GeTallele

GeTallele analyzes DNA and RNA variant allele frequency (VAF) distributions to assess gene- and chromosome-level allele asymmetries and detect genomic alterations.

Key Features:

• Variant Probability Estimation: Estimates a variant probability parameter that quantifies the likelihood of variants within chromosomal segments as a biologically interpretable descriptor of VAF distributions.
• Segmentation of multi-SNV regions: Segments continuous multi-SNV genomic regions to characterize local VAF distribution structure.
• Synthetic VAF generation: Generates synthetic VAF samples from estimated variant probabilities for statistical comparison with observed data.
• DNA–RNA comparison: Compares DNA and RNA VAF distributions from matched datasets, including whole exome sequencing (WES) and RNA sequencing, and reports similar segmentation and variant probability profiles.
• Correlation with copy number alterations (CNA): Correlates variant probabilities and VAF distribution structures with copy number alterations to link VAF patterns to structural genomic changes.
• Tumor purity estimation: Estimates tumor purity with reported Pearson correlations of 0.44–0.76 to other established methods.
• MATLAB implementation and analysis suite: Implemented in MATLAB and provides functions for analysis, statistical assessment, and visualization of genome and transcriptome allele frequencies.

Scientific Applications:

• Cancer Genomics: Identifies genomic alterations in cancer, illustrated for breast invasive carcinoma (BRCA) using The Cancer Genome Atlas (TCGA) data.
• Genetic Variation Studies: Assesses allele asymmetries and relates VAF distribution features to genetic variation and biological traits.
• Transcriptional Response Analysis: Analyzes allele-specific expression and gene-dosage transcriptional responses to investigate regulatory mechanisms.

Methodology:

Segments continuous multi-SNV genomic regions, estimates a variant probability per segment, generates synthetic VAF samples from those probabilities, and performs statistical comparisons between observed and synthetic VAFs in matched DNA (WES) and RNA-seq datasets with correlation analyses to CNAs.