Biology subjects

Won, D.-g.

Publications and source records attributed to Won, D.-g..

3 recordsLinked to original sources

bioRxiv2020

Quality threshold evaluation of Sanger confirmation for results of whole exome sequencing in clinically diagnostic setting

BackgroundWith the ability to simultaneously sequence more than 5,000 disease-associated genes, next-generation sequencing (NGS) has replaced Sanger sequencing as the preferred method in the diagnostic field at the laboratory level. However, Sanger sequencing has been used routinely to confirm identified variants prior to reporting results. This validation process causes a turnaround time delay and cost increase. Thus, this study aimed to set a quality threshold that does not require Sanger confirmation by analyzing the characteristics of identified variants from whole exome sequencing (WES). MethodsOur study analyzed data on a total of 694 disease-causing variants from 578 WES samples that had been diagnosed with suspected genetic disease. These samples were sequenced by Novaseq6000 and Exome Research Panel v2. All 694 variants (513 single-nucleotide variants (SNVs) and 181 indels) were validated by Sanger sequencing. ResultsA total of 693 variants included 512 SNVs and 181 indels from 578 patients and 367 genes. Five hundred seven heterozygous SNVs with at > 250 quality score and > 0.3 allele fraction were 100% confirmed by Sanger sequencing. Five heterozygous variants and one homozygous variant were not confirmed by Sanger sequencing, which showed 98.8% accuracy. There were 146 heterozygous variants and 35 homozygous variants among 181 indels, of which 11 heterozygous variants were not confirmed by Sanger sequencing (93.9% accuracy). Five non-confirmed variants with high quality were not identified on the ram .bam file. ConclusionOur results indicate that Sanger confirmation is not necessary for exome-derived SNVs with > 250 quality score and 0.3 > allele fraction set to an appropriate quality threshold. Indels or SNVs that do not meet the quality threshold should be reviewed by raw .bam file and Sanger confirmation should be performed to ensure accurate reporting.

genetics↗

bioRxiv2020

3Cnet: Pathogenicity prediction of human variants using knowledge transfer with deep recurrent neural networks

Thanks to the improvement of Next Generation Sequencing (NGS), genome-based diagnosis for rare disease patients become possible. However, accurate interpretation of human variants requires massive amount of knowledge gathered from previous researches and clinical cases. Also, manual analysis for each variant in the genome of patients takes enormous time and effort of clinical experts and medical doctors. Therefore, to reduce the cost of diagnosis, various computational tools have been developed for the pathogenicity prediction of human variants. Nevertheless, there has been the circularity problem of conventional tools, which leads to the overlap of training data and eventually causes overfitting of algorithms. In this research, we developed a pathogenicity predictor, named as 3Cnet, using deep recurrent neural networks which analyzes the amino-acid context of a missense mutation. 3Cnet utilizes knowledge transfer of evolutionary conservation to train insufficient clinical data without overfitting. The performance comparison clearly shows that 3Cnet can find the true disease-causing variant from a large number of missense variants in the genome of a patient with higher sensitivity (recall = 13.9 %) compared to other prediction tools such as REVEL (recall = 7.5 %) or PrimateAI (recall = 6.4 %). Consequently, 3Cnet can improve the diagnostic rate for patients and discover novel pathogenic variants with high probability.

bioinformatics↗

bioRxiv2020

A novel PS4 criterion approach based on symptoms of rare diseases and in-house frequency data in a Bayesian framework.

The American College of Medical Genetics (ACMG) and Genomics/Association for Molecular Pathology (AMP) previously reported standardized guidance for the assessment of genetic variants. One of the criteria regarding the prevalence in a case-control study, PS4, is important due to its evidence of pathogenicity. Despite recent studies approaching gene- and disease-specific probands, interpretation of a variant to PS4 still has certain limitations for rare variants. Here, we suggest a generalized method, Bayesian odds ratio (BayesianOR), applicable to PS4 via decomposing a disease to its symptoms and applying a Bayesian framework. Using this approach, we demonstrate reproducibility of the calculation of the original odds ratio from well-studied epilepsy data and verify the applicability to in-house frequencies for various rare diseases. In addition, BayesianOR showed a significant difference in tendency with different ClinVar pathogenicity, using in-house data. Thus, the novel method described here should provide an improved interpretation of sequence variants. Furthermore, we anticipate that it will enhance the diagnosis of patients with rare diseases.

genetics↗