2013
DOI: 10.1038/nmeth.2276
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Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing

Abstract: High-throughput sequencing has revolutionized microbial ecology, but read quality remains a significant barrier to accurate taxonomy assignment and alpha diversity assessment for microbial communities. We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq, and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data from, a… Show more

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Cited by 3,469 publications
(2,340 citation statements)
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References 15 publications
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“…Designing primers to successfully amplify ITS amplicons with Illumina MiSeq is a recent advance and many questions about appropriate data quality control remain . Researchers are conflicted over appropriate ways to deal with uneven sequencing depth (McMurdie and Holmes, 2014), and how to use negative controls and mock communities to calibrate bioinformatic pipelines (Bokulich et al, 2013;Nguyen et al, 2014). We took great pains to account for uneven sequencing depth, mock communities and negative controls with our analyses.…”
Section: Discussionmentioning
confidence: 99%
“…Designing primers to successfully amplify ITS amplicons with Illumina MiSeq is a recent advance and many questions about appropriate data quality control remain . Researchers are conflicted over appropriate ways to deal with uneven sequencing depth (McMurdie and Holmes, 2014), and how to use negative controls and mock communities to calibrate bioinformatic pipelines (Bokulich et al, 2013;Nguyen et al, 2014). We took great pains to account for uneven sequencing depth, mock communities and negative controls with our analyses.…”
Section: Discussionmentioning
confidence: 99%
“…A complete set of samples was not available for all subjects because of issues with logistics and subjects dropping out of the study. Processing of the biological samples, and generation of 16S rRNA sequencing data was as described in (Bokulich et al, 2013;Dogra et al, 2015). Briefly, the variable regions V4-V5-V6 (V456) of the 16S rRNA gene were used to characterise the microbiota by the barcoded-primer approach to multiplex pyrosequencing.…”
Section: S Rrna Sequencing and Qpcrmentioning
confidence: 99%
“…Taxonomy assignment of OTU representative sequences used the RDP Classifier with confidence threshold of 0.6 (Wang et al, 2007) on the Greengenes reference database v.13.8 (McDonald et al, 2012). After quality filtering (Bokulich et al, 2013), similarities between all samples were computed as Binary Jaccard (for similarity in community membership) or Bray Curtis (for similarity in community structure) distances using OTUs (the lowest discriminant units with this technology). Diversity analyses were calculated in QIIME on data rarefied at 200 reads per sample.…”
Section: S Rrna Sequencing and Qpcrmentioning
confidence: 99%
“…The FastTree program (Price et al 2009) was used to construct a phylogeny based on the representative OTU sequences. In order to ensure the accuracy of the results, OTUs with an abundance value <0.001% of the total number of sequences were removed (Bokulich et al 2012). The ACE index was used to measure community richness (Pitta et al 2010), and the Shannon index was used to evaluate microbial diversity in soil samples (Shannon and Weaver 1949).…”
Section: Clustering and Annotation Of Operational Taxonomic Unitsmentioning
confidence: 99%