The tool takes read mappings and target regions as input, and produces amplification and deletion annotations. The annotations are generated by a "depth-of-coverage" method, where the target-level coverages of the case and the controls are compared in a statistical framework. The algorithm implemented in the Copy Number Variant Detection tool is inspired by the following papers:
- Li et al., CONTRA: copy number analysis for targeted resequencing, Bioinformatics. 2012, 28(10):1307-1313[Li et al., 2012].
- Niu and Zhang, The screening and ranking algorithm to detect DNA copy number variations, Ann Appl Stat. 2012, 6(3): 1306-1326 [Niu and Zhang, 2012].
The Copy Number Variant Detection tool identifies CNVs regions where the normalized coverage is statistically significantly different from the controls.
The algorithm carries out the analysis in several steps.
- Base-level coverages are analyzed for all samples, and a robust coverage baseline is generated using the control samples.
- Chromosome-level coverage analysis is carried out on the case sample, and any chromosomes with unexpectedly high or low coverages are identified.
- Sample coverages are normalized, and a global, target-level statistical model is set up for the variation in fold-change as a function of coverage in the baseline.
- Each chromosome is segmented into regions of similar fold-changes.
- The expected fold-change variation in region is determined using the statistical model for target-level coverages. Region-level CNVs are identified as the regions with fold-changes significantly different from 1.0.
- If chosen in the parameter steps, gene-level CNV calls are also produced.
- Running the Copy Number Variant Detection tool
- Region-level CNV track (Region CNVs)
- Target-level CNV track (Target CNVs)
- Gene-level annotation track (Gene CNVs)
- CNV results report
- CNV algorithm report