The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest.
LowMACA allows to collect, align, analyze and visualize mutations from different proteins or pfam domains.
newLowMACA: construct a LowMACA object with your proteins or pfam
setup: align sequences, get mutations and map mutations on the consensus sequence
entropy: calculate entropy score and pvalues for every position
lfm: retrieve significant position
lmPlot: visualize mutations on the consensus sequence, conservation and significant clusters
Stefano de Pretis , Giorgio Melloni
Maintainer: <email@example.com> <firstname.lastname@example.org>
Melloni GEM, de Pretis S, Riva L, et al. LowMACA: exploiting protein family analysis for the identification of rare driver mutations in cancer. BMC Bioinformatics. 2016;17:80. doi:10.1186/s12859-016-0935-7
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#Create an object of class LowMACA for RAS domain family lm <- newLowMACA(pfam="PF00071" , genes=c("KRAS" , "NRAS" , "HRAS")) #Select melanoma, breast cancer and colorectal cancer lmParams(lm)$tumor_type <- c("skcm" , "brca" , "coadread") #Align sequences, get mutation data and map them on consensus lm <- setup(lm) #Calculate statistics lm <- entropy(lm) #Retrieve original mutations lfm(lm) #Plot bpAll(lm) lmPlot(lm) protter(lm)
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