Bioinformatic Analysis of the Genetics of Common Complex Diseases

National Institute On Aging

Investigators

Linked publications & trials

Abstract

Large volumes of high-throughput sequencing data have been deposited in various public databases such as GEO, SRA, dbGaP, synapse.org, and others. We leverage computational biology approaches to identify non-coding RNAs, including circular RNAs (circRNAs), associated with age-related cognitive decline and Alzheimer's Disease. Additionally, we utilize bioinformatics techniques to analyze microbiome data in aging systems and age-related diseases, focusing particularly on the microbiome's role in Alzheimer's Disease pathophysiology and progression. Our work also involves employing computational biology, machine learning, and artificial intelligence (AI) approaches to analyze data from a wide range of complex human diseases, chronic conditions, aging, and age-associated disease processes. Furthermore, we develop new algorithms for identifying cell boundaries in spatial transcriptomics projects and for multi-omics analysis. Over the years, we have applied these efforts to numerous projects, including the Genetic Association Database (GAD), Disease-Phenotype webPAGE, microbiome data annotation (PubBug), AI model integration in cell segmentation, and the CircInteractome web tool. Additionally, we have created Shiny Apps to facilitate data analysis for common complex diseases. We also maintain software licenses for bioinformatic analysis tools and provide training for fellows and scientists in data science.