CNVkit processes sequencing data to identify CNVs through several steps:
Reference Generation: CNVkit generates a reference from normal samples or uses a pre-built reference to compare against tumor samples. Segmentation: It segments the genome into regions of similar copy number based on read depth and log-ratio values. Normalization: It normalizes the data to account for biases and noise inherent in sequencing technologies. Visualization: CNVkit provides tools for visualizing CNV profiles, aiding in the interpretation of complex data.
