createTargetingModel {shazam}

Creates a TargetingModel

Description

createTargetingModel creates a 5-mer TargetingModel.

Usage

createTargetingModel(
  db,
  model = c("s", "rs"),
  sequenceColumn = "sequence_alignment",
  germlineColumn = "germline_alignment_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, "s", builds a model by counting only silent mutations. model="s" should be used for data that includes functional sequences. Setting model="rs" creates a model by counting both replacement and silent mutations and may be used on fully non-functional sequence data sets.

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.

Details

Caution: The targeting model functions do NOT support ambiguous characters in their inputs. You MUST make sure that your input and germline sequences do NOT contain ambiguous characters (especially if they are clonal consensuses returned from collapseClones).

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.

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.

Examples

# Subset example data to one isotype and sample as a demo
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call == "IGHA" & sample_id == "-1h")[1:80,]

# Create model using only silent mutations and ignore multiple mutations
model <- createTargetingModel(db, model="s", sequenceColumn="sequence_alignment",
                              germlineColumn="germline_alignment_d_mask",
                              vCallColumn="v_call", multipleMutation="ignore")

# View top 5 mutability estimates
head(sort(model@mutability, decreasing=TRUE), 5)

# View number of silent mutations used for estimating mutability
model@numMutS