Warning about counts for chromosome X
Calling CNVs on the X chromosome can create issues if the exome sample of interest and the reference exome samples it is being compared to are not gender matched. Make sure that the genders are matched properly (i.e. do not use male as a reference for female samples and vice versa).
What it does
This tool uses ExomeDepth to call copy number variants (CNVs) from targeted sequence data.
Output format
| Column | Description |
| chr | Chromosome |
| start | Start of CNV region |
| end | End of CNV region |
| type | CNV type (deletion, duplication) |
| sample | Name of the sample with CNV |
| corr | Correlation between reference and test counts. To get meaningful result, this correlation should really be above 0.97. If this is not the case, consider the output of ExomeDepth as less reliable (i.e. most likely a high false positive rate) |
| nexons | Number of target regions covered by the CNV |
| BF | Bayes factor. It quantifies the statistical support for each CNV. It is in fact the log10 of the likelihood ratio of data for the CNV call divided by the null (normal copy number). The higher that number, the more confident one can be about the presence of a CNV. While it is difficult to give an ideal threshold, and for short exons the Bayes Factor are bound to be unconvincing, the most obvious large calls should be easily flagged by ranking them according to this quantity |
| reads.ratio | Observed/expected reads ratio |
What ExomeDepth does and does not do
ExomeDepth uses read depth data to call CNVs from exome sequencing experiments. A key idea is that the test exome should be compared to a matched aggregate reference set. This aggregate reference set should combine exomes from the same batch and it should also be optimized for each exome. It will certainly differ from one exome to the next.
Importantly, ExomeDepth assumes that the CNV of interest is absent from the aggregate reference set. Hence related individuals should be excluded from the aggregate reference. It also means that ExomeDepth can miss common CNVs, if the call is also present in the aggregate reference. ExomeDepth is really suited to detect rare CNV calls (typically for rare Mendelian disorder analysis).
The ideas used in this package are of course not specific to exome sequencing and could be applied to other targeted sequencing datasets, as long as they contain a sufficiently large number of exons to estimate the parameters (at least 20 genes, say, but probably more would be useful). Also note that PCR based enrichment studies are often not well suited for this type of read depth analysis. The reason is that as the number of cycles is often set to a high number in order to equalize the representation of each amplicon, which can discard the CNV information.