isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data.
BMC Bioinformatics
; 22(1): 530, 2021 Oct 29.
Article
em En
| MEDLINE
| ID: mdl-34715772
ABSTRACT
BACKGROUND:
Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole-exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches.RESULTS:
We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets.CONCLUSIONS:
isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https//gitlab.com/sequentiateampublic/isocnv .Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Variações do Número de Cópias de DNA
/
Exoma
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Espanha