Package: uga
Universal Genome AnalystUniversal Genome Analyst (uga) is an open, flexible, and efficient tool for the distribution, management, and visualization of whole genome data analyses.
It is designed to assist biomedical researchers in complex genomic data analysis through the use of a low level interface between the powerful R statistical
environment and Python, allowing for rapid integration of emerging analytical strategies. This project usesCythonfor a significant reduction in computation
time and researchers with access to a high performance computing cluster or with access to multiple cores will find time-saving features for parallel analysis
using a flexible, yet controlled, commandline interface.This software is currently under rapid development. Updates and bug fixes are being tracked on theuga github pageNotable FeaturesSingle variant association modeling (R base: lm, glm; Rgeepack: geeglm, RseqMeta: singlesnpMeta, Rlme4: lmer)Gene/Group based association modeling (with meta analysis: RseqMeta: burdenMeta, skatMeta, skatOMeta)Family based single variant association modelingPublication quality Q-Q and manhattan plotsGenomic control correctionPost modeling meta analysisRun multiple models as a single submission (variant names need not match)Alignment of compatible variants based on genomic position and both alleles (A/T and G/C SNVs are ambiguous and are assumed to be pre-aligned)Automatic job splitting (with job array queueing)Input data split by chromosome can be linked via wildcardAutomatically submit jobs on parallel computing systems usingqsubmultiple processor parallelization in addition to cluster parallelizationUser definable buffered reading for RAM usage controlVerification and compilation for parallel distributed jobsGzipandBgzip/Tabixmapped output where possible to save disc spacePlanned For Future ReleasesFull documentationAdditional association models (Rsurvival: coxph;nlme: lme)Family data inclusion in gene/group based testsCalculation for grouped analysis multiple test correctionRegion-based plots viaLocuszoomsoftwareResults annotation usingSnpEffSince parallel computing is sometimes unreliable, analysts need to be able to verify and possibly rerun failed jobs at various stages of the analysis.
In the interest of user efficiency and to avoid limitations induced by excessive automation, uga breaks the analytical process into the following modules.settingsuser definable settingssnvsingle variant statistical modelingsnvgroupgene/region-based statistical modelingmetameta-analysiscompileverify and compile split analysis resultsresubmitautomatically resubmit failed jobs for a projectsnvplotQ-Q and manhattan plots for snv testssnvgroupplotQ-Q and manhattan plots for snvgroup testsfilterfilter results / apply genomic control to resultsmergemerge and annotate results with external filestoolsrun any command line tool with ability to include genomic region automaticallyInstallationThis software uses a variety of Python modules, R packages, and some stand-alone software. Thus, the easiest method for installation is to use one of two platforms of the
softwareconda; eitherAnacondaorMiniconda.Also, consolidation and compression of data and results files requirestabix/bgzipandgzip.To prepare your system for uga, you need toclone an environment. You will need the included environment.yml file from the source code and a number of
packages frommy anaconda cloud channeland other custom channels (listed in the environment.yml file). After downloading the most recent
release (availablehere), use the following commands to begin the installation.For the sake of this tutorial, let’s assume the release version is ‘X’.>>> tar -xvf uga-X.tar.gz
>>> cd uga-XAt this point you may change the name of the environment to anything you’d prefer by modifying the first line of the environment.yml file. For these instructions, we will
assume the name is unchanged from ‘uga’.>>> conda env create -f environment.yml
>>> source activate ugaNow that your environment is activated, you are ready to install uga from source.>>> python setup.py installCutting Edge InstallKeeping up with the most current changes may be of interest to you as I will likely continue to add features and fix bugs on a regular basis. Thus, you may want to run a fork
of this repository rather than installing from source. See a tutorial describing how tofork this repository.Getting StartedIf you install uga under a conda environment, you need to source the environment as shown above before running any task in uga.>>> source activate ugaVerify that uga is functional using the following command to display help.>>> uga -hNote: further help is provided after selecting a specific module, ie.>>> uga snv -hParallel computingWhile you may simply run uga on a single cpu system, if you have access to a parallel computing cluster or even a single multiple core
processor, you will be able to take advantage of the self-managed parallel mode of use for which this software was designed.
This release was tested on a system which deploys Sun Grid Engine andqsubfor job management and will likely be compatible
with other PBS systems.CitationPlease cite this software as follows. A manuscript is in the works and yet to be submitted.Koesterer, Ryan. Universal Genome Analyst (uga).https://github.com/rmkoesterer/uga. DOI: 10.5281/zenodo.578712.ContactAuthor:Ryan KoestererPlease report any bugs or issues using theIssuestab on this page. I will respond to all concerns as quickly as possible.LicenseUniversal Genome Analyst (uga) is distributed under the GNU General Public License v3:Copyright (c) 2015 Ryan KoestererThis program is free software: you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see
<http://www.gnu.org/licenses/>