hi,
currently, the VariantFiltering package works with GRanges objects
obtained from locateVariants() and predictCoding() to hold annotated
variants and add further annotations. However, I'd like to use 'VRanges'
objects which are, as far as i understand them, developed for exactly
the purpose of storing and manipulating variants and their annotations.
from the available documentation, it seems to me that the route for this
should coercing the 'VCF' object obtained with 'readVcf()' to 'VRanges'
via as(vcf, "VRanges"). When the input 'VCF' object has more than one
sample, this results in to a 'VRanges' object with the variants
replicated per different sample and a sample indicator column.
i was thinking that as one add annotations to variants the redundancy of
the information stored in a "multi-sample VRanges" which greatly
increase, so I was thinking to work having a minimal 'multi-sample
VRanges' with the sample-specific information and store the annotations
in a separate DataFrame object with some index column that would link to
the corresponding 'VRanges' "row".
i'd like to as you if you have thoughts, suggestions or comments about
this redundancy issue and this approach i'm thinking about.
btw, in the presence of multiple samples, i would find more natural to
coerce a 'VCF' object into a VRangesList object, instead of a VRanges
with a sample indicator column.
there is in fact the 'stackSamples()' method to compress a 'VRangesList'
into a 'VRanges' with a sample indicator column, however there is no
coercion method:
as(vcf, "VRangesList")
Error in as(vcf, "VRangesList") :
no method or default for coercing “CollapsedVCF” to “VRangesList”
i guess i can write some one-liner get a 'VRangesList' from a
multi-sample VCF with one 'VRanges' element per sample, but i wonder
whether it would not make sense to have this as an 'as(vcf,
"VRangesList")' method.
thanks!!!!
robert.
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