genomics-cnv-calling

You can detect copy number variations using CNVkit, GATK gCNV, or Control-FREEC for read-depth-based variant calling. This Skill supports all three algorithms to identify amplifications and deletions from exome or whole-genome sequencing BAM files.

Yes, CNV calling supports tumor-only sequencing samples without matched normals. This Skill handles tumor-normal pairs, tumor-only samples, and germline analysis modes to identify copy number variations across different experimental designs.

Using CNVkit for rapid analysis of exome sequencing datasets is an effective way to find gene amplifications. This Skill leverages read-depth-based segmentation to detect specific amplifications or deletions driving cancer progression.

Yes, this copy number variant detection approach works with GATK gCNV and Control-FREEC in addition to CNVkit. It supports multiple established bioinformatics algorithms for robust read-depth-based variant calling and segmentation.

You need aligned sequencing data files, typically BAM files, for copy number variant calling. This Skill processes BAM files to perform read-depth-based segmentation and identify amplifications and deletions across exome or whole-genome data.

Use germline analysis mode for inherited copy number variations and tumor-normal pairs for somatic CNVs in cancer. This Skill flexibly handles both modes to target different types of genetic diseases and cancer progression.