The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Accuracy and quality of massively parallel DNA pyrosequencing
Open accessAbstract
Background: Massively parallel pyrosequencing systems have increased the efficiency of DNA sequencing, although the published per-base accuracy of a Roche GS20 is only 96%. In genome projects, highly redundant consensus assemblies can compensate for sequencing errors. In contrast, studies of microbial diversity that catalogue differences between PCR amplicons of ribosomal RNA genes (rDNA) or other conserved gene families cannot take advantage of consensus assemblies to detect and minimize incorrect base calls. Results: We performed an empirical study of the per-base error rate for the Roche GS20 system using sequences of the V6 hypervariable region from cloned microbial ribosomal DNA (tag sequencing). We calculated a 99.5% accuracy rate in unassembled sequences, and identified several factors that can be used to remove a small percentage of low-quality reads, improving the accuracy to 99.75% or better. Conclusion: By using objective criteria to eliminate low quality data, the quality of individual GS20 sequence reads in molecular ecological applications can surpass the accuracy of traditional capillary methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Base-Calling of Automated Sequencer Traces Using Phred. II. Error Probabilities
Ewing, B., & Green, P. (1998) · Genome Research · doi:10.1101/gr.8.3.186 · Cited by 5,439
Cited by
Metagenomic biomarker discovery and explanation
Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011) · Genome Biology · doi:10.1186/gb-2011-12-6-r60 · Cited by 16,192
Life in the “plastisphere”: microbial communities on plastic marine debris
Zettler, E. R., Mincer, T. J., & Amaral-Zettler, L. A. (2013) · Environmental Science & Technology · doi:10.1021/es401288x · Cited by 3,368
A framework for human microbiome research
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11209 · Cited by 2,650
Dethlefsen, L., Huse, S., Sogin, M. L., & Relman, D. A. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060280 · Cited by 2,361
Self-citationReducing 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
Ironing out the wrinkles in the rare biosphere through improved OTU clustering
Huse, S. M., Welch, D. M., Morrison, H. G., & Sogin, M. L. (2010) · Environmental Microbiology · doi:10.1111/j.1462-2920.2010.02193.x · Cited by 1,346
Self-citationError-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex
Hamady, M., Walker, J. J., Harris, J. K., Gold, N. J., & Knight, R. (2008) · Nature Methods · doi:10.1038/nmeth.1184 · Cited by 1,310
Substantial biases in ultra-short read data sets from high-throughput DNA sequencing
Dohm, J. C., Lottaz, C., Borodina, T., & Himmelbauer, H. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn425 · Cited by 1,088
Dowd, S. E., Callaway, T. R., Wolcott, R. D., Sun, Y., McKeehan, T., Hagevoort, R. G., & Edrington, T. S. (2008) · BMC Microbiology · doi:10.1186/1471-2180-8-125 · Cited by 1,121
“The use of rapid sequencing technologies combined with molecular methods is becoming a gold standard for evaluating the microbiomes of animals [21-25].”
MethodCharacterizing and measuring bias in sequence data
Ross, M. G., Russ, C., Costello, M., Hollinger, A., Lennon, N. J., Hegarty, R., Nusbaum, C., & Jaffe, D. B. (2013) · Genome Biology · doi:10.1186/gb-2013-14-5-r51 · Cited by 909
Pyrosequencing analysis of the oral microflora of healthy adults
Keijser, B. J. F., Zaura, E., Huse, S. M., van der Vossen, J. M. B. M., Schuren, F. H. J., Montijn, R. C., ten Cate, J. M., & Crielaard, W. (2008) · Journal of Dental Research · doi:10.1177/154405910808701104 · Cited by 669
Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems
Zinger, L., Amaral-Zettler, L. A., Fuhrman, J. A., Horner-Devine, M. C., Huse, S. M., Welch, D. B. M., Martiny, J. B. H., Sogin, M., Boetius, A., & Ramette, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024570 · Cited by 643
Williams, B. L., Hornig, M., Buie, T., Bauman, M. L., Cho Paik, M., Wick, I., Bennett, A., Jabado, O., Hirschberg, D. L., & Lipkin, W. I. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024585 · Cited by 492
Parchman, T. L., Geist, K. S., Grahnen, J. A., Benkman, C. W., & Buerkle, C. A. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-180 · Cited by 446
Kong, L.-C., Tap, J., Aron-Wisnewsky, J., Pelloux, V., Basdevant, A., Bouillot, J.-L., Zucker, J.-D., Doré, J., & Clément, K. (2013) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.058743 · Cited by 416
Dalloul, R. A., Long, J. A., Zimin, A. V., Aslam, L., Beal, K., Ann Blomberg, L., Bouffard, P., Burt, D. W., Crasta, O., Crooijmans, R. P. M. A., et al. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000475 · Cited by 416
Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults
Tap, J., Furet, J., Bensaada, M., Philippe, C., Roth, H., Rabot, S., Lakhdari, O., Lombard, V., Henrissat, B., Corthier, G., et al. (2015) · Environmental Microbiology · doi:10.1111/1462-2920.13006 · Cited by 397
The Microbiome and Human Biology
Knight, R., Callewaert, C., Marotz, C., Hyde, E. R., Debelius, J. W., McDonald, D., & Sogin, M. L. (2017) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022438 · Cited by 373
Self-citationCommunity structures of fecal bacteria in cattle from different animal feeding operations
Shanks, O. C., Kelty, C. A., Archibeque, S., Jenkins, M., Newton, R. J., McLellan, S. L., Huse, S. M., & Sogin, M. L. (2011) · Applied and Environmental Microbiology · doi:10.1128/aem.02988-10 · Cited by 359
Self-citationOligotyping analysis of the human oral microbiome
Eren, A. M., Borisy, G. G., Huse, S. M., & Mark Welch, J. L. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1409644111 · Cited by 347
Self-citationGut microbial colonisation in premature neonates predicts neonatal sepsis
Madan, J. C., Salari, R. C., Saxena, D., Davidson, L., O'Toole, G. A., Moore, J. H., Sogin, M. L., Foster, J. A., Edwards, W. H., Palumbo, P., et al. (2012) · Archives of Disease in Childhood - Fetal and Neonatal Edition · doi:10.1136/fetalneonatal-2011-301373 · Cited by 340
Self-citationMetabarcoding vs. morphological identification to assess diatom diversity in environmental studies
Zimmermann, J., Glöckner, G., Jahn, R., Enke, N., & Gemeinholzer, B. (2015) · Molecular Ecology Resources · doi:10.1111/1755-0998.12336 · Cited by 317
Evaluation 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
Shining a Light on Dark Sequencing: Characterising Errors in Ion Torrent PGM Data
Bragg, L. M., Stone, G., Butler, M. K., Hugenholtz, P., & Tyson, G. W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003031 · Cited by 261
Metagenomic Sequencing of an In Vitro-Simulated Microbial Community
Morgan, J. L., Darling, A. E., & Eisen, J. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010209 · Cited by 248
Global characterization of Artemisia annua glandular trichome transcriptome using 454 pyrosequencing
Wang, W., Wang, Y., Zhang, Q., Qi, Y., & Guo, D. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-465 · Cited by 224
AbLang: an antibody language model for completing antibody sequences
Olsen, T. H., Moal, I. H., & Deane, C. M. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac046 · Cited by 198
Variation in Raw Milk Microbiota Throughout 12 Months and the Impact of Weather Conditions
Li, N., Wang, Y., You, C., Ren, J., Chen, W., Zheng, H., & Liu, Z. (2018) · Scientific Reports · doi:10.1038/s41598-018-20862-8 · Cited by 187
Forensic STR analysis using massive parallel sequencing
Van Neste, C., Van Nieuwerburgh, F., Van Hoofstat, D., & Deforce, D. (2012) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2012.03.004 · Cited by 102
Synthetic antibodies designed on natural sequence landscapes
Zhai, W., Glanville, J., Fuhrmann, M., Mei, L., Ni, I., Sundar, P. D., Van Blarcom, T., Abdiche, Y., Lindquist, K., Strohner, R., et al. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.07.018 · Cited by 93
Aging affects B‐cell antigen receptor repertoire diversity in primary and secondary lymphoid tissues
Tabibian‐Keissar, H., Hazanov, L., Schiby, G., Rosenthal, N., Rakovsky, A., Michaeli, M., Shahaf, G. L., Pickman, Y., Rosenblatt, K., Melamed, D., et al. (2016) · European Journal of Immunology · doi:10.1002/eji.201545586 · Cited by 84
Next-generation sequencing methodologies to detect low-frequency mutations: “Catch me if you can”
Menon, V., & Brash, D. E. (2023) · Mutation Research - Reviews in Mutation Research · doi:10.1016/j.mrrev.2023.108471 · Cited by 48
Regulation of Huntingtin Gene Expression by miRNA-137, -214, -148a, and Their Respective isomiRs
Kozlowska, E., Krzyzosiak, W., & Koscianska, E. (2013) · International Journal of Molecular Sciences · doi:10.3390/ijms140816999 · Cited by 45
Microbiome depiction through user-adapted bioinformatic pipelines and parameters
Nayman, E. I., Schwartz, B. A., Polanco, F. C., Firek, A. K., Gumabong, A. C., Hofstee, N. J., Narasimhan, G., Cickovski, T., & Mathee, K. (2023) · Journal of Medical Microbiology · doi:10.1099/jmm.0.001756 · Cited by 6
Metadata
- Authors
- Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L., & Welch, D. M.
- Venue
- Genome Biology
- Published
- 2007-07-20
- Volume
- 8
- Issue
- 7
- Pages
- R143-R143
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2130077037
- Cited by
- 1,282
- References
- 2
- FWCI
- 27.18
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
