Evidence map›Paper›PMID 26764020›Full record

ArticleBMC genomics2016

cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data.

Pubudu Saneth Samarakoon, Hanne Sørmo Sorte, Asbjørg Stray-Pedersen, Olaug Kristin Rødningen, Torbjørn Rognes, Robert Lyle

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Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  13. Detection of genomic rearrangements from targeted resequencing data in Parkinson's disease patients.Movement disorders : official journal of the Movement Disorder Society · 2017
    Article
  14. A potential founder variant inMolecular genetics & genomic medicine · 2016
    Article
  15. 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

6 authors.

Pubudu Saneth SamarakoonDepartment of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway. saneth.samarakoon@gmail.com.
Hanne Sørmo SorteDepartment of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway. h.s.sorte@medisin.uio.no.
Asbjørg Stray-PedersenNorwegian National Newborn Screening, Oslo University Hospital, Oslo, Norway. astrayped@gmail.com.
Olaug Kristin RødningenDepartment of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway. olaugrod@gmail.com.
Torbjørn RognesDepartment of Informatics, University of Oslo, Oslo, Norway. torognes@ifi.uio.no.
Robert LyleDepartment of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway. robert.lyle@medisin.uio.no.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies.

resultsWe have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families.

conclusionsIn summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data.

Indexed as

High-Throughput Nucleotide SequencingAlgorithmsDNA Copy Number VariationsExomeExonsFemaleHumansMaleMolecular Sequence AnnotationMutation

Identifiers

PMID26764020
PMCPMC4712464

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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.