1. kleinstein
  2. B Cell Repertoires
  3. shazam

Source

shazam /

Filename Size Date modified Message
R
data
data-raw
docs
inst
profiling
tests
vignettes
117 B
Created R file with code to install alakazam. Will use bitbucket if
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Added tag Version 0.1.8 - Mutation Profiling Enhancements for changeset adde99892b88
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Fixed a bug wherein `plotBaselineDensity` wouldn't plot if there was only a single category in the `idColumn`.
4.9 KB
added calculateMutability function...
17.5 KB
update news
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1. Fixed many brokens in `collapseClones()` and `calcClonalConsensus()`.
276 B
Switched to single R+python test image.
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rebuilt docs

SHazaM

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
analysis topics:

  1. Quantification of mutational load
    SHazaM includes methods for determine the rate of observed and expected mutations under various criteria. Mutational profiling criteria include rates under SHM targeting models, mutations specific to CDR and FWR regions, and physicochemical property dependent substitution rates.
  2. Statistical models of SHM targeting patterns
    Models of SHM may be divided into two independent components: (a) a mutability model that defines where mutations occur and (b) a nucleotide substitution model that defines the resulting mutation. Collectively these two components define an SHM targeting model. SHazaM provides empirically derived SHM 5-mer context mutation models for both humans and mice, as well tools to build SHM targeting models from data.
  3. Analysis of selection pressure using BASELINe
    The Bayesian Estimation of Antigen-driven Selection in Ig Sequences (BASELINe) method is a novel method for quantifying antigen-driven selection in high-throughput Ig sequence data. BASELINe uses SHM targeting models can be used to estimate the null distribution of expected mutation frequencies, and provide measures of selection pressure informed by known AID targeting biases.
  4. Model-dependent distance calculations
    SHazaM provides methods to compute evolutionary distances between sequences or set of sequences based on SHM targeting models. This information is particularly useful in understanding and defining clonal relationships.