Schloss PD, Gevers D, Westcott SL.. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS One 6: e27310

Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS ONE (Impact Factor: 3.23). 12/2011; 6(12):e27310. DOI: 10.1371/journal.pone.0027310
Source: PubMed


The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.

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    • "Sequencing was performed using the Roche 454 FLX Titanium platform with primers for the amplification of hypervariable regions 1 through 3 (V1–3) of the 16S rRNA gene, as previously described[2]. Sequence reads were trimmed and analyzed using the QIIME software package[32]. These were then clustered into OTUs and used to characterize the diversity of the lung microbiota in the BAL. "

    Full-text · Article · Dec 2016
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    • "The nine hypervariable (V) regions of the 16S rRNA gene differ between species , and depending on the V region chosen, one can discriminate some species but not others. Hence the use of different V regions influences operational taxonomic unit (OUT) clustering, suggesting caution when analyzing these data[27]. For this study we used the V4 hypervariable region which has poor resolution below the genus level[31]but a sequence length compatible with current sequencing technologies. "

    Full-text · Dataset · Jan 2016
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    • "In total, 1 161 057 sequences were obtained. Bioinformatic processing of sequences was performed in mothur following the standard operational protocol (Schloss et al., 2011), including PyroNoise, which reduces the sequencing error rate by correcting the original flowgram data (Schloss et al., 2011). Sequences were aligned against the SILVA reference database and taxonomically assigned using mothur's naive Bayesian classifier . "
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