可能会大大不同，具体取决于测序的目标高变区（V区）。作为结果，在不同的科学研究中比较微生物组变得困难，并且在现有知识的背景下分析新发现时常常构成挑战。基于参考的OTU挑选的OTUX方法建议通过使用“定制的” OTU参考数据库来克服这些限制，该数据库可以满足与不同16S V区域相对应的短读序列的不同集合。使用OTUX方法获得的结果（以OTUX-OTU标识符表示）也可以“映射”或以其他OTU数据库标识符/分类法（例如Greengenes）表示，从而可以轻松进行跨研究比较。与常规方法相比，使用模拟数据集进行的验证表明，使用OTUX方法获得的分类效率更高，更准确且一致性更高。