Evidence map›Paper›PMID 27579173›Full record

ArticleHuman genome variation2016

Challenges in detecting genomic copy number aberrations using next-generation sequencing data and the eXome Hidden Markov Model: a clinical exome-first diagnostic approach.

Toshiyuki Yamamoto, Keiko Shimojima, Yumiko Ondo, Katsumi Imai, Pin Fee Chong, Ryutaro Kira, Mitsuhiro Amemiya, Akira Saito, Nobuhiko Okamoto

Abstract read
In one paragraph

Article in Human genome variation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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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

24 citing papers in PubMed.

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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

9 authors.

Toshiyuki YamamotoTokyo Women's Medical University Institute for Integrated Medical Sciences , Tokyo, Japan.ORCID 0000-0002-9484-3505
Keiko ShimojimaTokyo Women's Medical University Institute for Integrated Medical Sciences , Tokyo, Japan.
Yumiko OndoTokyo Women's Medical University Institute for Integrated Medical Sciences , Tokyo, Japan.
Katsumi ImaiNational Epilepsy Center, NHO Shizuoka Institute of Epilepsy and Neurological Disorders , Shizuoka, Japan.
Pin Fee ChongDepartment of Pediatric Neurology, Fukuoka Children's Hospital , Fukuoka, Japan.
Ryutaro KiraDepartment of Pediatric Neurology, Fukuoka Children's Hospital , Fukuoka, Japan.
Mitsuhiro AmemiyaDivision of Biomedical Informatics, StaGen Co., Ltd. , Tokyo, Japan.
Akira SaitoDivision of Biomedical Informatics, StaGen Co., Ltd. , Tokyo, Japan.
Nobuhiko OkamotoDepartment of Medical Genetics, Osaka Medical Center and Research Institute for Maternal and Child Health , Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation sequencing (NGS) is widely used for the detection of disease-causing nucleotide variants. The challenges associated with detecting copy number variants (CNVs) using NGS analysis have been reported previously. Disease-related exome panels such as Illumina TruSight One are more cost-effective than whole-exome sequencing (WES) because of their selective target regions (~21% of the WES). In this study, CNVs were analyzed using data extracted through a disease-related exome panel analysis and the eXome Hidden Markov Model (XHMM). Samples from 61 patients with undiagnosed developmental delays and 52 healthy parents were included in this study. In the preliminary study to validate the constructed XHMM system (microarray-first approach), 34 patients who had previously been analyzed by chromosomal microarray testing were used. Among the five CNVs larger than 200 kb that were considered as non-pathogenic CNVs and were used as positive controls, four CNVs was successfully detected. The system was subsequently used to analyze different samples from 27 patients (NGS-first approach); 2 of these patients were successfully diagnosed as having pathogenic CNVs (an unbalanced translocation der(5)t(5;14) and a 16p11.2 duplication). These diagnoses were re-confirmed by chromosomal microarray testing and/or fluorescence in situ hybridization. The NGS-first approach generated no false-negative or false-positive results for pathogenic CNVs, indicating its high sensitivity and specificity in detecting pathogenic CNVs. The results of this study show the possible clinical utility of pathogenic CNV screening using disease-related exome panel analysis and XHMM.

Identifiers

PMID27579173
PMCPMC4989049

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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.