gemma 0.97+dfsg-3 source package in Ubuntu

Changelog

gemma (0.97+dfsg-3) unstable; urgency=medium

  * Team upload.
  * Add autopkgtest.
  * Add patch for reproducible builds.

 -- Dylan Aïssi <email address hidden>  Fri, 15 Jun 2018 21:46:35 +0200

Upload details

Uploaded by:
Debian Med on 2018-06-16
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Disco release on 2018-10-30 universe misc
Cosmic release on 2018-06-16 universe misc

Downloads

File Size SHA-256 Checksum
gemma_0.97+dfsg-3.dsc 2.1 KiB e0203aa008f957032be52b55ff5e3dbb9fe9382aa5425b0bd9c2439fe42e375a
gemma_0.97+dfsg.orig.tar.gz 38.7 MiB 46bc34a111c34003d8bfc9724fe701d8a5a27c9aacb872d32ba85633b0f58403
gemma_0.97+dfsg-3.debian.tar.xz 4.7 KiB de99e54452e2c2ea260b57e9d7fe6e78026a11f2c984dc501b6d591a54a58c82

Available diffs

No changes file available.

Binary packages built by this source

gemma: Genome-wide Efficient Mixed Model Association

 GEMMA is the software implementing the Genome-wide Efficient Mixed
 Model Association algorithm for a standard linear mixed model and some
 of its close relatives for genome-wide association studies (GWAS):
 .
  * It fits a univariate linear mixed model (LMM) for marker association
    tests with a single phenotype to account for population stratification
    and sample structure, and for estimating the proportion of variance in
    phenotypes explained (PVE) by typed genotypes (i.e. "chip heritability").
  * It fits a multivariate linear mixed model (mvLMM) for testing marker
    associations with multiple phenotypes simultaneously while controlling
    for population stratification, and for estimating genetic correlations
    among complex phenotypes.
  * It fits a Bayesian sparse linear mixed model (BSLMM) using Markov
    chain Monte Carlo (MCMC) for estimating PVE by typed genotypes,
    predicting phenotypes, and identifying associated markers by jointly
    modeling all markers while controlling for population structure.
  * It estimates variance component/chip heritability, and partitions
    it by different SNP functional categories. In particular, it uses HE
    regression or REML AI algorithm to estimate variance components when
    individual-level data are available. It uses MQS to estimate variance
    components when only summary statisics are available.
 .
 GEMMA is computationally efficient for large scale GWAS and uses freely
 available open-source numerical libraries.

gemma-dbgsym: debug symbols for gemma
gemma-doc: Example folder for GEMMA

 This package ships example data for the Genome-wide Efficient Mixed
 Model Association.