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OTUX: V-region specific OTU database for improved 16S rRNA OTU picking and efficient cross-study taxonomic comparison of microbiomes
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 le...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476724/ https://www.ncbi.nlm.nih.gov/pubmed/30624596 http://dx.doi.org/10.1093/dnares/dsy045 |
Sumario: | 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. |
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