Source: r-cran-shazam Maintainer: Debian R Packages Maintainers Uploaders: Steffen Moeller Section: gnu-r Testsuite: autopkgtest-pkg-r Priority: optional Build-Depends: debhelper (>= 12~), dh-r, r-base-dev, r-cran-ggplot2 (>= 2.0.0), r-cran-stringi (>= 1.1.3), r-cran-alakazam, r-cran-ape, r-cran-diptest, r-cran-doparallel, r-cran-dplyr (>= 0.5.0), r-cran-foreach, r-cran-igraph, r-cran-iterators, r-cran-kedd, r-cran-kernsmooth, r-cran-lazyeval, r-cran-mass, r-cran-progress, r-cran-sdmtools, r-cran-scales, r-cran-seqinr, r-cran-tidyr Standards-Version: 4.3.0 Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-shazam Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-shazam.git Homepage: https://cran.r-project.org/package=shazam Package: r-cran-shazam Architecture: all Depends: ${R:Depends}, ${misc:Depends} Recommends: ${R:Recommends} Suggests: ${R:Suggests} Description: 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.