The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Microbial Community Composition and Diversity via 16S rRNA Gene Amplicons: Evaluating the Illumina Platform
Open accessAbstract
As new sequencing technologies become cheaper and older ones disappear, laboratories switch vendors and platforms. Validating the new setups is a crucial part of conducting rigorous scientific research. Here we report on the reliability and biases of performing bacterial 16S rRNA gene amplicon paired-end sequencing on the MiSeq Illumina platform. We designed a protocol using 50 barcode pairs to run samples in parallel and coded a pipeline to process the data. Sequencing the same sediment sample in 248 replicates as well as 70 samples from alkaline soda lakes, we evaluated the performance of the method with regards to estimates of alpha and beta diversity. Using different purification and DNA quantification procedures we always found up to 5-fold differences in the yield of sequences between individually barcodes samples. Using either a one-step or a two-step PCR preparation resulted in significantly different estimates in both alpha and beta diversity. Comparing with a previous method based on 454 pyrosequencing, we found that our Illumina protocol performed in a similar manner - with the exception for evenness estimates where correspondence between the methods was low. We further quantified the data loss at every processing step eventually accumulating to 50% of the raw reads. When evaluating different OTU clustering methods, we observed a stark contrast between the results of QIIME with default settings and the more recent UPARSE algorithm when it comes to the number of OTUs generated. Still, overall trends in alpha and beta diversity corresponded highly using both clustering methods. Our procedure performed well considering the precisions of alpha and beta diversity estimates, with insignificant effects of individual barcodes. Comparative analyses suggest that 454 and Illumina sequence data can be combined if the same PCR protocol and bioinformatic workflows are used for describing patterns in richness, beta-diversity and taxonomic composition.
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QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055
vegan: Community Ecology Package
Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., et al. (2001) · CRAN: Contributed Packages · doi:10.32614/cran.package.vegan · Cited by 22,615
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,826
Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00062-07 · Cited by 19,885
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 19,549
Proceedings of the National Academy of Sciences of the United States of America
Dowling, J. E., & Wald, G. (2009) · Nutrition Reviews · doi:10.1111/j.1753-4887.1981.tb06752.x · Cited by 17,893
UPARSE: highly accurate OTU sequences from microbial amplicon reads
Edgar, R. C. (2013) · Nature Methods · doi:10.1038/nmeth.2604 · Cited by 16,707
UCHIME improves sensitivity and speed of chimera detection
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., & Knight, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr381 · Cited by 15,114
FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Cited by
FROGS: Find, Rapidly, OTUs with Galaxy Solution
Escudié, F., Auer, L., Bernard, M., Mariadassou, M., Cauquil, L., Vidal, K., Maman, S., Hernandez-Raquet, G., Combes, S., & Pascal, G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx791 · Cited by 1,018
Decomposing multiple dimensions of stability in global change experiments
Hillebrand, H., Langenheder, S., Lebret, K., Lindström, E., Östman, Ö., & Striebel, M. (2018) · Ecology Letters · doi:10.1111/ele.12867 · Cited by 312
Allali, I., Arnold, J. W., Roach, J., Cadenas, M. B., Butz, N., Hassan, H. M., Koci, M., Ballou, A., Mendoza, M., Ali, R., et al. (2017) · BMC Microbiology · doi:10.1186/s12866-017-1101-8 · Cited by 298
Tan, B., Ng, C., Nshimyimana, J. P., Loh, L. L., Gin, K. Y.-H., & Thompson, J. R. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.01027 · Cited by 297
Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis
Clooney, A. G., Fouhy, F., Sleator, R. D., O’ Driscoll, A., Stanton, C., Cotter, P. D., & Claesson, M. J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0148028 · Cited by 270
Metadata
- Authors
- Sinclair, L., Osman, O. A., Bertilsson, S., & Eiler, A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-02-03
- Volume
- 10
- Issue
- 2
- Pages
- e0116955
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2094492452
- Cited by
- 364
- References
- 48
- FWCI
- 30.02
- Type
- journal article
- Language
- en
- Open access
- gold
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