createTargetingModel - Creates a TargetingModel

Description

createTargetingModel creates a 5-mer TargetingModel.

Usage

createTargetingModel(db, model = c("RS", "S"),
sequenceColumn = "SEQUENCE_IMGT", germlineColumn = "GERMLINE_IMGT_D_MASK",
vCallColumn = "V_CALL", multipleMutation = c("independent", "ignore"),
minNumMutations = 50, minNumSeqMutations = 500, modelName = "",
modelDescription = "", modelSpecies = "", modelCitation = "",
modelDate = NULL)

Arguments

db
data.frame containing sequence data.
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 calls.
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 otal mutation tally.
minNumMutations
minimum number of mutations required to compute the 5-mer substitution rates. If the number of mutations for a 5-mer is below this threshold, its substitution rates will be estimated from neighboring 5-mers. Default is 50.
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.
modelName
name of the model.
modelDescription
description of the model and its source data.
modelSpecies
genus and species of the source sequencing data.
modelCitation
publication source.
modelDate
date the model was built. If NULL the current date will be used.

Value

A TargetingModel object.

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.

Examples

# Subset example data to one isotype and sample as a demo
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, ISOTYPE == "IgA" & SAMPLE == "-1h")

# Create model using only silent mutations and ignore multiple mutations
model <- createTargetingModel(db, model="S", multipleMutation="ignore")

Warning:Insufficient number of mutations to infer some 5-mers. Filled with 0.

See also

See TargetingModel for the return object. See plotMutability plotting a mutability model. See createSubstitutionMatrix, extendSubstitutionMatrix, createMutabilityMatrix, extendMutabilityMatrix and createTargetingMatrix for component steps in building a model.