deepSNV

deepSNV detects low-frequency and subclonal mutations from targeted deep resequencing of cancer genes to characterize clonal heterogeneity and identify variants with prognostic significance in cancer genomics.

Key Features:

• Novel statistical approach: Applies a statistical methodology tailored for mutation calling from large targeted resequencing datasets of cancer genes.
• Local error profiling: Estimates precise local error profiles to achieve high sensitivity and specificity for low-frequency subclonal variant detection.
• Probabilistic methodology: Implements a probabilistic framework that integrates prior knowledge and prior probabilities about variant distributions to improve call accuracy.
• Prognostic relevance: Identifies clonal and subclonal variants and links them to prognostic consequences.

Scientific Applications:

• Cancer genomics research: Supports large-scale studies of mutation landscapes in cancer using targeted resequencing data.
• Diagnostic precision: Enhances diagnostic precision by enabling detection of subclonal mutations relevant for personalized treatment planning.
• Prognostic studies: Enables identification of variants with prognostic significance to inform studies of disease progression and patient outcomes.

Methodology:

Implements a novel statistical, probabilistic model that estimates precise local error profiles and incorporates prior probabilities of variant occurrence for mutation calling from targeted deep resequencing data.