r-cran-shazam 0.1.10-1 source package in Ubuntu

Changelog

r-cran-shazam (0.1.10-1) unstable; urgency=medium

  * Team upload.
  * New upstream version

 -- Andreas Tille <email address hidden>  Sun, 23 Sep 2018 22:56:36 +0200

Upload details

Uploaded by:
Debian R Packages Maintainers on 2018-09-24
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Disco release on 2018-11-10 universe misc

Builds

Disco: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-shazam_0.1.10-1.dsc 2.3 KiB 9ae781623432b98851bc3785834175be3884d20ed5922587f390c2485b1ebce1
r-cran-shazam_0.1.10.orig.tar.gz 1.7 MiB 01c5a8b3b8a9125aa6406bb957c9b6bb3d082c668012942ef988148da0140de6
r-cran-shazam_0.1.10-1.debian.tar.xz 8.2 KiB 248a6a81bb5ee04cb2ce3e6380b592863157bf5e93aa6d063744d7679f34a98f

No changes file available.

Binary packages built by this source

r-cran-shazam: 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.