14 months ago
So there are certain things I would like to clarify here, when you say as cancer CNV workflow are you intending to work on whole genome or whole exome. Both have its own pros and cons but both the workflows needs some tweaking to give the highest confident hits. Here are my pointers:
1) If you are working with WGS/WES try to use GATK to process the alignment files and the final alignment files can be then subjected to any CNV tools downstream.
2) If you are looking for Somatic CNV then you have to use the Normal/Tumor samples processed with GATK and use a tool that works on fishing out high confidence somatic CNVs (for WGS/WES somatic CNVs there are tools like
ADtex, ExomeCNV, Control-FREEC, you can always make a wrapper function that can parse the processed normal/tumor bam files directly to the above mentioned tools to fish out the CNVs and produce the plots.
3) There is also
VarScan2 which uses circular binary segmentation and produces somatic CNVs (but I never had convincing results with it so I do not mention it much but obviously it might give great results for others)
I am stating the above keeping in mind that you are trying to detect CNVs from WGS/WES data. I have never done CNV detection from RNA-Seq so cannot comment , there are some papers which also did CNV detection from methylation sequencing data but I do not have any experience so I rest my pointers here. Good luck!