The sequencing center we work with recently started to report variants for random chromosomes, we now end up with variants (whole exome) that have been mapped to one of the chrrandom. I would first ask for a good explanation of this chrrandom issue and secondly can anyone explain how to approach the analysis when it comes to these randomchr based variants? Should I just disregard them? I fear it would be hard to find many downstream annotations for these positions in the public databases like dbsnp? I am sorry for the question is not specific but I think I basically need chrrandom and exome sequencing 101 type of an answer or please direct me to a good read on this...
For what I know, these are the contigs of genome that are not quite sure the exact position. Because there are many factors effect the assembling of genome, so some contigs the consortium didn't integarate with whole genome, just labeled as chrUn_ (not sure which chromosome come from) or chr1__random (from chr1 already known). And for hg19, there are patches released when the consortium integerate the contig with genome (in hg19, the coordinates are reversed for contigs, so in the version hg19, the integeration patch doesn't effect the already sequence coordinates).
More information you may check this:
For what I checked, some chrUn contigs have also some variants of rRNAs or such things. So I think you'd better exclude chr__random firstly, because the annotation is just duplication of the known annotated, so the result may be false positive, and acutally perhaps we should mapped the reads as variants to the annoatation record of reference choromosomes.
I am also working with NGS data analysis and although my work is more focussed on TF binding and urs is more focussed on SNP analysis.
We generally/mostly discard this Chr_rand in order to avoid any ambiguity in our data analysis.
In order to check how this Chr_rand is skewing your data just perform two analysis on same dataset one with chr_rand regions and one without and browse them to genome browser and you will see the difference.