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Source: rcranshazam
Maintainer: Debian R Packages Maintainers <rpkgteam@aliothlists.debian.net>
Uploaders: Steffen Moeller <moeller@debian.org>
Section: gnur
Testsuite: autopkgtestpkgr
Priority: optional
BuildDepends: debhelper (>= 12~),
dhr,
rbasedev,
rcranggplot2 (>= 2.0.0),
rcranstringi (>= 1.1.3),
rcranalakazam,
rcranape,
rcrandiptest,
rcrandoparallel,
rcrandplyr (>= 0.5.0),
rcranforeach,
rcranigraph,
rcraniterators,
rcrankedd,
rcrankernsmooth,
rcranlazyeval,
rcranmass,
rcranprogress,
rcransdmtools,
rcranscales,
rcranseqinr,
rcrantidyr
StandardsVersion: 4.3.0
VcsBrowser: https://salsa.debian.org/rpkgteam/rcranshazam
VcsGit: https://salsa.debian.org/rpkgteam/rcranshazam.git
Homepage: https://cran.rproject.org/package=shazam
Package: rcranshazam
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
antigendriven 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 modeldependent distance comparisons of Ig repertoires.
.
SHazaM is part of the Immcantation analysis framework for Adaptive
Immune Receptor Repertoire sequencing (AIRRseq) 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 5mer 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 Antigendriven Selection in Ig Sequences
(BASELINe) method is a novel method for quantifying antigendriven
selection in highthroughput 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.
* Modeldependent 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.
