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Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing
High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389483/ https://www.ncbi.nlm.nih.gov/pubmed/27980100 http://dx.doi.org/10.1093/nar/gkw984 |
Sumario: | High-throughput sequencing of 16S rRNA gene amplicons (16S-seq) has become a widely deployed method for profiling complex microbial communities but technical pitfalls related to data reliability and quantification remain to be fully addressed. In this work, we have developed and implemented a set of synthetic 16S rRNA genes to serve as universal spike-in standards for 16S-seq experiments. The spike-ins represent full-length 16S rRNA genes containing artificial variable regions with negligible identity to known nucleotide sequences, permitting unambiguous identification of spike-in sequences in 16S-seq read data from any microbiome sample. Using defined mock communities and environmental microbiota, we characterized the performance of the spike-in standards and demonstrated their utility for evaluating data quality on a per-sample basis. Further, we showed that staggered spike-in mixtures added at the point of DNA extraction enable concurrent estimation of absolute microbial abundances suitable for comparative analysis. Results also underscored that template-specific Illumina sequencing artifacts may lead to biases in the perceived abundance of certain taxa. Taken together, the spike-in standards represent a novel bioanalytical tool that can substantially improve 16S-seq-based microbiome studies by enabling comprehensive quality control along with absolute quantification. |
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