WIFA

WIFA applies wavelet-based multi-resolution analysis to detect focal copy number aberrations (CNAs) from SNP microarray and array CGH data and to identify candidate driver genes in cancer genomes.

Key Features:

• Wavelet-based multi-resolution analysis: Uses wavelet analysis to resolve focal genomic aberrations within broadly altered chromosomal regions.
• Data types: Operates on high-resolution SNP microarray and array CGH datasets.
• Multi-sample integration: Simultaneously analyzes array CGH data from multiple cancer samples to identify consistent aberrations across cohorts.
• Focal CNA detection and gene mapping: Detects focal amplifications and maps them to candidate driving oncogenes within those regions.
• Comparative performance and examples: Successfully identified focal amplifications harboring SMAD7 (chr18q21.1) and FGF10 (chr5p12) in a lung cancer dataset that were not detected by GISTIC, and has been applied to glioblastoma multiforme and lung cancer SNP microarray datasets.

Scientific Applications:

• Focal CNA discovery: Detection of focal copy number aberrations within broadly altered genomic regions in cancer samples.
• Driver gene identification: Prioritization and mapping of candidate oncogenes and driver genes within focal amplification or deletion regions.
• Comparative cancer genomics: Identification of recurrent, consistent CNAs across multiple samples for cohort-level and large-scale studies.
• Application to specific cancers: Analysis of glioblastoma multiforme and lung cancer SNP microarray datasets to confirm known and uncover novel focal aberrations.

Methodology:

Performs wavelet-based multi-resolution analysis on copy number data and integrates array CGH and SNP microarray data across multiple samples to detect focal CNAs and map them to genes.