Cristin-resultat-ID: 1936224
Sist endret: 14. februar 2022, 11:29
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Bidragsytere:
  • Ashish Kumar Singh
  • Maren Fridtjofsen Olsen
  • Liss Ane Lavik
  • Trine Vold
  • Finn Drabløs og
  • Wenche Sjursen

Tidsskrift

BMC Medical Genomics
ISSN 1755-8794
e-ISSN 1755-8794
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Volum: 14
Hefte: 1
Sider: 1 - 12
Artikkelnummer: 214
Open Access

Importkilder

Scopus-ID: 2-s2.0-85113952282

Beskrivelse Beskrivelse

Tittel

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

Sammendrag

Background Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples. Results Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests. Conclusions With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Bidragsytere

Ashish Kumar Singh

  • Tilknyttet:
    Forfatter
    ved Institutt for klinisk og molekylær medisin ved Norges teknisk-naturvitenskapelige universitet
  • Tilknyttet:
    Forfatter
    ved Laboratoriemedisinsk klinikk ved St. Olavs Hospital HF
Aktiv cristin-person

Maren Fridtjofsen Olsen

  • Tilknyttet:
    Forfatter
    ved Laboratoriemedisinsk klinikk ved St. Olavs Hospital HF

Liss Ane Lavik

  • Tilknyttet:
    Forfatter
    ved Laboratoriemedisinsk klinikk ved St. Olavs Hospital HF

Trine Vold

  • Tilknyttet:
    Forfatter
    ved Laboratoriemedisinsk klinikk ved St. Olavs Hospital HF

Finn Sverre Drabløs

Bidragsyterens navn vises på dette resultatet som Finn Drabløs
  • Tilknyttet:
    Forfatter
    ved Institutt for klinisk og molekylær medisin ved Norges teknisk-naturvitenskapelige universitet
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