Immunoglobulin Somatic Hypermutation Analysis


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 analysis topics:

 * 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.
 * Statistical models of SHM targeting patterns
   Models of SHM may be divided into two independent components:
    1) a mutability model that defines where mutations occur and
    2) 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.
 * 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.
 * 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.

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