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Universal RObust Peak Annotator Anaconda-Server Badge Docker Badge PyPI version

The Universal RObust Peak Annotator (UROPA) is a command line based tool, intended for genomic region annotation. Based on a configuration file, different target features can be prioritized with multiple integrated queries. These can be sensitive for feature type, distance, strand specificity, feature attributes (eg. protein_coding) or the anchor position relative to the feature. UROPA can incorporate reference annotation files (GTF) from different sources, like Gencode, Ensembl, or RefSeq, as well as custom reference files produced by the user.

Features

  • Detect the most appropriate annotation with flexible parameter keys that allow robustness and simple customization, such as

    • feature type
    • feature anchor
    • feature direction relative to peak location
    • filter for attribute values, e.g. “protein_coding”
    • strand specificity
  • Utilization of all available GTF files as annotation database

  • One run with variable sets of parameters by multiple queries

  • Graduated annotation due to priorization

  • Different easily-readable output tables (allhits, finalfits, besthits).

  • Visual summary for annotation evaluation

  • Preparation of custom annotation files

Documentation

A detailed description of how to apply UROPA to your data can be found here.

Installation and Command-line usage

Conda package manager

We recommend to install UROPA using the conda package manager. Make sure to have conda installed, e.g. via

  • Miniconda
    • download the Miniconda installer for Python 2.7
    • run bash Miniconda2-latest-Linux-x86_64.sh to install Miniconda
    • Answer the question "Do you wish the installer to prepend the Miniconda install location to PATH in your /home/.../.bashrc ?" with yes OR do PATH=dir/to/miniconda2:$PATH after installation process

The UROPA installation is now as easy as conda install -c bioconda uropa.

Biocontainers / Docker

If you have a running Docker environment, you can pull a biocontainer with UROPA and all dependencies via docker pull quay.io/biocontainers/uropa

Installation from source

You can also install UROPA from the source PyPI package. Note that this comes without the R dependencies for auxillary scripts:

pip install uropa

To fulfill all other dependencies, follow the instructions below:

  • R/Rscript (v3.3.0 or higher; follow instructions on url)
    • install required packages step by step:
     install.packages(c("ggplot2", "devtools", "gplots", "gridExtra", "jsonlite", "VennDiagram", "getopt", "tidyr", "UpSetR"))
     source("https://bioconductor.org/biocLite.R")
     biocLite(c("RBGL", "graph"))

In order to plot the Chow-Ruskey plot with uropa_summary.R, install the modified Vennerable package from our fork:

library(devtools)
install_github("jenzopr/Vennerable")

Usage

To effectively use UROPA, make yourself familiar with the command-line options:

$ uropa                   
Usage: uropa [options]          

Available options:

	-h, --help             	print this help message and further details on the configuration file
  -i, --input            	filename of configuration file [mandatory]
  -p, --prefix           	prefix for output files, can include subdirectories [basename of --input]
  -r, --reformat         	create an additional compact and line-reduced table as result file
  -s, --summary          	additional visualisation of results in graphical format will be created
  -t n, --threads n      	multiprocessed run: n = number of threads to run annotation process
  -add-comments          	show comment lines in output files explaining the columns
  -l, --log              	log file name for messages and warnings
  -d, --debug            	print verbose messages (for debugging purposes)
  -v, --version          	print the version and exit

How to cite

Kondili M, Fust A, Preussner J, Kuenne C, Braun T, and Looso M. UROPA: a tool for Universal RObust Peak Annotation. Scientific Reports 7 (2017), doi: 10.1038/s41598-017-02464-y

Contribute

Support

If you have any questions please feel free to contact Mario Looso (mario.looso@mpi-bn.mpg.de).

License

The project is licensed under the MIT License.