ClusteredMutations (version 1.0.2)

showers: Location of clustered mutations in the cancer genome.

Description

showers() identifies all groups of closely spaced mutations using the anti-Robinson matrix. Hyper-mutated regions are defined as those segments containing a number (min = 6) or more mutations with a distance that is less than or equal to a number (max=1000) of bp, and referred to as mutation showers (Drake 2007a; Wang et al. 2007), clustered mutations (Drake 2007a; Drake 2007b; Roberts et al. 2012), or kataegis (from the Greek word for shower or thunderstorm) (Alexandrov et al. 2013; Nik-Zainal et al. 2012). showers() can be used to locate complex mutations (Roberts et al. 2012; Roberts et al. 2013) (min = 2; max=10).

Usage

showers(data = NULL, chr = NULL, position, min = 6, max = 5000)

Value

showers() returns a data set with all hyper-mutated zones in the DNA sequence of tumor cells.

chr

: the shower mutations data set contains seven variables: chromosome.

pend

: the last position in the chromosome of the mutation shower.

pstart

: the first position in the chromosome of the mutation shower.

nend

: the last number of a consecutive mutation shower.

nstart

: the first number of a consecutive mutation shower.

distance

: the length of a hyper-mutated zone and the number of mutations in the clustered mutation.

Arguments

data

: somatic substitution mutations of the cancer genome data set.

chr

: chromosome where the somatic mutation is located.

position

: position of somatic mutations in the DNA sequence of the cancer genome.

min

: a number min of consecutive mutations.

max

: a distance less than or equal to a number max of bp.

Author

David Lora.

Details

By default, showers() identifies the hyper-mutated zones (min = 6; max=5000). Complex mutations are those segments containing >= 2 consecutive mutations with a distance =< 100 bp.

References

Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature. 2013 Aug 22;500(7463):415-21.

Drake JW. Mutations in clusters and showers. Proc Natl Acad Sci U S A. 2007 May 15;104(20):8203-4.

Drake JW. Too many mutants with multiple mutations. Crit Rev Biochem Mol Biol. 2007 Jul-Aug;42(4):247-58.

Nik-Zainal S, Alexandrov LB, Wedge DC, et al; Breast Cancer Working Group of the International Cancer Genome Consortium. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012 May 25;149(5):979-93.

Roberts SA, Sterling J, Thompson C, et al. Clustered mutations in yeast and in human cancers can arise from damaged long single-strand DNA regions. Mol Cell. 2012 May 25;46(4):424-35.

Roberts SA, Lawrence MS, Klimczak LJ, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat Genet. 2013 Sep;45(9):970-6.

Wang J, Gonzalez KD, Scaringe WA, et al. Evidence for mutation showers. Proc Natl Acad Sci U S A. 2007 May 15;104(20):8403-8.

Examples

###Example 1:
example1<-c(1,101,201,299,301,306,307,317,318,320,418,518,528,628)
showers(position=example1, min=5, max=100)

###Example 2:
example2<-c(1,101,201,299,301,306,307,317,318,320,402,404,406,628)
showers(position=example2, min=5, max=100)

###Example 3:
# \donttest{
data(PD4107a)

###Locate the clustered mutations;
showers(data=PD4107a,chr=Chr,position=Position)

###Locate the complex mutations;
complex.showers<-showers(data=PD4107a,chr=Chr,position=Position,min=2,max=10)
nrow(complex.showers)
table(complex.showers$chr)
# }