Genomics, Computational Biology and Technology Study Section – GCAT

Topics

• 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 and Overlaps

There are shared interests in the application of genomic technologies to the identification of genes involved in human disease with Genetics of Health and Disease (GHD). Applications that emphasize the development of new genomic and epi-genomic methods irrespective of the disease are reviewed in GCAT. Applications that emphasize the identification genes for a specific disease using existing methodologies are reviewed in GHD.

There are shared interests with Biodata Management and Analysis (BDMA) in the computational analysis of ‘–omics’ data. Applications that propose to develop computational methods and tools for improving ‘-omics’ data analysis and integration may be assigned to BDMA. Applications developing such methods with a focus on fundamental biological questions may be assigned to GCAT.

There are shared interests with Cancer Genetics Study Section (CG) in the applications that propose analysis and mining of ‘-omic’ datasets. Applications focusing mainly on analysis and mining of “–omic” datasets and application of bioinformatics to study genomes, transcriptomes and proteomes of tumors and cancer cell(s), including structural variation and phylogenetic analysis of tumors should be assigned to 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 Genetic Variation and Evolution (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 in the analysis of omic/genetic data with Analytics and Statistics for Population Research Panel A (ASPA). Applications that focus on development of new computational methodologies, algorithms and software as applied to –omic/genetic data are reviewed in GCAT. Applications that focus on the development of statistical methodology for analysis of human omic/genetic data from population-based studies are reviewed in ASPA.  

There are shared interests in microbial genomics with Prokaryotic Cell and Molecular Biology (PCMB). Applications that emphasize development of metagenomic analysis methods are reviewed in GCAT. Applications that emphasize processes of microbial communities are reviewed in PCMB. 

There are shared interests in the genomic-scale analysis of processes related to transcription, splicing, epigenetics, DNA replication and repair, and translation with Molecular Genetics (MG). Applications that develop and apply novel and emerging technologies to study and catalog these processes at a genomic scale are reviewed in GCAT. Applications that focus on elucidating the molecular mechanism of these processes are reviewed in MG. 

There are shared interests in the computational analysis of -omics data with Neuro Informatics, Computational and Data Analysis (NICD). Applications that emphasize developing computational methods to address specific biological questions in genetics, genomics, metabolomics, and proteomics are reviewed in GCAT. Applications that emphasize developing computational methods and tools for improving brain related imaging -omic data analysis and integration are reviewed in NICD.