Immunoglobulin Somatic Hypermutation Analysis

Provides a computational framework for analyzing mutations in immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of antigen-driven selection pressure, mutational load quantification, building of somatic hypermutation (SHM) models, and model-dependent distance calculations. Also includes empirically derived models of SHM for both mice and humans. Citations: Gupta and Vander Heiden, et al (2015) , Yaari, et al (2012) , Yaari, et al (2013) , Cui, et al (2016) .


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.

Contact

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Reference manual

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install.packages("shazam")

1.4.0 by Susanna Marquez, 13 days ago


https://shazam.readthedocs.io


Report a bug at https://github.com/immcantation/shazam/issues


Browse source code at https://github.com/cran/shazam


Authors: Mohamed Uduman [aut] , Namita Gupta [aut] , Susanna Marquez [aut, cre] , Julian Zhou [aut] , Nima Nouri [aut] , Noah Yann Lee [aut] , Ang Cui [ctb] , Jason Vander Heiden [aut] , Gur Yaari [aut] , Steven Kleinstein [aut, cph]


Documentation:   PDF Manual  


AGPL-3 license


Imports alakazam, ape, diptest, doParallel, dplyr, foreach, graphics, grid, igraph, iterators, KernSmooth, lazyeval, MASS, methods, parallel, progress, rlang, scales, seqinr, stats, stringi, tidyr, tidyselect, utils

Depends on ggplot2

Suggests knitr, rmarkdown, testthat, tibble


Imported by dowser, scoper.


See at CRAN