Dolan, M. E. | bioRxiv Science

bioRxiv2020

Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2019

RegSNPs-Intron: A Computational Framework For Prioritizing Intronic Single Nucleotide Variants in Human Genetic Disease

A large number of single nucleotide variants (SNVs) in the human genome are known to be responsible for inherited disease. An even larger number of SNVs, particularly those located in introns, have yet to be investigated for their pathogenic potential. Using known pathogenic and neutral intronic SNVs (iSNVs), we developed the regSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. regSNPs-intron showed high accuracy in computing disease-causing probabilities of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we validated regSNPs-intron predictions by measuring the impact of iSNVs on splicing outcome. Together, regSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis. regSNPs-intron is available at https://regsnps-intron.ccbb.iupui.edu.

bioinformatics↗