VanillaICE

VanillaICE analyzes chromosomal alterations from high-density single nucleotide polymorphism (SNP) microarray data to characterize copy number and genotype variation and to detect aneuploidy, segmental changes (insertions, deletions, inversions, translocations), and SNP-level alterations.

Key Features:

• Comprehensive Detection of Chromosomal Variations: Identifies aneuploidy, segmental insertions, deletions, inversions, translocations, and single nucleotide polymorphisms (SNPs).
• Integration with High-Density SNP Microarrays: Processes high-density SNP microarray data to detect genetic variation linked to normal phenotypic diversity and disease.
• Enhanced HMM Framework: Integrates copy number information, genotype calls, and their uncertainties into a Hidden Markov Model to model spatial dependencies between neighboring SNPs.
• Probabilistic Smoothing with Confidence Scores: Uses confidence scores to control smoothing within a probabilistic framework to account for measurement uncertainty.

Scientific Applications:

• Genomic Research: Investigating normal genetic diversity and the genomic basis of diseases by characterizing chromosomal alterations.
• Clinical Diagnostics: Identifying chromosomal abnormalities relevant to the diagnosis of genetic disorders.
• Cancer Genomics: Detecting copy number variations and other genomic changes associated with cancer development and progression.

Methodology:

Implements a Hidden Markov Model that incorporates copy number and genotype information with associated uncertainties, models spatial dependencies between neighboring SNPs, and applies confidence-score-controlled probabilistic smoothing to high-density SNP microarray data.