Identification of novel
Analytical validation
BRCA1 LGRs
DQ analysis
Deep coverage
MLPA
NGS
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2019
2019
Historique:
received:
29
08
2019
accepted:
01
10
2019
entrez:
20
11
2019
pubmed:
20
11
2019
medline:
20
11
2019
Statut:
epublish
Résumé
Genetic testing for We first used NGS data of a group of three patients (training set), previously screened in our laboratory by conventional methods, to develop an algorithm for the calculation of the dosage quotient (DQ) to be compared with the Ion Reporter (IR) analysis. Then, we tested the optimized pipeline with a consecutive cohort of 85 uncharacterized probands (validation set) also subjected to MLPA analysis. Characterization of the breakpoints of three novel In our cohort, the newly defined DQ-based algorithm detected 3/3 Our study defined a DQ-based algorithm to identify
Sections du résumé
BACKGROUND
BACKGROUND
Genetic testing for
METHODS
METHODS
We first used NGS data of a group of three patients (training set), previously screened in our laboratory by conventional methods, to develop an algorithm for the calculation of the dosage quotient (DQ) to be compared with the Ion Reporter (IR) analysis. Then, we tested the optimized pipeline with a consecutive cohort of 85 uncharacterized probands (validation set) also subjected to MLPA analysis. Characterization of the breakpoints of three novel
RESULTS
RESULTS
In our cohort, the newly defined DQ-based algorithm detected 3/3
CONCLUSIONS
CONCLUSIONS
Our study defined a DQ-based algorithm to identify
Identifiants
pubmed: 31741787
doi: 10.7717/peerj.7972
pii: 7972
pmc: PMC6859874
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e7972Informations de copyright
©2019 Nicolussi et al.
Déclaration de conflit d'intérêts
The authors declare there are no competing interests.
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