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The BeviMed package comes with a script containing
functions to simplify reading allele count matrices from VCF files. The
functions depend on tabix, but have the advantage of
allowing variants in local regions to be read in, reducing the amount of
memory consumed at any one time. However, if you want to analyse many
regions, it may be more efficient to read in larger parts of the file -
in which case, a package such as vcfR might be more
appropriate.
In order to make the functions available, we must source the script:
The script creates the function vcf2matrix, which
depends on the external program tabix (available from http://www.htslib.org/download/) for reading allele
count matrices from VCF files. It uses arguments:
vcf_file_name - path to vcf file.chr - character value giving
chromosome.from/to - integer values
giving from/to coordinates for chromosome.samples - character vector of sample names
as used in the VCF.include_variant_info - boolean value
determining whether to return just a matrix of allele counts
(TRUE, default) or a list of allele count matrix
G and data.frame of variant information
info (FALSE). The variant information
info could be useful for filtering the variants, for
example if the VCF has not been pre-filtered for rare variants.description_columns - integer value giving
number of columns of description fields in the VCF file (i.e. before the
genotype columns begin), defaults to 9.warn_if_AF_greater_than - numeric value
giving threshold allele frequency for generating a warning.You can invoke the function simply to obtain the allele count matrix
and pass straight to bevimed, along with phenotype
label:
ac_matrix <- vcf2matrix("my-vcf.vcf.gz", chr="2", from=1, to=1e4)
pheno <- read.table(file="my-phenotype-data.txt", header=TRUE)
bevimed(y=pheno$disease_status, G=ac_matrix)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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