See motifbreakR vignette for an introduction to motifbreakR
See help("motifbreakR") for detailed help with running motifbreakR.
See help("plotMB") for detailed help with visualization.
Functional annotation represents a key step toward the understanding and interpretation of germline and somatic variation as revealed by genome wide association studies (GWAS) and The Cancer Genome Atlas (TCGA), respectively. GWAS have revealed numerous genetic risk variants residing in non-coding DNA associated with complex diseases. For sequences that lie within enhancers or promoters of transcription, it is straightforward to assess the effects of variants on likely transcription factor binding sites. We introduce motifbreakR, which allows the biologist to judge whether the sequence surrounding a polymorphism or mutation is a good match, and how much information is gained or lost in one allele of the polymorphism relative to another or mutation vs. wildtype. MotifbreakR is flexible, giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum, 2) log-probabilities, and 3) relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within bioconductor.
# Install prerequisite packages from Bioconductor
source("http://bioconductor.org/biocLite.R")
biocLite(c("BiocParallel", "motifStack", "BSgenome", "BiocGenerics",
"Biostrings", "GenomeInfoDb", "GenomicRanges", "Gviz", "S4Vectors",
"rtracklayer", "IRanges", "MotifDb", "BSgenome.Hsapiens.UCSC.hg19",
"SNPlocs.Hsapiens.dbSNP.20120608", "SNPlocs.Hsapiens.dbSNP142.GRCh37"))
# Install motifbreakR from github
install.packages("devtools")
devtools::install_github("Simon-Coetzee/motifBreakR")