Evidence map›Paper›PMID 35246132›Full record

ArticleBMC medical genomics2022

ECNano: A cost-effective workflow for target enrichment sequencing and accurate variant calling on 4800 clinically significant genes using a single MinION flowcell.

Amy Wing-Sze Leung, Henry Chi-Ming Leung, Chak-Lim Wong, Zhen-Xian Zheng, Wui-Wang Lui, Ho-Ming Luk, Ivan Fai-Man Lo, Ruibang Luo, Tak-Wah Lam

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 31% 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

9 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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  3. Transgene Mapping in Animals: What to Choose?International journal of molecular sciences · 2025
    Review
  4. Review
  5. Article
  6. Review
  7. Calling and Phasing of Single-Nucleotide and Structural Variants of theInternational journal of molecular sciences · 2023
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Amy Wing-Sze Leung *Department of Computer Science, The University of Hong Kong, Hong Kong, China.
Henry Chi-Ming Leung *Department of Computer Science, The University of Hong Kong, Hong Kong, China.
Chak-Lim WongDepartment of Computer Science, The University of Hong Kong, Hong Kong, China.
Zhen-Xian ZhengDepartment of Computer Science, The University of Hong Kong, Hong Kong, China.
Wui-Wang LuiDepartment of Computer Science, The University of Hong Kong, Hong Kong, China.
Ho-Ming LukDepartment of Health, Clinical Genetic Service, Hong Kong, SAR, China.
Ivan Fai-Man LoDepartment of Health, Clinical Genetic Service, Hong Kong, SAR, China.
Ruibang LuoDepartment of Computer Science, The University of Hong Kong, Hong Kong, China. rbluo@cs.hku.hk.ORCID 0000-0001-9711-6533
Tak-Wah LamDepartment of Computer Science, The University of Hong Kong, Hong Kong, China. twlam@cs.hku.hk.
University of Hong Kong · HKDepartment of Health · CNHong Kong University of Science and Technology · HKHong Kong Virtual University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of long-read sequencing using the Oxford Nanopore Technologies (ONT) MinION sequencer is getting more diverse in the medical field. Having a high sequencing error of ONT and limited throughput from a single MinION flowcell, however, limits its applicability for accurate variant detection. Medical exome sequencing (MES) targets clinically significant exon regions, allowing rapid and comprehensive screening of pathogenic variants. By applying MES with MinION sequencing, the technology can achieve a more uniform capture of the target regions, shorter turnaround time, and lower sequencing cost per sample.

methodWe introduced a cost-effective optimized workflow, ECNano, comprising a wet-lab protocol and bioinformatics analysis, for accurate variant detection at 4800 clinically important genes and regions using a single MinION flowcell. The ECNano wet-lab protocol was optimized to perform long-read target enrichment and ONT library preparation to stably generate high-quality MES data with adequate coverage. The subsequent variant-calling workflow, Clair-ensemble, adopted a fast RNN-based variant caller, Clair, and was optimized for target enrichment data. To evaluate its performance and practicality, ECNano was tested on both reference DNA samples and patient samples.

resultsECNano achieved deep on-target depth of coverage (DoC) at average > 100× and > 98% uniformity using one MinION flowcell. For accurate ONT variant calling, the generated reads sufficiently covered 98.9% of pathogenic positions listed in ClinVar, with 98.96% having at least 30× DoC. ECNano obtained an average read length of 1000 bp. The long reads of ECNano also covered the adjacent splice sites well, with 98.5% of positions having ≥ 30× DoC. Clair-ensemble achieved > 99% recall and accuracy for SNV calling. The whole workflow from wet-lab protocol to variant detection was completed within three days.

conclusionWe presented ECNano, an out-of-the-box workflow comprising (1) a wet-lab protocol for ONT target enrichment sequencing and (2) a downstream variant detection workflow, Clair-ensemble. The workflow is cost-effective, with a short turnaround time for high accuracy variant calling in 4800 clinically significant genes and regions using a single MinION flowcell. The long-read exon captured data has potential for further development, promoting the application of long-read sequencing in personalized disease treatment and risk prediction.

Indexed as

High-Throughput Nucleotide SequencingNanoporesCost-Benefit AnalysisHumansSequence Analysis, DNAWorkflowEnsemble variant callingMedical exome sequencingMinION sequencingTarget enrichmentThird-generation sequencing

Identifiers

PMID35246132
PMCPMC8895767
OpenAlexW4221114396

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Registered trials

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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.