Research
Research in the Bioinformatics and Computational Medicine Program currently focusses
on five research streams: bioinformatics and computational genomics, population genomics,
translational genomics, knowledge discovery and big data analytics and drug discovery
and repurposing. In addition to these research streams individual members of the program
conduct genomic and bioinformatics research in their areas of expertise and in accordance
with their research interests.
Bioinformatics and computational genomics: The over-arching goal of research for this research stream is to utilize multiple sources of high-throughput “omics”, genotype, sequence and other biological data to map the genomic susceptibility and mutation landscape of human diseases. We are primarily focused on: (1) Development and application of rigorous computational genomics methods and bioinformatics tools for the discovery and functional characterization of genomic alterations, mutations, copy number variants and other variations driving disease phenotypes (2) Discovering and characterizing the network states and biological pathways that are impacted by rare, common genetic variants, copy number variants, mutations and other genomic alterations driving human diseases. (3) Integrating “omics”, genotype, sequences and other biological data for the discovery of clinically actionable biomarkers and for the prediction of disease prognosis and clinical outcomes.
Population genomics and epigenomics of human diseases: The goal of this research stream is to decode the genomic and epigenomic landscape to understand the triangular relationship between genomic, epigenomic and socio-economic factors and their contributions to disease phenotypes and heath disparities. Key components of this research stream include use of the coalescent theory of population genetics to understand the genomic basis of human diseases, modeling genomic and epigenomic variation and use of natural selection to understand the molecular basis of health disparities among ethnic populations.
Translational Genomics: Medicine is at the crossroads as genomic and other biological data are harnessed to obtain foundational knowledge about human diseases and to understand the molecular basis of health disparities in incidences and clinical outcomes. This research stream focuses on discovery of clinically actionable biomarkers and provides bioinformatics solutions designed to champion the use of omics driven clinical decisions to prevent, diagnose, and develop novel therapeutics to treat human diseases to positively impact the lives of patients and their families. We accomplish this goal by developing scalable integrative bioinformatics strategies and computational systems biology approaches that facilitate LSUHSC's efforts to translate genomic discoveries into clinical practice. Key to this research stream is our collaborative partnership with clinicians and other medical professionals which fosters our bench to bedside and bedside to bench approach.
Knowledge discovery and big data analytics: The goal of research under this research stream is to develop and apply data analytics and knowledge discovery tools to analysis and integration of large, complex and diverse analogue and digital data arising in healthcare, social and environmental investigations. Key to this research stream is the development of new approaches, standards, methods, tools, software, and competencies that enhance the use of big data to improve human health and eliminate health disparities. We are primarily focused on developing an integrated Knowledge discovery environment and pipelines, development of data ecosystems for biomarker and knowledge discovery, and cloud and collaborative computing with the National Data Science Centers and other national and international scientific and business organizations.
Drug discovery and repurposing: The goal of research is to develop bioinformatics and genomics solutions and technologies for drug discovery and repurposing. Key to this research is the use of omics data for compound screening and target discovery, and use of knowledge-driven systems in the form of large data marts and applying rational in silico experimental design to generate pipelines and workflows that are capable of identifying novel uses for drugs that span the therapeutic landscape. We use both publicly accessible data, such as Medline and Chembank, in addition to internal proprietary data generated by our collaborative partners in the form of gene expression, compound screening databases, and clinical trial information for target discovery and drug repositioning.