Evidence map›Paper›PMID 38368425›Full record

ArticleNPJ genomic medicine2024

Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes.

Wataru Nakamura, Makoto Hirata, Satoyo Oda, Kenichi Chiba, Ai Okada, Raúl Nicolás Mateos, Masahiro Sugawa, Naoko Iida, Mineko Ushiama, Noriko Tanabe and 11 more

Open access · goldAbstract read
In one paragraph

Article in NPJ genomic medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
18.7field-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

30 citing papers in PubMed, 34 citations in OpenAlex.

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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

21 authors at 10 institutions in 2 countries.

Wataru Nakamura *Division of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Makoto Hirata *Division of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.
Satoyo OdaDivision of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.
Kenichi ChibaDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Ai OkadaDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Raúl Nicolás MateosDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Masahiro SugawaDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Naoko IidaDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan.
Mineko UshiamaDivision of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.
Noriko TanabeDivision of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0002-8240-007X
Hiromi SakamotoDivision of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.
Shigeki SekineDivision of Molecular Pathology, National Cancer Center Research Institute, Tokyo, Japan.
Akira HirasawaDepartment of Clinical Genetics and Genomic Medicine, Okayama University Hospital, Okayama, Japan.
Yosuke KawaiGenome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.ORCID http://orcid.org/0000-0003-0666-1224
Katsushi TokunagaGenome Medical Science Project, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.ORCID http://orcid.org/0000-0001-5501-0503
NCBN Controls WGS Consortium
Shin-Ichi TsujimotoDepartment of Pediatrics, Yokohama City University Hospital, Kanagawa, Japan.ORCID http://orcid.org/0000-0001-5738-4138
Norio ShibaDepartment of Pediatrics, Yokohama City University Hospital, Kanagawa, Japan.
Shuichi ItoDepartment of Pediatrics, Yokohama City University Hospital, Kanagawa, Japan.ORCID http://orcid.org/0000-0002-5242-8987
Teruhiko YoshidaDivision of Genetic Medicine and Services, National Cancer Center Hospital, Tokyo, Japan.
Yuichi ShiraishiDivision of Genome Analysis Platform Development, National Cancer Center Research Institute, Tokyo, Japan. yuishira@ncc.go.jp.
National Center for Global Health and Medicine · JPNational Cancer Center Hospital East · JPNational Center of Neurology and Psychiatry · JPBioBank Japan · JPNational Center For Child Health and Development · JPNational Center for Geriatrics and Gerontology · JPNational Cerebral and Cardiovascular Center · JPYokohama City University Hospital · JPNational Cancer Center · USOkayama University Hospital · JP

Funding

Japan Agency for Medical Research and Development (AMED) 19ck0106268h0003Japan Agency for Medical Research and Development (AMED) 20ek0109485h0001
6 · The paper itself

Abstract

Innovations in sequencing technology have led to the discovery of novel mutations that cause inherited diseases. However, many patients with suspected genetic diseases remain undiagnosed. Long-read sequencing technologies are expected to significantly improve the diagnostic rate by overcoming the limitations of short-read sequencing. In addition, Oxford Nanopore Technologies (ONT) offers adaptive sampling and computationally driven target enrichment technology. This enables more affordable intensive analysis of target gene regions compared to standard non-selective long-read sequencing. In this study, we developed an efficient computational workflow for target adaptive sampling long-read sequencing (TAS-LRS) and evaluated it through application to 33 genomes collected from suspected hereditary cancer patients. Our workflow can identify single nucleotide variants with nearly the same accuracy as the short-read platform and elucidate complex forms of structural variations. We also newly identified several SINE-R/VNTR/Alu (SVA) elements affecting the APC gene in two patients with familial adenomatous polyposis, as well as their sites of origin. In addition, we demonstrated that off-target reads from adaptive sampling, which is typically discarded, can be effectively used to accurately genotype common single-nucleotide polymorphisms (SNPs) across the entire genome, enabling the calculation of a polygenic risk score. Furthermore, we identified allele-specific MLH1 promoter hypermethylation in a Lynch syndrome patient. In summary, our workflow with TAS-LRS can simultaneously capture monogenic risk variants including complex structural variations, polygenic background as well as epigenetic alterations, and will be an efficient platform for genetic disease research and diagnosis.

Identifiers

PMID38368425
PMCPMC10874402
OpenAlexW4391899393

What OpenQuestion holds

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LicenceCC BY
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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.