recipe r-shazam

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) <doi:10.1093/bioinformatics/btv359>, Yaari, et al (2012) <doi:10.1093/nar/gks457>, Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.






package r-shazam

(downloads) docker_r-shazam



depends r-alakazam:


depends r-ape:

depends r-base:


depends r-diptest:

depends r-doparallel:

depends r-dplyr:


depends r-foreach:

depends r-ggplot2:


depends r-igraph:

depends r-iterators:

depends r-kedd:

depends r-kernsmooth:

depends r-lazyeval:

depends r-mass:

depends r-progress:

depends r-rlang:

depends r-scales:

depends r-seqinr:

depends r-stringi:


depends r-tidyr:

depends r-tidyselect:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install r-shazam

and update with::

   mamba update r-shazam

To create a new environment, run:

mamba create --name myenvname r-shazam

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `r-shazam/tags`_ for valid values for ``<tag>``)

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