Evidence map›Paper›PMID 40659980›Full record

ArticleNature methods2025

Fast, cost-effective and flexible DNA sequencing by roll-to-roll fluidics.

Yanzhe Qin, Stephan A Koehler, Yunyan Ling, Shiqiang Yu, Yongjie Zhang, Jie Luo, Kaijian Chen, Luo Junjie, Junjie Zeng, Haiyang Chu and 24 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Machine-Learning Microfluidic Minute-Scale Microorganism Metrics Monitoring(M6).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    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

34 authors.

Yanzhe QinBGI Research, Shenzhen, China. yanzheqin@126.com.ORCID http://orcid.org/0000-0001-9414-028X
Stephan A KoehlerArtificial Intelligence and Machine Learning Laboratory, Riverside Research Institute, Lexington, MA, USA.
Yunyan LingBGI Research, Shenzhen, China.
Shiqiang YuBGI Research, Shenzhen, China.
Yongjie ZhangNew Cornerstone Science Laboratory, College of Chemistry, Fuzhou University, Fuzhou, China.
Jie LuoBGI Research, Shenzhen, China.
Kaijian ChenBGI Research, Shenzhen, China.
Luo JunjieBGI Research, Shenzhen, China.
Junjie ZengBGI Research, Shenzhen, China.
Haiyang ChuBGI Research, Shenzhen, China.
Fei WangBGI Research, Shenzhen, China.
Wei LiBGI Research, Shenzhen, China.
Dan LiBGI Research, Shenzhen, China.
Xinran YuBGI Research, Shenzhen, China.
Xiangchao WuBGI Research, Shenzhen, China.
Shengming ZhaoMGI Tech, Shenzhen, China.
Hao LuMGI Tech, Shenzhen, China.
Ziqing DengMGI Tech, Shenzhen, China.
Zhijian YangNew Cornerstone Science Laboratory, College of Chemistry, Fuzhou University, Fuzhou, China.
Ruibin MaiMGI Tech, Shenzhen, China.
Zhuo LiuMGI Tech, Shenzhen, China.
Zihua NiuMGI Tech, Shenzhen, China.
Xin HuangBGI Research, Shenzhen, China.
Chengmei XingBGI Research, Shenzhen, China.
Xingcai ZhangSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-7114-1095
Yongyou HuSchool of Environment and Energy, South China University of Technology, Guangzhou, China.
Xinwen PengSchool of Light Industry and Engineering, South China University of Technology, Guangzhou, China.
Xiaoxing LiaoEmergency and Disaster Medical Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Xiangmeng QuSchool of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China.
Yiming GongBGI Research, Shenzhen, China.
Qiushui ChenNew Cornerstone Science Laboratory, College of Chemistry, Fuzhou University, Fuzhou, China. qchen@fzu.edu.cn.ORCID http://orcid.org/0000-0002-3039-2098
Thomas M HermansIMDEA Nanoscience, Madrid, Spain. thomas.hermans@imdea.org.
Wenwei ZhangBGI Research, Shenzhen, China. zhangww@genomics.cn.ORCID http://orcid.org/0000-0001-9220-2467
Huanghao YangNew Cornerstone Science Laboratory, College of Chemistry, Fuzhou University, Fuzhou, China. hhyang@fzu.edu.cn.ORCID http://orcid.org/0000-0001-5894-0909

Funding

National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund) 2019M650215National Science Foundation of China | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund) 22027805, 22334004, and 22421002
6 · The paper itself

Abstract

Next-generation sequencing (NGS) technologies have achieved remarkable success in both biological research and clinical applications. However, in recent years, performance improvements have slowed due to fundamental limitations imposed by Poiseuille fluid dynamics in flow cells, which we overcome using Couette flow. Here we show NGS by roll-to-roll fluidics (r2r-fl), a cost-effective approach compatible with flexible biochip sizes. r2r-fl is a practical implementation of plane Couette flow, with up to 85-fold lower reagent consumption (US$0.16 per gigabase pair), rinsing times under 2 s and a reduction in paired-end 100-base pair sequencing turnaround from days to less than 12 h. The method maintains over 99.9% precision and 99.3% sensitivity of single nucleotide polymorphisms in the human genome, 99.9% mapping rate for Escherichia coli, and minimal nucleotide substitutions, deletions or insertions in the severe acute respiratory syndrome coronavirus 2 alpha strain. By lowering cost and time, r2r-fl enables rapid, scalable NGS for pathogen detection, cancer diagnostics and genetic disease profiling.

Indexed as

High-Throughput Nucleotide SequencingSequence Analysis, DNACost-Benefit AnalysisCOVID-19Escherichia coliGenome, HumanHumansPolymorphism, Single NucleotideSARS-CoV-2

Identifiers

What OpenQuestion holds

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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.