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Test development, optimization and validation of a WGS pipeline for genetic disorders
- Source :
- BMC Medical Genomics, Vol 16, Iss 1, Pp 1-11 (2023)
- Publication Year :
- 2023
- Publisher :
- BMC, 2023.
-
Abstract
- Abstract Background With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. Methods In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. Results In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7–8 deletion of SMN1 gene) validated the effectiveness of our pipeline. Conclusions This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.
Details
- Language :
- English
- ISSN :
- 17558794
- Volume :
- 16
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Medical Genomics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fe51f6315e5483190495461e1c34d35
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12920-023-01495-x