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dc.contributor.authorSchloss, Patrick D.
dc.contributor.authorGevers, Dirk
dc.contributor.authorWestcott, Sarah L.
dc.date.accessioned2012-02-23T18:03:59Z
dc.date.available2012-02-23T18:03:59Z
dc.date.issued2011-12
dc.date.submitted2011-07
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/69169
dc.description.abstractThe 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[superscript 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.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (1R01HG005975-01)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (award #0743432)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant NIHU54HG004969)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0027310en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titleReducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studiesen_US
dc.typeArticleen_US
dc.identifier.citationSchloss, Patrick D., Dirk Gevers, and Sarah L. Westcott. “Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies.” Ed. Jack Anthony Gilbert. PLoS ONE 6.12 (2011): e27310. Web. 23 Feb. 2012.en_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.approverGevers, Dirk
dc.contributor.mitauthorGevers, Dirk
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSchloss, Patrick D.; Gevers, Dirk; Westcott, Sarah L.en
mit.licensePUBLISHER_CCen_US


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