Tangent

Tangent normalizes tumor copy-number profiles to infer somatic copy-number alterations (SCNAs) from cancer genomic data by removing systematic noise.

Key Features:

• Normalization methodology: Uses a linear combination of normal samples as a reference for each tumor sample to subtract systematic errors that vary across samples.
• Signal-to-noise ratio enhancement: Increases signal-to-noise ratios in single-nucleotide polymorphism (SNP) array and whole-exome sequencing (WES) copy-number analyses compared to conventional methods.
• Adaptability with Pseudo-Tangent: Provides Pseudo-Tangent to denoise by comparing tumor profiles directly when normal samples are limited.
• Minimal impact on signal integrity: Reduces noise while preserving true copy-number signal.
• Broad applicability: Applicable across DNA sequencing and array data types, including SNP arrays and WES.
• Integration with GATK4: Serves as the normalization method within the Genome Analysis Toolkit 4 (GATK4) copy-number pipeline.

Scientific Applications:

• SCNA inference: Enhances precision of somatic copy-number alteration (SCNA) detection by improving signal quality.
• Cancer biology research: Supports investigation of genomic alterations that drive cancer development and progression.
• Clinical and translational applications: Facilitates identification of genetic alterations relevant to targeted therapies and personalized medicine.

Methodology:

Normalizes tumor profiles by constructing a linear combination of normal samples as a reference and subtracting sample-specific systematic errors; includes Pseudo-Tangent for denoising via tumor-to-tumor comparisons when normals are limited; implemented as the normalization method within the GATK4 copy-number pipeline and applied to SNP array and WES data.