The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing
Open accessAbstract
High-throughput amplicon sequencing of large genomic regions remains challenging for short-read technologies. Here, we report a high-throughput amplicon sequencing approach combining unique molecular identifiers (UMIs) with Oxford Nanopore Technologies (ONT) or Pacific Biosciences circular consensus sequencing, yielding high-accuracy single-molecule consensus sequences of large genomic regions. We applied our approach to sequence ribosomal RNA operon amplicons (~4,500 bp) and genomic sequences (>10,000 bp) of reference microbial communities in which we observed a chimera rate <0.02%. To reach a mean UMI consensus error rate <0.01%, a UMI read coverage of 15× (ONT R10.3), 25× (ONT R9.4.1) and 3× (Pacific Biosciences circular consensus sequencing) is needed, which provides a mean error rate of 0.0042%, 0.0041% and 0.0007%, respectively. This work presents a sequencing strategy based on unique molecular identifiers that improves long-read consensus sequence accuracy of targeted amplicons as well as shotgun whole-genome fragments.
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
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0209-9 · Cited by 23,742
Self-citationSearch and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,826
Minimap2: Pairwise alignment for nucleotide sequences
Li, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty191 · Cited by 14,807
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201
VSEARCH: a versatile open source tool for metagenomics
(2016) · doi:10.7717/peerj.2584 · Cited by 10,773
Cited by
Next-generation sequencing technologies: An overview
Hu, T., Chitnis, N., Monos, D., & Dinh, A. (2021) · Human Immunology · doi:10.1016/j.humimm.2021.02.012 · Cited by 949
Curry, K. D., Wang, Q., Nute, M. G., Tyshaieva, A., Reeves, E., Soriano, S., Wu, Q., Graeber, E., Finzer, P., Mendling, W., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01520-4 · Cited by 380
Continuous evolution of user-defined genes at 1 million times the genomic mutation rate
Rix, G., Williams, R. L., Hu, V. J., Spinner, A., Pisera, A. (., Marks, D. S., & Liu, C. C. (2024) · Science · doi:10.1126/science.adm9073 · Cited by 71
Integrated molecular approaches for fermented food microbiome research
Walsh, A. M., Leech, J., Huttenhower, C., Delhomme-Nguyen, H., Crispie, F., Chervaux, C., & Cotter, P. D. (2023) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuad001 · Cited by 56
Prediction of designer-recombinases for DNA editing with generative deep learning
Schmitt, L. T., Paszkowski-Rogacz, M., Jug, F., & Buchholz, F. (2022) · Nature Communications · doi:10.1038/s41467-022-35614-6 · Cited by 45
Metadata
- Authors
- Karst, S. M., Ziels, R. M., Kirkegaard, R. H., Sørensen, E. A., McDonald, D., Zhu, Q., Knight, R., & Albertsen, M.
- Venue
- Nature Methods
- Published
- 2021-01-11
- Volume
- 18
- Issue
- 2
- Pages
- 165-169
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W3120817351
- Cited by
- 423
- References
- 62
- FWCI
- 19.52
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. https://app.cochat.ai/papers/10.1038/s41592-020-01041-y
- The paper
- Karst, S. M., Ziels, R. M., Kirkegaard, R. H., Sørensen, E. A., McDonald, D., Zhu, Q., Knight, R., & Albertsen, M. (2021). High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nature Methods. https://doi.org/10.1038/s41592-020-01041-y
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.
