createTargetingModel - Creates a TargetingModel
createTargetingModel creates a 5-mer
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 )
- data.frame containing sequence data.
- 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.
- name of the column containing IMGT-gapped sample sequences.
- name of the column containing IMGT-gapped germline sequences.
- name of the column containing the V-segment allele calls.
- 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.
- 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.
- 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.
- name of the model.
- description of the model and its source data.
- genus and species of the source sequencing data.
- publication source.
- date the model was built. If
NULLthe current date will be used.
A TargetingModel object.
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
- 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.
# Subset example data to one isotype and sample as a demo data(ExampleDb, package="alakazam") db <- subset(ExampleDb, c_call == "IGHA" & sample_id == "-1h") # 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")
Warning:Insufficient number of mutations to infer some 5-mers. Filled with 0.
# View top 5 mutability estimates head(sort(model@mutability, decreasing=TRUE), 5)
AAGTA ACGTA AGGTA CAGTA CCGTA 0.01480406 0.01480406 0.01480406 0.01480406 0.01480406
# View number of silent mutations used for estimating mutability model@numMutS
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.