Thanks for your detailed explanation. Just out of curiosity and to follow-up, presumably this is a example of the list of detected CNVs in a TCGA Breast Cancer

Thanks for your detailed explanation. Just out of curiosity and to follow-up, presumably this is a example of the list of detected CNVs in a TCGA Breast Cancer data-set you're referring to: http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv_t http://cancer.sanger.ac.uk/cosmic/gene/analysis?ln=BRCA1#cnv... According to Sanger (or maybe TCGA?), a gain is when a genomic region (for a diploid) has more than five absolute copies of this region and a loss is when the genomic region has no reads ((http://cancer.sanger.ac.uk/cosmic/help/cnv/overview http://cancer.sanger.ac.uk/cosmic/help/cnv/overview), where the copy number is perhaps determined by that normalized distribution of read coverage across the reference genome? (http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-S11-S1#Fig1 http://bmcbioinformatics.biomedcentral.com/articles/10.1186/...). This is for CNVs that are longer than the 150-200bp Illumina fragments (Fig1c. Read Depth method, e.g., exome#3 looks like it has two absolute copies vs exome #1 and #2) Then for small CNVs that perhaps span that 150-200bp fragment, we use the split read method to filter for incompletely mapped reads that are only aligned on the edges to the reference. This implies that there was a duplication event that expanded that sequence? (Fig 1b. Split Read method). Presumably, the pipeline would determine the CNV sites in a specific patient sample, then cross-reference with the TCGA CNV data-set and come up with correlation score of how much those CNVs sites match with consensus CNVs in the cancer data-set? Thanks again for your detailed breakdown.