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Genomics, Computational Biology and Technology Study Section [GCAT]

The GCAT study section reviews grant applications involved with the development of tools and methodologies or the application of emerging technologies to the global and integrative analysis of biological systems. Applications that are focused on applying established methods or that focus on a specific disease or molecular mechanism are not suitable for review by GCAT.



  • Generation, analysis, and mining of –omic datasets: genetic, epigenetic, biochemical, gene expression, metabolic, proteomic, microarray, sequencing.
  • Large-scale genomic, genetic, and epigenetic resources: collections of mutant strains and lines, tagged genes, small molecule probes, model organism systems for genetic, genomic or high throughput analyses, high throughput genetic and epigenetic technologies, classification and annotation systems for genetic and epigenetic data including databases and user interfaces.
  • Development and application of emerging genomic and epigenomic technologies to cellular, metabolic or disease pathways.
  • Computational and mathematical representation and simulation of genetic and genomic systems: population genetics, gene mapping, genetic and biochemical networks, genetic and epigenetic systems biology, signaling pathways, physiological or metabolic systems, integration of –omic datasets.
  • Development of new computational algorithms and software as applied to –omic/genetic data.
Shared Interests
  • There are shared interests with GHD in the application of genomic technologies to the identification of genes involved in human disease. Grant applications where the main focus is the development of new methods irrespective of the disease may be assigned to GCAT. Grant applications where the main focus is to identify genes for a specific disease using existing methodologies may be assigned to GHD.
  • There are shared interests with BDMA in the development of computational methods for analysis of large scale datasets and databases. Applications proposing to develop bioinformatic methods with application to –omic studies and the analysis of biological content are more suitable for GCAT. Applications proposing to develop informatics methods for –omics information as data elements may be assigned to BDMA.
  • There are shared interests with CG in the application of methods to the study of cancer genomics, including structural variation and phylogenetic analysis of tumor samples. Applications that apply existing computational methods to address specific mechanisms or processes in cancer may be reviewed in CG. Applications that propose to develop new computational methods or technologies, which may also be applied to other diseases, are more suitable for GCAT.
  • There are shared interests with MGA and MGB in the analysis of processes related to transcription, splicing, epigenetics, DNA replication and repair, and translation. Applications that develop and apply novel and emerging technologies to study and catalog these processes at a genomic scale may be assigned to GCAT. Applications that focus on elucidating the molecular mechanism of these processes may be assigned to MGA or MGB.
  • There are shared interests with GVE in studies of genetic variation in humans and model organisms. Applications with an evolutionary focus that use existing computational methods, or applications to develop new methods for evolutionary theory, may be reviewed in GVE. Applications proposing new computational methods as applied to genetic variation may be reviewed in GCAT.
  • There are shared interests with BMRD in the development of statistical methodology as related to human genetic studies. Applications where the main focus is on development of statistical genetic methods may be assigned to BMRD. Applications whose primary focus is the development of methods to address specific questions in genetics or genomics may be assigned to GCAT.

Closely Related


Keywords: Genomics, genetics, epigenetics, proteomics, metabolomics, –omics, systems biology, algorithm development, high throughput technologies, bioinformatics, data integration, computational biology, high dimensional data