The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improved data analysis for the MinION nanopore sequencer
Open accessAbstract
Speed, single-base sensitivity and long read lengths make nanopores a promising technology for high-throughput sequencing. We evaluated and optimized the performance of the MinION nanopore sequencer using M13 genomic DNA and used expectation maximization to obtain robust maximum-likelihood estimates for insertion, deletion and substitution error rates (4.9%, 7.8% and 5.1%, respectively). Over 99% of high-quality 2D MinION reads mapped to the reference at a mean identity of 85%. We present a single-nucleotide-variant detection tool that uses maximum-likelihood parameter estimates and marginalization over many possible read alignments to achieve precision and recall of up to 99%. By pairing our high-confidence alignment strategy with long MinION reads, we resolved the copy number for a cancer-testis gene family (CT47) within an unresolved region of human chromosome Xq24.
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
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine with nanopore MspA
Laszlo, A. H., Derrington, I. M., Brinkerhoff, H., Langford, K. W., Nova, I. C., Samson, J. M., Bartlett, J. J., Pavlenok, M., & Gundlach, J. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310240110 · Cited by 321
Cited by
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Nanopore sequencing and assembly of a human genome with ultra-long reads
Jain, M., Koren, S., Miga, K. H., Quick, J., Rand, A. C., Sasani, T. A., Tyson, J. R., Beggs, A. D., Dilthey, A. T., Fiddes, I. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4060 · Cited by 1,892
Self-citationThe Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community
Jain, M., Olsen, H. E., Paten, B., & Akeson, M. (2016) · Genome Biology · doi:10.1186/s13059-016-1103-0 · Cited by 1,588
Self-citationCentrifuge: rapid and sensitive classification of metagenomic sequences
Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016) · Genome Research · doi:10.1101/gr.210641.116 · Cited by 1,438
Three decades of nanopore sequencing
Deamer, D., Akeson, M., & Branton, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3423 · Cited by 1,292
Self-citationThe Ensembl gene annotation system
Aken, B. L., Ayling, S., Barrell, D., Clarke, L., Curwen, V., Fairley, S., Fernandez Banet, J., Billis, K., García Girón, C., Hourlier, T., et al. (2016) · Database · doi:10.1093/database/baw093 · Cited by 1,086
The Third Revolution in Sequencing Technology
van Dijk, E. L., Jaszczyszyn, Y., Naquin, D., & Thermes, C. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.05.008 · Cited by 1,057
Human-Specific NOTCH2NL Genes Affect Notch Signaling and Cortical Neurogenesis
Fiddes, I. T., Lodewijk, G. A., Mooring, M., Bosworth, C. M., Ewing, A. D., Mantalas, G. L., Novak, A. M., van den Bout, A., Bishara, A., Rosenkrantz, J. L., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.051 · Cited by 595
Self-citationThe 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
Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes
Shafin, K., Pesout, T., Lorig-Roach, R., Haukness, M., Olsen, H. E., Bosworth, C., Armstrong, J., Tigyi, K., Maurer, N., Koren, S., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0503-6 · Cited by 567
Self-citationGreninger, A. L., Naccache, S. N., Federman, S., Yu, G., Mbala, P., Bres, V., Stryke, D., Bouquet, J., Somasekar, S., Linnen, J. M., et al. (2015) · Genome Medicine · doi:10.1186/s13073-015-0220-9 · Cited by 558
Mapping DNA methylation with high-throughput nanopore sequencing
Rand, A. C., Jain, M., Eizenga, J. M., Musselman-Brown, A., Olsen, H. E., Akeson, M., & Paten, B. (2017) · Nature Methods · doi:10.1038/nmeth.4189 · Cited by 507
Self-citationByrne, A., Beaudin, A. E., Olsen, H. E., Jain, M., Cole, C., Palmer, T., DuBois, R. M., Forsberg, E. C., Akeson, M., & Vollmers, C. (2017) · Nature Communications · doi:10.1038/ncomms16027 · Cited by 379
Self-citationGenome graphs and the evolution of genome inference
Paten, B., Novak, A. M., Eizenga, J. M., & Garrison, E. (2017) · Genome Research · doi:10.1101/gr.214155.116 · Cited by 351
Self-citationBulkVis: a graphical viewer for Oxford nanopore bulk FAST5 files
Payne, A., Holmes, N., Rakyan, V., & Loose, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty841 · Cited by 326
Errors in long-read assemblies can critically affect protein prediction
Watson, M., & Warr, A. (2019) · Nature Biotechnology · doi:10.1038/s41587-018-0004-z · Cited by 224
A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data
Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00023 · Cited by 199
“At launch, the MinION read length was approximately 6–8 kb (Jain et al., 2015; Loman and Watson, 2015); however, Urban et al. (2015) published a lab protocol which could improve the MinION reads length producing many reads even longer than 100 kb.”
MethodTransposable element detection from whole genome sequence data
Ewing, A. D. (2015) · Mobile DNA · doi:10.1186/s13100-015-0055-3 · Cited by 155
A 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
MinION Analysis and Reference Consortium: Phase 2 data release and analysis of R9.0 chemistry
Jain, M., Tyson, J. R., Loose, M., Ip, C. L. C., Eccles, D. A., O'Grady, J., Malla, S., Leggett, R. M., Wallerman, O., Jansen, H. J., et al. (2017) · F1000Research · doi:10.12688/f1000research.11354.1 · Cited by 140
Self-citationAdvances in genomics for adapting crops to climate change
Scheben, A., Yuan, Y., & Edwards, D. (2016) · Current Plant Biology · doi:10.1016/j.cpb.2016.09.001 · Cited by 117
Next-Generation Sequencing: Advantages, Disadvantages, and Future
Ari, Ş., & Arikan, M. (2016) · Plant Omics: Trends and Applications · doi:10.1007/978-3-319-31703-8_5 · Cited by 84
Metadata
- Authors
- Jain, M., Fiddes, I. T., Miga, K. H., Olsen, H. E., Paten, B., & Akeson, M.
- Venue
- Nature Methods
- Published
- 2015-02-16
- Volume
- 12
- Issue
- 4
- Pages
- 351-356
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2007772656
- Cited by
- 639
- References
- 3
- FWCI
- 37.57
- 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
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.
