Calling structural variants (CNVs) with single-end reads

[agwe]

Hi,

I would like to hear if anyone tried calling structural variants using single-end data. It seems that most of the tools available for calling structural variants are only using paired-end data. I know that it should be possible to at least detect CNVs using single-end. Perhaps I should also add that my data is from targeted resequencing, so I would ideally like to be able to specify ranges of a given chromosome for which I want to determine the CNVs. Does anyone know about any suitable program for this?

[HESmith]

It might be possible to use split-end alignment to generate pseudo paired-end reads, which could then be used to identify novel junctions (indels and rearrangements). The citation for this approach is here.

[agwe]

Thanks, it looks interesting. However I am mostly interested in detection of gains or losses in certain regions, perhaps by simple comparing the number of reads falling in a given region and somehow correcting for GC contents or low complexity sequence regions.

[ForeignMan]

Hi,

you may have a look at tools like CNVseq, rdxplorer or freec. They call CNV from read depth. I know that freec also normalizes by GC content. But I'm not sure how well it's gonna work with targeted sequencing. All tools I know and mentioned only refer to whole genome sequencing data. Guess this case may need some special treatment, because of the different read distribution. But, nevertheless, this may be some good lecture to start with. I'd be interested too, how well CNV estimation works with targeted (e.g. exome) sequencing.

[gprakhar]

Hello agwe,

CNVseq requires a paired normal, so you can use it only if you have one.
RDXplorer is good and same for Freec.

According to analysis done in our lab about performance of CNV callers, CNVnator has turned out to be the best.
Only catch is you would have to be care full about the Exon Intron junctions, rest it performs very good.

Regards,
Prakhar

[simobioinfo]

Hi,
I'm working with targeted Ion torrent PGM data, that is a small panel of genes.
I would like to know if there are methods to identify CNVs from such a kind of data.
Thank you in advance