Dataset Information

A practical method to detect SNVs and indels from whole genome and exome sequencing data.

ABSTRACT: The recent development of massively parallel sequencing technology has allowed the creation of comprehensive catalogs of genetic variation. However, due to the relatively high sequencing error rate for short read sequence data, sophisticated analysis methods are required to obtain high-quality variant calls. Here, we developed a probabilistic multinomial method for the detection of single nucleotide variants (SNVs) as well as short insertions and deletions (indels) in whole genome sequencing (WGS) and whole exome sequencing (WES) data for single sample calling. Evaluation with DNA genotyping arrays revealed a concordance rate of 99.98% for WGS calls and 99.99% for WES calls. Sanger sequencing of the discordant calls determined the false positive and false negative rates for the WGS (0.0068% and 0.17%) and WES (0.0036% and 0.0084%) datasets. Furthermore, short indels were identified with high accuracy (WGS: 94.7%, WES: 97.3%). We believe our method can contribute to the greater understanding of human diseases.

SUBMITTER: Shigemizu D

PROVIDER: S-EPMC3703611 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A practical method to detect SNVs and indels from whole genome and exome sequencing data.

Shigemizu Daichi D Fujimoto Akihiro A Akiyama Shintaro S Abe Tetsuo T Nakano Kaoru K Boroevich Keith A KA Yamamoto Yujiro Y Furuta Mayuko M Kubo Michiaki M Nakagawa Hidewaki H Tsunoda Tatsuhiko T

Scientific reports 20130101

The recent development of massively parallel sequencing technology has allowed the creation of comprehensive catalogs of genetic variation. However, due to the relatively high sequencing error rate for short read sequence data, sophisticated analysis methods are required to obtain high-quality variant calls. Here, we developed a probabilistic multinomial method for the detection of single nucleotide variants (SNVs) as well as short insertions and deletions (indels) in whole genome sequencing (WG ...[more]

PMID: 23831772

Similar Datasets

Project description:Background: Epileptic encephalopathies (EEs) are a pediatric entity with highly phenotypic and genetic heterogeneity. Both single nucleotide variants (SNVs)/Indels and copy number variations (CNVs) could be the causes. Whole exome sequencing (WES) is widely applied to detect SNVs/Indels, but the bioinformatics approach for detecting CNVs is still limited and weak. In the current study, the possibility of profiling both disease-causing SNVs/Indels and CNVs in a single test based on WES in EEs was evaluated. Methods: The infants diagnosed with EEs were enrolled from a single pediatric epilepsy center between January 2018 and February 2020. Demographic and clinical data were collected. In WES data, the pathogenic SNVs were identified through an in-house pipeline, and pathogenic CNVs were identified by CNVkit. The diagnostic rate was evaluated, and the molecular findings were characterized. Results: A total of 73 infants were included; 36 (49.32%) of them were males. The median age was 7 months. Thirty-two (43.84%) infants had been diagnosed with epilepsy syndrome. The most common type of syndrome was West syndrome (22/73, 30.1%), followed by Dravet syndrome (20/77, 27.4%). Fifty-four (73.97%) had intellectual development delay. The genetic cause of EEs, pathogenic or likely pathogenic variants, were successfully discovered in 46.6% (34/73) of the infants, and 29 (39.7%) infants carried SNVs/Indels, while 5 (6.8%) carried CNVs. The majority of the disease-causing variants were inherited in de novo pattern (25, 71.4%). In addition to showing that the variants in the ion channel encoding genes accounted for the main etiology, we discovered and confirmed two new disease-causing genes, CACNA1E and WDR26. Five discovered CNVs were deletions of 2q24.3, 1p36, 15q11-q13, 16p11.2, and 17p13.3, and all were confirmed by array comparative genomic hybridization. Conclusion: The application of both SNVs/Indels and CNVs detection in a single test based on WES yielded a high diagnosis rate in EEs. WES may serve as a first-tier test with cost-effective benefit in EEs.

Project description:INDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.We characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low-quality INDELs (7% vs. 51%).Simulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (53%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (84% vs. 57%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.Overall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (for example, capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Dataset Information

A practical method to detect SNVs and indels from whole genome and exome sequencing data.

Publications

A practical method to detect SNVs and indels from whole genome and exome sequencing data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure