adjustedCADD.annotation.SNVs: SNVs annotation with adjusted CADD scores

Description

Annotate SNVs with the adjusted CADD scores (CADD PHRED scores for coding, regulatory and intergenic regions)

Usage

adjustedCADD.annotation.SNVs(x, variant.scores = NULL, 
			     cores = 10, verbose = T, path.data)

Value

The bed matrix x with adjusted CADD scores in adjCADD.

Arguments

x: A bed.matrix annotated with CADD regions using set.CADDregions
variant.scores: A dataframe containing the ADJUSTED CADD scores of the SNVs (Optional, useful to gain in computation time if the adjusted CADD scores of variants in the study are available)
cores: How many cores to use, set at 10 by default
verbose: Whether to display information about the function actions
path.data: The repository where data for RAVA-FIRST are or will be downloaded from https://lysine.univ-brest.fr/RAVA-FIRST/

Details

SNVs are directly annotated with the adjusted CADD scores in the function using the file "AdjustedCADD_v1.4_202108.tsv.gz" downloaded from https://lysine.univ-brest.fr/RAVA-FIRST/ in the repository of the package Ravages or the scores of variants can be provided to variant.scores to gain in computation time (this file should contain 5 columns: the chromosome ('chr'), position ('pos'), reference allele ('A1'), alternative allele ('A2') and adjusted CADD scores ('adjCADD').

Those adjusted scores are used in the RAVA.FIRST() pipeline to filter rare variants.

As this function can take time when a large number of SNVs are present, it is recommended to use this function chromosome by chromosome for large datasets or to fitler the bed matrix before the annotation.

Examples

Run this code

#Import 1000Genome data from region around LCT gene
#x <- as.bed.matrix(LCT.gen, LCT.fam, LCT.bim)

#Annotate variants with adjusted CADD score
#x <- adjustedCADD.annotation.SNVs(x)