LowMACA-package: LowMACA : Low frequency Mutations Analysis via Consensus...

In LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

Description

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.

Details

LowMACA allows to collect, align, analyze and visualize mutations from different proteins or pfam domains.

1. newLowMACA: construct a LowMACA object with your proteins or pfam

2. setup: align sequences, get mutations and map mutations on the consensus sequence

3. entropy: calculate entropy score and pvalues for every position

4. lfm: retrieve significant position

5. lmPlot: visualize mutations on the consensus sequence, conservation and significant clusters

Author(s)

Stefano de Pretis , Giorgio Melloni

Maintainer: <[e-mail]> <[e-mail]>

References

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

See Also

LowMACA project website

Examples

#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)

LowMACA documentation built on Nov. 8, 2020, 8:14 p.m.