Provides a computational framework for Bayesian estimation of antigen-driven selection in immunoglobulin (Ig) sequences, providing an intuitive means of analyzing selection by quantifying the degree of selective pressure. Also provides tools to profile mutations in Ig sequences, build models of somatic hypermutation (SHM) in Ig sequences, and make model-dependent distance comparisons of Ig repertoires.
SHazaM is part of the Immcantation
analysis framework for Adaptive Immune Receptor Repertoire sequencing
(AIRR-seq) and provides tools for advanced analysis of somatic hypermutation
(SHM) in immunoglobulin (Ig) sequences. Shazam focuses on the following
createMutabilityMatrix(), enabling parameter tuning for
InfluenzaDbdata object, in favor of the updated
ExampleDbprovided in alakazam 0.2.4.
distToNearest()which allows restriction of distances to only distances across samples (ie, excludes within-sample distances).
distToNearest(), which will return all distances to neighboring nodes in a minimum spanning tree.
shmulateTree()to simulate mutations on sequences and lineage trees, respectively, using a 5-mer targeting model.
groupBaseline()multiple times resulted in incorrect normalization.
testBaseline()function to test the significance of differences between two selection distributions.
dplyr::tbl_dfobject instead of a
distToNearest()did not return the nearest neighbor with a non-zero distance.
MUTATIONS_POLARITYproviding alternate approaches to defining replacement and silent annotations to mutations when calling
regionDefinition=NULLconsistent for all mutation profiling functions. Now the entire sequence is used as the region and calculations are made accordingly.
calcDBObservedMutations()returns R and S mutations also when
regionDefinition=NULL. Older versions reported the sum of R and S mutations. The function will add the columns
symmetryparameter to distToNearest to change behavior of how asymmetric distances (A->B != B->A) are combined to get distance between A and B.
minNumMutationsparameter to createSubstitutionMatrix. This is the minimum number of observed 5-mers required for the substituion model. The substitution rate of 5-mers with fewer number of observed mutations will be inferred from other 5-mers.
minNumSeqMutationsparameter to createMutabilityMatrix. This is the minimum number of mutations required in sequences containing the 5-mers of interest. The mutability of 5-mers with fewer number of observed mutations in the sequences will be inferred.
returnModelparameter to createSubstitutionMatrix. This gives user the option to return 1-mer or 5-mer model.
returnSourceparameter to createMutabilityMatrix. If TRUE, the code will return a data frame indicating whether each 5-mer mutability is observed or inferred.
Initial public release.
Influenza.tabfile did not load on Mac OS X.
HS1FDistance, based on the Yaari et al, 2013 data.
hs1fas the default distance model for
calcDBClonalConsensus()so that the function now works correctly when called with the argument
calcDBObservedMutations(), which enables return of mutation frequencies rather the default of mutation counts.
M3NModeland all options for using said model.
createMutabilityMatrix()where IMGT gaps were not being handled.
U5NModel, which is a uniform 5-mer model.
Prerelease for review.