shazam (version 0.1.2)

createMutabilityMatrix: Builds a mutability model

Description

createMutabilityMatrix builds a 5-mer nucleotide mutability model by counting the number of mutations occuring in the center position for all 5-mer motifs.

Usage

createMutabilityMatrix(db, substitutionModel, model = c("RS", "S"), sequenceColumn = "SEQUENCE_IMGT", germlineColumn = "GERMLINE_IMGT_D_MASK", vCallColumn = "V_CALL", multipleMutation = c("independent", "ignore"), minNumSeqMutations = 500, returnSource = FALSE)

Arguments

db

data.frame containing sequence data.

substitutionModel

matrix of 5-mer substitution rates built by createSubstitutionMatrix.

model

type of model to create. The default model, "RS", creates a model by counting both replacement and silent mutations. The "S" specification builds a model by counting only silent mutations.

sequenceColumn

name of the column containing IMGT-gapped sample sequences.

germlineColumn

name of the column containing IMGT-gapped germline sequences.

vCallColumn

name of the column containing the V-segment allele call.

multipleMutation

string specifying how to handle multiple mutations occuring within the same 5-mer. If "independent" then multiple mutations within the same 5-mer are counted indepedently. If "ignore" then 5-mers with multiple mutations are excluded from the total mutation tally.

minNumSeqMutations

minimum number of mutations in sequences containing each 5-mer to compute the mutability rates. If the number is smaller than this threshold, the mutability for the 5-mer will be inferred. Default is 500.

returnSource

return the sources of 5-mer mutabilities (measured vs. inferred). Default is false.

Value

A named numeric vector of 1024 normalized mutability rates for each 5-mer motif with names defining the 5-mer nucleotide sequence.

References

1. Yaari G, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol. 2013 4(November):358.

See Also

Other targeting model functions: calcTargetingDistance, createSubstitutionMatrix, createTargetingMatrix, createTargetingModel, extendMutabilityMatrix, extendSubstitutionMatrix, plotMutability, writeTargetingDistance

Examples

# Subset example data to one isotype and sample as a demo
db <- subset(InfluenzaDb, CPRIMER == "IGHA" & BARCODE == "RL014")

# Create model using only silent mutations and ignore multiple mutations
sub_model <- createSubstitutionMatrix(db, model="S", multipleMutation="ignore")
mut_model <- createMutabilityMatrix(db, sub_model, model="S", multipleMutation="ignore",
                                    minNumSeqMutations=10)