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   Twist exome capture allows for lower average sequence coverage in clinical exome sequencing; ; et al in Human Genomics (2023), 17(1), 39 Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main ... [more ▼] Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques. [less ▲] Detailed reference viewed: 19 (0 UL)A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing; ; et al in Genetics in Medicine (2023), 25(4), 100018 Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a ... [more ▼] Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock. [less ▲] Detailed reference viewed: 34 (4 UL)The Data Use Ontology to streamline responsible access to human biomedical datasets; ; et al in Cell Genomics (2021), 1(2), Detailed reference viewed: 43 (1 UL)Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.; ; et al in Nucleic Acids Research (2015), 43(Database issue), 1071-8 The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype ... [more ▼] The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protege to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning. [less ▲] Detailed reference viewed: 421 (4 UL) |
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