Evidence map›Paper›PMID 25686389›Full record

ArticleNature methods2015

Improved data analysis for the MinION nanopore sequencer.

Miten Jain, Ian T Fiddes, Karen H Miga, Hugh E Olsen, Benedict Paten, Mark Akeson

Abstract read
In one paragraph

Article in Nature methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 292 papers.

0numbers the graph read from it
0cells of the map it votes in
292citing papers in PubMed
49.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

292 citing papers in PubMed, 686 citations in OpenAlex.

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  12. Detecting Foldback Artifacts in Long-reads.bioRxiv : the preprint server for biology · 2025
    Article
  13. Review
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  19. Nanopore sequencing for precise detection ofJournal of clinical microbiology · 2025
    Article
  20. Study on the early diagnostic value of nanopore sequencing in alveolar lavage fluid smear-negative pulmonary tuberculosis.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025
    Article

232 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Miten Jain1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
Ian T Fiddes1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
Karen H Miga1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
Hugh E Olsen1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
Benedict Paten1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
Mark Akeson1] UC Santa Cruz Genomics Institute, Santa Cruz, California, USA. [2] Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.
University of California, Santa Cruz · US

Funding

Training SubprojectU54HG007990 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, PATTERSON, DAVID · 2014 to 2018
$14.8M
Optimization of Processive Enzymes for DNA Sequencing using NanoporesR01HG006321 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI AKESON, MARK A · 2011 to 2013
$3.6M
Optimization of Nanopore Genomic DNA SequencingR01HG007827 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI AKESON, MARK A · 2014 to 2016
$2.2M
NHGRI NIH HHS HG006321NHGRI NIH HHS HG007827NHGRI NIH HHS R01 HG006321NHGRI NIH HHS R01 HG007827NHGRI NIH HHS U54 HG007990NHGRI NIH HHS U54HG007990
6 · The paper itself

Abstract

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.

Indexed as

NanoporesAlgorithmsGene DosageHigh-Throughput Nucleotide SequencingHumansNeoplasms

Identifiers

PMID25686389
PMCPMC4907500
OpenAlexW2007772656

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.