## CNA-seq / Segment and call copy number aberrations

### Description

CNA-seq binned read counts are segmented and called to identify copy number aberrations.

### Parameters

- organism (human) [human]
- human genome build (GRCh37/hg19) [GRCh37/hg19]
- breakpoint threshold [0.8]
- minimum mappability (0...1) [0.85]
- phenodata column with ploidy of samples
- phenodata column with cellularity of samples (0...1)
- unique match (no, yes) [no]

### Details

The input should be original read counts (not GC corrected, normalized, or filtered for mappability). They are processed with the FREEC algorithm, which includes GC correction, mappability filtering, segmentation, and calling copy number aberrations. Parameters are explained in the tutorial for the algorithm.

After running, copy number profiles can be plotted both with the tools "NGS / CNA-seq / Plot copy number profiles" or "Microarrays / aCGH / Plot copy number profiles". For more downstream analyses, tools in the category "Microarrays / aCGH" are recommended starting from the "Identify common regions from called copy number data". Alternative, calling can also be done with the tool "aCGH / Call copy number aberrations".

### Output

Log ratios, segmented log ratios, copy number calls (-1 = loss, 0 = normal, 1 = gain), and the estimated copy numbers.

A plot is also returned that shows the frequencies of gains (blue) and losses (red).

### References

Boeva et al. (2011) Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics 27: 268-269