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Computational Genomics and Data Science Methods Development

Computational Methods for Clinical Genomics 

Development and implementation of genomic-based clinical informatics resources and tools that harmonize scalable, sharable and computable inferences of genomic knowledge with clinical practice guidelines. Includes frameworks and collaborative tools that allow researchers to share, analyze and secure genomic data and patient information.

Computational Methods for Functional Genomics

Development of novel methods, software and tools to analyze gene regulation, gene expression, epigenetic modifications and methylation data. Includes methods to integrate and interpret across multiple data types.

Computational Methods for Genomic Sequencing Data

Development of novel methods, software and tools to process, align, format and visualize genomic sequence reads; perform genome assembly; and extract sequence features. Includes graph-based and other novel approaches for pangenome analysis. Also includes general genomic analysis tools.

Development of novel methods, software and tools to process, align, format and visualize genomic sequence reads; perform genome assembly; and extract sequence features. Includes graph-based and other novel approaches for pangenome analysis. Also includes general genomic analysis tools.

Computational Methods for Variation and Association Analysis

Development of novel methods, software and tools for identifying and interpreting genetic variation, elucidating the genetic architecture of human traits and disease, and analyzing population and evolutionary level genomic data.

Development of novel methods, software and tools for identifying and interpreting genetic variation, elucidating the genetic architecture of human traits and disease, and analyzing population and evolutionary level genomic data.

Genomic Data Resources

Genomic and Phenotype Measures and Standards

Development of tools and standards to facilitate sharing and analysis of large-scale genomics data, phenotype data and associated metadata. Includes approaches to harmonize phenotypic information for use in genomic analysis, such as incorporating family history information, electronic phenotyping and ontology development.

Development of tools and standards to facilitate sharing and analysis of large-scale genomics data, phenotype data and associated metadata. Includes approaches to harmonize phenotypic information for use in genomic analysis, such as incorporating family history information, electronic phenotyping and ontology development.

Genomic Community Resources 

Development and maintenance of resources that collect, curate, integrate and distribute comprehensive sets of genomic information from humans or biomedically relevant species. Includes software environments to store, share, analyze and visualize genomics data.

Last updated: April 21, 2022