gaia
gaia assesses the statistical significance of chromosomal aberrations in high-throughput genomic datasets to identify genomic alterations associated with cancer, genetic disorders, and other complex diseases.
Key Features:
- Statistical analysis: Implements robust statistical methodologies to determine the significance of chromosomal aberrations within genomic datasets.
- Bioconductor integration: Operates within the Bioconductor ecosystem using R, enabling interoperability with Bioconductor packages and data structures.
Scientific Applications:
- Genomics research: Assessment of chromosomal aberrations in genome-wide studies.
- Cancer research: Identification of significant chromosomal alterations associated with cancer.
- Genetic disorder studies: Detection and statistical evaluation of aberrations relevant to genetic disorders and other complex diseases.
Methodology:
Uses R within the Bioconductor framework and employs advanced statistical techniques to analyze high-throughput genomic data.
Topics
Collections
Details
- License:
- GPL-2.0
- Tool Type:
- command-line tool, library
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R
- Added:
- 1/17/2017
- Last Updated:
- 11/25/2024
Operations
Data Inputs & Outputs
Variant classification
Publications
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods. 2015;12(2):115-121. doi:10.1038/nmeth.3252. PMID:25633503. PMCID:PMC4509590.