CNAnorm

CNAnorm estimates copy number alterations (CNA) from low-coverage high-throughput sequencing data (one read every 100–10,000 base pairs), correcting for normal-cell contamination and genome size variation to enable ploidy-aware CNA calling.

Key Features:

• Low Coverage Sequencing Adaptation: Tailored to low coverage data (one read every 100–10,000 base pairs) for CNA analysis from sparse sequencing.
• Ratio and GC Content Correction: Performs corrections for ratio biases and GC content variations to improve CNA accuracy.
• Normalization Methodology: Employs a "discrete" normalization approach that analyzes the multi-modality of smoothed ratio distributions to identify modes reflecting ploidy states and contamination levels.
• Tumor Content Estimation: Identifies at least two distinct ploidy states to provide insights into tumor cell fraction within samples.
• Correction for Normal Cell Contamination: Corrects for the presence of normal cells in tumor samples to recover true CNA signals.
• Genome Size Adjustment: Adjusts for differences in genome size to ensure accurate copy number estimates across regions and sample types.

Scientific Applications:

• Cancer Genomics: Distinguishes between normal and cancerous cell populations within tumor samples to support analyses of tumor heterogeneity and evolution.
• Public Data Analysis: Applied to publicly available cell line datasets such as HCC1143 and COLO829 for benchmarking and broader genomic studies.

Methodology:

Identifies the multi-modal distribution of smoothed ratios, estimates underlying ploidy levels and normal-cell contamination from the identified modes, and applies corrections to adjust CNA estimates accordingly.