Dataset Information

ExomeSuite: Whole exome sequence variant filtering tool for rapid identification of putative disease causing SNVs/indels.

ABSTRACT: Exome and whole-genome analyses powered by next-generation sequencing (NGS) have become invaluable tools in identifying causal mutations responsible for Mendelian disorders. Given that individual exomes contain several thousand single nucleotide variants and insertions/deletions, it remains a challenge to analyze large numbers of variants from multiple exomes to identify causal alleles associated with inherited conditions. To this end, we have developed user-friendly software that analyzes variant calls from multiple individuals to facilitate identification of causal mutations. The software, termed exomeSuite, filters for putative causative variants of monogenic diseases inherited in one of three forms: dominant, recessive caused by a homozygous variant, or recessive caused by two compound heterozygous variants. In addition, exomeSuite can perform homozygosity mapping and analyze the variant data of multiple unrelated individuals. Here we demonstrate that filtering of variants with exomeSuite reduces datasets to a fraction of a percent of their original size. To the best of our knowledge this is the first freely available software developed to analyze variant data from multiple individuals that rapidly assimilates and filters large data sets based on pattern of inheritance.

SUBMITTER: Maranhao B

PROVIDER: S-EPMC4146529 | biostudies-literature | 2014 Feb-Mar

REPOSITORIES: biostudies-literature

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exomeSuite: Whole exome sequence variant filtering tool for rapid identification of putative disease causing SNVs/indels.

Maranhao B B Biswas P P Duncan J L JL Branham K E KE Silva G A GA Naeem M A MA Khan S N SN Riazuddin S S Hejtmancik J F JF Heckenlively J R JR Riazuddin S A SA Lee P L PL Ayyagari R R

Genomics 20140201 2-3

Exome and whole-genome analyses powered by next-generation sequencing (NGS) have become invaluable tools in identifying causal mutations responsible for Mendelian disorders. Given that individual exomes contain several thousand single nucleotide variants and insertions/deletions, it remains a challenge to analyze large numbers of variants from multiple exomes to identify causal alleles associated with inherited conditions. To this end, we have developed user-friendly software that analyzes varia ...[more]

PMID: 24603341

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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.

Dataset Information

ExomeSuite: Whole exome sequence variant filtering tool for rapid identification of putative disease causing SNVs/indels.

Publications

exomeSuite: Whole exome sequence variant filtering tool for rapid identification of putative disease causing SNVs/indels.

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