Publications by authors named "Sean Griffith"

Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that provide variant-level information and phenotypic features to researchers, clinicians, healthcare providers and patients. Following the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases have as one goal to facilitate connection to one another using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).

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GeneMatcher (genematcher.org) is a tool designed to connect individuals with an interest in the same gene. Now used around the world to create collaborations and generate the evidence needed to support novel disease gene identification, GeneMatcher is a founding member of the Matchmaker Exchange (MME; matchmakerexchange.

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Background: With the advent of whole exome (ES) and genome sequencing (GS) as tools for disease gene discovery, rare variant filtering, prioritization and data sharing have become essential components of the search for disease genes and variants potentially contributing to disease phenotypes. The computational storage, data manipulation, and bioinformatic interpretation of thousands to millions of variants identified in ES and GS, respectively, is a challenging task. To aid in that endeavor, we constructed PhenoDB, GeneMatcher and VariantMatcher.

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During the development of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), a myriad of complications has emerged and although rare, several genitourinary complications have been reported. The bulk of these complications have been secondary to hypercoagulable states, such as priapism. Previous SARS family infections have caused orchitis, though no adult cases of orchitis have been reported.

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This unit describes a technique for generating exome-enriched sequencing libraries using DNA extracted from formalin-fixed paraffin-embedded (FFPE) samples. Utilizing commercially available kits, we present a low-input FFPE workflow starting with 50 ng of DNA. This procedure includes a repair step to address damage caused by FFPE preservation that improves sequence quality.

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Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly stringent significance thresholds in traditional statistical methods, such as linear and logistic regression. Machine learning methods, such as Random Forests (RF), are an alternative approach to identify potentially interesting variants.

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