Evidence map›Paper›PMID 38015437›Full record

ArticleNucleic acids research2024

Exome-wide benchmark of difficult-to-sequence regions using short-read next-generation DNA sequencing.

Atsushi Hijikata, Mikita Suyama, Shingo Kikugawa, Ryo Matoba, Takuya Naruto, Yumi Enomoto, Kenji Kurosawa, Naoki Harada, Kumiko Yanagi, Tadashi Kaname and 17 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. [Genetic diagnostics].Pathologie (Heidelberg, Germany) · 2026
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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

27 authors.

Atsushi HijikataLaboratory of Computational Genomics, School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, Hachioji, Tokyo 192-0392, Japan.ORCID 0000-0003-4297-8368
Mikita SuyamaDivision of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka 812-8582, Japan.ORCID 0000-0001-9526-3193
Shingo KikugawaDNA Chip Research Inc., Minato-ku, Tokyo 105-0022, Japan.
Ryo MatobaDNA Chip Research Inc., Minato-ku, Tokyo 105-0022, Japan.
Takuya NarutoClinical Research Institute, Kanagawa Children's Medical Center, Minami-ku, Yokohama, Kanagawa 232-0066, Japan.ORCID 0000-0002-5590-8126
Yumi EnomotoClinical Research Institute, Kanagawa Children's Medical Center, Minami-ku, Yokohama, Kanagawa 232-0066, Japan.
Kenji KurosawaClinical Research Institute, Kanagawa Children's Medical Center, Minami-ku, Yokohama, Kanagawa 232-0066, Japan.
Naoki HaradaDepartment of Fundamental Cell Technology, Center for iPS Cell Research and Application (CiRA), Kyoto University, Sakyo-ku, Kyoto 606-8507, Japan.
Kumiko YanagiDepartment of Genome Medicine, National Center for Child Health and Development, Setagaya-ku, Tokyo 157-8535, Japan.
Tadashi KanameDepartment of Genome Medicine, National Center for Child Health and Development, Setagaya-ku, Tokyo 157-8535, Japan.ORCID 0000-0003-0281-9610
Keisuke MiyakoDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Masaki TakazawaDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Hideo SasaiDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Junichi HosokawaDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Sakae ItogaDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.
Tomomi YamaguchiDepartment of Medical Genetics, Shinshu University School of Medicine, Matsumoto, Nagano 390-8621, Japan.
Tomoki KoshoDepartment of Medical Genetics, Shinshu University School of Medicine, Matsumoto, Nagano 390-8621, Japan.ORCID 0000-0002-8344-7507
Keiko MatsubaraDivision of Collaborative Research, National Research Institute for Child Health and Development, Setagaya-ku, Tokyo 157-8535, Japan.
Yoko KurokiDepartment of Genome Medicine, National Center for Child Health and Development, Setagaya-ku, Tokyo 157-8535, Japan.
Maki FukamiDepartment of Molecular Endocrinology, National Research Institute for Child Health and Development, Setagaya-ku, Tokyo 157-8535, Japan.ORCID 0000-0001-9971-4035
Kaori AdachiOrganization for Research Initiative and Promotion, Tottori University, Yonago, Tottori 680-8550, Japan.
Eiji NanbaOrganization for Research Initiative and Promotion, Tottori University, Yonago, Tottori 680-8550, Japan.
Naomi TsuchidaDepartment of Human Genetics, Yokohama City University Graduate School of Medicine, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan.
Yuri UchiyamaDepartment of Human Genetics, Yokohama City University Graduate School of Medicine, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan.
Naomichi MatsumotoDepartment of Human Genetics, Yokohama City University Graduate School of Medicine, Kanazawa-ku, Yokohama, Kanagawa 236-0027, Japan.ORCID 0000-0001-9846-6500
Kunihiro NishimuraXcoo, Inc., Bunkyo-ku, Tokyo 113-0033, Japan.
Osamu OharaDepartment of Applied Genomics, Kazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.ORCID 0000-0002-3328-9571

Funding

Kazusa DNA Research InstituteMedical Research Centre Initiative for High Depth Omics at Kyushu University
6 · The paper itself

Abstract

Next-generation DNA sequencing (NGS) in short-read mode has recently been used for genetic testing in various clinical settings. NGS data accuracy is crucial in clinical settings, and several reports regarding quality control of NGS data, primarily focusing on establishing NGS sequence read accuracy, have been published thus far. Variant calling is another critical source of NGS errors that remains unexplored at the single-nucleotide level despite its established significance. In this study, we used a machine-learning-based method to establish an exome-wide benchmark of difficult-to-sequence regions at the nucleotide-residue resolution using 10 genome sequence features based on real-world NGS data accumulated in The Genome Aggregation Database (gnomAD) of the human reference genome sequence (GRCh38/hg38). The newly acquired metric, designated the 'UNMET score,' along with additional lines of structural information from the human genome, allowed us to assess the sequencing challenges within the exonic region of interest using conventional short-read NGS. Thus, the UNMET score could provide a basis for addressing potential sequential errors in protein-coding exons of the human reference genome sequence GRCh38/hg38 in clinical sequencing.

Indexed as

ExomeHigh-Throughput Nucleotide SequencingSequence Analysis, DNADNAHumansDNA

Identifiers

PMID38015437
PMCPMC10783491

What OpenQuestion holds

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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.