The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Microbial phylogenetic profiling with the Pacific Biosciences sequencing platform
Open accessAbstract
High-throughput sequencing of 16S rRNA gene amplicons has revolutionized the capacity and depth of microbial community profiling. Several sequencing platforms are available, but most phylogenetic studies are performed on the 454-pyrosequencing platform because its longer reads can give finer phylogenetic resolution. The Pacific Biosciences (PacBio) sequencing platform is significantly less expensive per run, does not rely on amplification for library generation, and generates reads that are, on average, four times longer than those from 454 (C2 chemistry), but the resulting high error rates appear to preclude its use in phylogenetic profiling. Recently, however, the PacBio platform was used to characterize four electrosynthetic microbiomes to the genus-level for less than USD 1,000 through the use of PacBio's circular consensus sequence technology. Here, we describe in greater detail: 1) the output from successful 16S rRNA gene amplicon profiling with PacBio, 2) how the analysis was contingent upon several alterations to standard bioinformatic quality control workflows, and 3) the advantages and disadvantages of using the PacBio platform for community profiling.
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Cites
Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies
Schloss, P. D., Gevers, D., & Westcott, S. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027310 · Cited by 2,112
Quail, M., Smith, M. E., Coupland, P., Otto, T. D., Harris, S. R., Connor, T. R., Bertoni, A., Swerdlow, H. P., & Gu, Y. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-341 · Cited by 2,031 · cited 3×
“Illumina and 454 have low error rates, but the errors are positional (they increase distally, with guanine-cytosine (GC) content, or with homopolymers) [8,9,15].”
BackgroundEvaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research
Jumpstart Consortium Human Microbiome Project Data Generation Working Group (2012) · PLoS ONE · doi:10.1371/journal.pone.0039315 · Cited by 297
Cited by
The Madness of Microbiome: Attempting To Find Consensus “Best Practice” for 16S Microbiome Studies
Pollock, J., Glendinning, L., Wisedchanwet, T., & Watson, M. (2018) · Applied and Environmental Microbiology · doi:10.1128/aem.02627-17 · Cited by 575
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics
Escobar-Zepeda, A., Vera-Ponce de León, A., & Sanchez-Flores, A. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00348 · Cited by 363
Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system.
Schloss, P. D., Jenior, M. L., Koumpouras, C. C., Westcott, S. L., & Highlander, S. K. (2016) · PeerJ · doi:10.7717/peerj.1869 · Cited by 226
“Previous studies utilizing the technology have removed reads with mismatched primers89 and barcodes, ambiguous base calls, and low quality scores (Fichot & Norman, 2013) or screened90 sequences based on the predicted error rate (Singer et al., 2016).”
BackgroundA comprehensive evaluation of long read error correction methods
Zhang, H., Jain, C., & Aluru, S. (2020) · BMC Genomics · doi:10.1186/s12864-020-07227-0 · Cited by 150
Metadata
- Authors
- Fichot, E. B., & Norman, R. S.
- Venue
- Microbiome
- Published
- 2013-03-04
- Volume
- 1
- Issue
- 1
- Pages
- 10-10
- ISSN
- 2049-2618 (ISSN-L)
- OpenAlex
- W2126132152
- Cited by
- 159
- References
- 3
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- gold
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