The accurate detection of low-allelic variants is still challenging, particularly for the identification of somatic mosaicism, where matched control sample is not available. High throughput sequencing, by the simultaneous and independent analysis of thousands of different DNA fragments, might overcome many of the limits of traditional methods, greatly increasing the sensitivity. However, it is necessary to take into account the high number of false positives that may arise due to the lack of matched control samples. Here, we applied deep amplicon sequencing to the analysis of samples with known genotype and variant allele fraction (VAF) followed by a tailored statistical analysis. This method allowed to define a minimum value of VAF for detecting mosaic variants with high accuracy. Then, we exploited the estimated VAF to select candidate alterations in NF2 gene in 34 samples with unknown genotype (30 blood and 4 tumor DNAs), demonstrating the suitability of our method. The strategy we propose optimizes the use of deep amplicon sequencing for the identification of low abundance variants. Moreover, our method can be applied to different high throughput sequencing approaches to estimate the background noise and define the accuracy of the experimental design.

A systematic assessment of accuracy in detecting somatic mosaic variants by deep amplicon sequencing: Application to NF2 gene / Contini, Elisa; Paganini, Irene; Sestini, Roberta; Candita, Luisa; Capone, Gabriele Lorenzo; Barbetti, Lorenzo; Falconi, Serena; Frusconi, Sabrina; Giotti, Irene; Giuliani, Costanza; Torricelli, Francesca; Benelli, Matteo; Papi, Laura. - In: PLOS ONE. - ISSN 1932-6203. - ELETTRONICO. - 10:(2015), pp. e0129099-e0129099. [10.1371/journal.pone.0129099]

A systematic assessment of accuracy in detecting somatic mosaic variants by deep amplicon sequencing: Application to NF2 gene

PAGANINI, IRENE;SESTINI, ROBERTA;Candita, Luisa;CAPONE, GABRIELE LORENZO;BENELLI, MATTEO;PAPI, LAURA
2015

Abstract

The accurate detection of low-allelic variants is still challenging, particularly for the identification of somatic mosaicism, where matched control sample is not available. High throughput sequencing, by the simultaneous and independent analysis of thousands of different DNA fragments, might overcome many of the limits of traditional methods, greatly increasing the sensitivity. However, it is necessary to take into account the high number of false positives that may arise due to the lack of matched control samples. Here, we applied deep amplicon sequencing to the analysis of samples with known genotype and variant allele fraction (VAF) followed by a tailored statistical analysis. This method allowed to define a minimum value of VAF for detecting mosaic variants with high accuracy. Then, we exploited the estimated VAF to select candidate alterations in NF2 gene in 34 samples with unknown genotype (30 blood and 4 tumor DNAs), demonstrating the suitability of our method. The strategy we propose optimizes the use of deep amplicon sequencing for the identification of low abundance variants. Moreover, our method can be applied to different high throughput sequencing approaches to estimate the background noise and define the accuracy of the experimental design.
2015
10
e0129099
e0129099
Contini, Elisa; Paganini, Irene; Sestini, Roberta; Candita, Luisa; Capone, Gabriele Lorenzo; Barbetti, Lorenzo; Falconi, Serena; Frusconi, Sabrina; Giotti, Irene; Giuliani, Costanza; Torricelli, Francesca; Benelli, Matteo; Papi, Laura
File in questo prodotto:
File Dimensione Formato  
journal.pone.0129099.PDF

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Creative commons
Dimensione 461.52 kB
Formato Adobe PDF
461.52 kB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1010657
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 13
social impact