Evidence map›Paper›PMID 26402642›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2016

Capture-based high-coverage NGS: a powerful tool to uncover a wide spectrum of mutation types.

Jing Wang, Hui Yu, Victor Wei Zhang, Xia Tian, Yanming Feng, Guoli Wang, Elizabeth Gorman, Hao Wang, Richard E Lutz, Eric S Schmitt and 2 more

Abstract read
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In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Variant Type X91Journal of clinical immunology · 2022
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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

12 authors.

Jing WangDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.ORCID 0000-0001-9178-7731
Hui YuBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Victor Wei ZhangDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
Xia TianBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Yanming FengBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Guoli WangBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Elizabeth GormanBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Hao WangBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Richard E LutzMunroe-Meyer Institute, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Eric S SchmittBaylor Miraca Genetics Laboratories, Houston, Texas, USA.
Sandra PeacockBaylor Miraca Genetics Laboratories, Houston, Texas, USA.ORCID 0000-0001-8119-3745
Lee-Jun WongDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNext-generation sequencing (NGS) has been widely applied to clinical diagnosis. Target-gene capture followed by deep sequencing provides unbiased enrichment of the target sequences, which not only accurately detects single-nucleotide variations (SNVs) and small insertion/deletions (indels) but also provides the opportunity for the identification of exonic copy-number variants (CNVs) and large genomic rearrangements.

methodCapture NGS has the ability to easily detect SNVs and small indels. However, genomic changes involving exonic deletions/duplications and chromosomal rearrangements require more careful analysis of captured NGS data. Misaligned raw sequence reads may be more than just bad data. Some mutations that are difficult to detect are filtered by the preset analytical parameters. "Loose" filtering and alignment conditions were used for thorough analysis of the misaligned NGS reads. Additionally, using an in-house algorithm, NGS coverage depth was thoroughly analyzed to detect CNVs.

resultsUsing real examples, this report underscores the importance of the accessibility to raw sequence data and manual review of suspicious sequence regions to avoid false-negative results in the clinical application of NGS. Assessment of the NGS raw data generated by the use of loose filtering parameters identified several sequence aberrations, including large indels and genomic rearrangements. Furthermore, NGS coverage depth analysis identified homozygous and heterozygous deletions involving single or multiple exons.

conclusionOur results demonstrate the power of deep NGS in the simultaneous detection of point mutations and intragenic exonic deletion in one comprehensive step.Genet Med 18 5, 513-521.

Indexed as

AlgorithmsDNA Copy Number VariationsExonsGenetic Diseases, InbornHeterozygoteHigh-Throughput Nucleotide SequencingHomozygoteHumansINDEL MutationPolymorphism, Single NucleotideSequence Deletion

Identifiers

PMID26402642

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.