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
- 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: (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.
- 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.
Depends: ggplot2, stringi
Imports: alakazam, ape, diptest, doParallel, dplyr, foreach, graphics, grid, igraph, iterators, kedd, KernSmooth, lazyeval, MASS, methods, parallel, progress, rlang, scales, seqinr, stats, tidyr, utils
Suggests: knitr, rmarkdown, testthat
To cite the SHazaM package in publications, please use:
Gupta N, Vander Heiden J, Uduman M, Gadala-Maria D, Yaari G, Kleinstein S (2015). “Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.” Bioinformatics, 1-3. doi: 10.1093/bioinformatics/btv359 (URL: https://doi.org/10.1093/bioinformatics/btv359).
To cite the selection analysis methods, please use:
Yaari G, Uduman M, Kleinstein S (2012). “Quantifying selection in high-throughput Immunoglobulin sequencing data sets.” Nucleic acids research, 40(17), e134. doi: 10.1093/nar/gks457 (URL: https://doi.org/10.1093/nar/gks457).
To cite the HH_S5F model and the targeting model generation methods, please use:
Yaari G, Vander Heiden J, Uduman M, Gadala-Maria D, Gupta N, Stern J, O’Connor K, Hafler D, Lasserson U, Vigneault F, Kleinstein S (2013). “Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data.” Frontiers in Immunology, 4(358), 1-11. doi: 10.3389/fimmu.2013.00358 (URL: https://doi.org/10.3389/fimmu.2013.00358).
To cite the HKL_S1F, HKL_S5F, MK_RS1NF, and MK_RS5NF models, please use:
Cui A, Di Niro R, Vander Heiden J, Briggs A, Adams K, Gilbert T, O’Connor K, Vigneault F, Shlomchik M, Kleinstein S (2016). “A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data.” The Journal of Immunology, 197(9), 3566-3574. doi: 10.4049/jimmunol.1502263 (URL: https://doi.org/10.4049/jimmunol.1502263).