![]() Screening for mutations in the ‘syndromic HH’ genes is guided by phenotype with genetic testing used to confirm the clinical diagnosis. Abstract.ĪbstractObjectiveHyperinsulinaemic hypoglycaemia (HH) can occur in isolation or more rarely feature as part of a syndrome. We will continue to update the UTR annotator as we gain new knowledge on the impact of variants in UTRs.Availability and implementationUTRannotator is freely available on Github: informationSupplementary data are available at bioRxiv. We investigate the utility of this tool using the ClinVar database, providing an annotation for 30.8% of all 5’UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5’untranslated regions (5’UTR) that create or disrupt upstream open reading frames (uORFs). Consequently, these variants are poorly annotated using standard tools. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Abstract.ĪbstractSummar圜urrent tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. It can call CNVs genome-wide from targeted panel and exome data, increasing the utility and diagnostic yield of these tests. ![]() Savv圜NV outperforms existing tools for calling CNVs from off-target reads. We then applied Savv圜NV to clinical samples sequenced using a targeted panel and were able to call previously undetected clinically-relevant CNVs, highlighting the utility of this tool within the diagnostic setting. ![]() Savv圜NV called CNVs with high precision and recall, outperforming the five other tools at calling CNVs genome-wide, using off-target or on-target reads from targeted panel and exome sequencing. We benchmarked Savv圜NV against five state-of-the-art CNV callers using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. We have developed a new tool, Savv圜NV, to exploit this ‘free data’ to call CNVs across the genome. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions. We present Savv圜NV, a tool which uses off-target read data from exome and targeted sequencing data to call germline CNVs genome-wide. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. Identifying copy number variants (CNVs) can provide diagnoses to patients and provide important biological insights into human health and disease. In his spare time Matthew enjoys music, gardening, DIY and mountain walking. He has been working in the monogenic diabetes group in the university medical school since 2015. He has been instrumental in the discovery of multiple gene-disease associations for monogenic diabetes and hyperinsulinism. Matthew has developed a keen interest in novel data analysis methods for short read sequencing data, particularly for extracting information (such as copy number variants, homozygosity mapping, and relatedness) from sequence reads that would otherwise be unused. He then studied for his engineering doctorate with the University of Surrey and the Met Office. ![]() Immediately following this, he worked for two years in an internet shopping startup company, before returning to the university to work for the Department of Genetics in the FlyMine group for eight years. Matthew graduated with a BA (and MA) in Computer Science from the University of Cambridge. ![]()
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