mutationsR

mutationsR is a tool to manipulate mutation data

Installation

You can install the development version of mutationsR like so:

#### installing the dependencies
# install pacman
if (!require("pacman")) install.packages("pacman")
# install dependencies from CRAN
pacman::p_install(c(devtools, ggplot2, dplyr, ggpubr))
# install Biostrings
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("Biostrings")
# install graphicsPLr from GitHub
if (!require("graphicsPLr")) devtools::install_github('Phuong-Le/graphicsPLr')

# the package
devtools::install_github('Phuong-Le/mutationsR')

Example

Manipulating mutations as strings, you can get the central mutations from their k-mer context, get the wildtype sequence and get reverse complementary sequences.

library(mutationsR)
## 
## getting the central mutation
get_mut('A[C>T]G')
#> [1] "C>T"
get_mut('C>T')
#> [1] "C>T"
## getting the wildtype mutation
get_wt_seq('A[C>T]G')
#> [1] "ACG"
## getting reverse complementary mutations
rv_context('A[C>G]T')
#> [1] "A[G>C]T"
# this can be expanded so that all mutations from C and T are remained, and all mutations from A and G are converted to their reverse complementary counterparts
strand_symmetric('A[C>G]T')
#> [1] "A[C>G]T"
strand_symmetric('A[A>G]T')
#> [1] "A[T>C]T"

If you have a MAF or VCF file (mutation data provided by the ICGC portal), you can compute the k-mer mutations, given the reference genome sequence like so - note that the columns have to be in this order: donor_id, chromosome, chrom_start, chrom_end, reference_genome_allele, mutated_from_base, mutated_to_base

library(mutationsR)
if (requireNamespace("seqinr", quietly = TRUE)) {
  mut_dt = data.frame(
    donor_id = c('PD1', 'PD2'),
    chromosome = c('3', 'X'),
    chrom_start = c(5, 7),
    chrom_end = c(5, 7),
    reference_genome_allele = c('A', 'C'),
    mutated_from_base = c('A', 'C'),
    mutated_to_base = c('T', 'A')
  )
  seq = seqinr::s2c('AGCTAGCTGA')
  get_context = get_context_param(seq, k = 3)
  apply(mut_dt, MARGIN = 1, get_context)
}
#> [1] "T[A>T]G" "G[C>A]T"

option to use the simplified version of VCF - note that the columns have to be in this order: donor_id (SampleID), chromosome (Chr), position (Pos), reference_base (Ref), mutated_base (Alt)

library(mutationsR)
if (requireNamespace("seqinr", quietly = TRUE)) {
  mut_dt = data.frame(
    SampleID = c("PD1", "PD2"),
    Chr = c("3", "X"),
    Pos = c(5, 7),
    Ref = c("A", "C"),
    Alt = c("T", "A")
  )
  seq = seqinr::s2c("AGCTAGCTGA")
  get_context = get_context_param(seq, k = 3, format_ = 'simplified')
  apply(mut_dt, MARGIN = 1, get_context)
}
#> [1] "T[A>T]G" "G[C>A]T"

If you need to get all mutation contexts, given a k-mer size, do the following (this is currently strand symmetric only)

library(mutationsR)
gen_contexts(1)
#> [1] "C>A" "C>G" "C>T" "T>A" "T>C" "T>G"
gen_contexts(3)
#>  [1] "A[C>A]A" "A[C>A]C" "A[C>A]G" "A[C>A]T" "C[C>A]A" "C[C>A]C" "C[C>A]G"
#>  [8] "C[C>A]T" "G[C>A]A" "G[C>A]C" "G[C>A]G" "G[C>A]T" "T[C>A]A" "T[C>A]C"
#> [15] "T[C>A]G" "T[C>A]T" "A[C>G]A" "A[C>G]C" "A[C>G]G" "A[C>G]T" "C[C>G]A"
#> [22] "C[C>G]C" "C[C>G]G" "C[C>G]T" "G[C>G]A" "G[C>G]C" "G[C>G]G" "G[C>G]T"
#> [29] "T[C>G]A" "T[C>G]C" "T[C>G]G" "T[C>G]T" "A[C>T]A" "A[C>T]C" "A[C>T]G"
#> [36] "A[C>T]T" "C[C>T]A" "C[C>T]C" "C[C>T]G" "C[C>T]T" "G[C>T]A" "G[C>T]C"
#> [43] "G[C>T]G" "G[C>T]T" "T[C>T]A" "T[C>T]C" "T[C>T]G" "T[C>T]T" "A[T>A]A"
#> [50] "A[T>A]C" "A[T>A]G" "A[T>A]T" "C[T>A]A" "C[T>A]C" "C[T>A]G" "C[T>A]T"
#> [57] "G[T>A]A" "G[T>A]C" "G[T>A]G" "G[T>A]T" "T[T>A]A" "T[T>A]C" "T[T>A]G"
#> [64] "T[T>A]T" "A[T>C]A" "A[T>C]C" "A[T>C]G" "A[T>C]T" "C[T>C]A" "C[T>C]C"
#> [71] "C[T>C]G" "C[T>C]T" "G[T>C]A" "G[T>C]C" "G[T>C]G" "G[T>C]T" "T[T>C]A"
#> [78] "T[T>C]C" "T[T>C]G" "T[T>C]T" "A[T>G]A" "A[T>G]C" "A[T>G]G" "A[T>G]T"
#> [85] "C[T>G]A" "C[T>G]C" "C[T>G]G" "C[T>G]T" "G[T>G]A" "G[T>G]C" "G[T>G]G"
#> [92] "G[T>G]T" "T[T>G]A" "T[T>G]C" "T[T>G]G" "T[T>G]T"

Mutation spectra plotting

You can also plot mutation spectra, for example

library(mutationsR)
kmer = gen_contexts(3)
value = 1:96
spectra_dt = data.frame(kmer = kmer, value = value)
spectra_plot(spectra_dt)

Mutational signatures and phylogenetics

The package also provides several functions to analyse mutations together with phylogenetic trees they are associated with.

library(mutationsR)

plot_signature_by_branches(mut_tree, exposure_by_branch)
#> Registered S3 method overwritten by 'ggtree':
#>   method      from 
#>   identify.gg ggfun

Besides, it is also possible to reconstruct the mutations on the sample level if the only data available is the branches with tree_to_samples