This repository contains pipelines to process RNA-seq data. You can download the latest version from the releases page and a history of version changes is in the CHANGELOG.
These features are explained in more detail later in this README.
Description pending.
If your system has everything installed, run the examples like this:
cd examples
looper run test_config.yaml -d
Prerequisite python packages. This pipeline uses pypiper to run a single sample, looper to handle multi-sample projects (for either local or cluster computation), and pararead for parallel processing sequence reads. You can do a user-specific install of these like this:
pip install --user https://github.com/databio/pypiper/zipball/master
pip install --user https://github.com/pepkit/looper/zipball/master
pip install --user https://github.com/databio/pararead/zipball/master
Required executables. You will need some common bioinformatics tools installed. The list is specified in the pipeline configuration files (.yaml files in src/).
Genome resources. This pipeline requires genome assemblies produced by refgenie. You may download pre-indexed references or you may index your own (see refgenie instructions).
Clone the pipeline. Clone this repository using one of these methods:
- using SSH:
git clone [email protected]:databio/rnapipe.git - using HTTPS:
git clone https://github.com/databio/rnapipe.git
There are two configuration options: You can either set up environment variables to fit the default configuration, or change the configuration file to fit your environment. Choose one:
Option 1: Default configuration (recommended; .yaml files in src/).
- Make sure the executable tools (java, samtools, bowtie2, etc.) are in your PATH.
- Set up environment variables to point to
jarfiles for the java tools (picardandtrimmomatic).
export PICARD="/path/to/picard.jar"
export TRIMMOMATIC="/path/to/trimmomatic.jar"
- Define environment variable
GENOMESfor refgenie genomes.
export GENOMES="/path/to/genomes/folder/"
Option 2: Custom configuration. Instead, you can also put absolute paths to each tool or resource in the configuration file to fit your local setup. Just change the pipeline configuration file (.yaml files in src/) appropriately.
You never need to interface with the pipeline directly, but you can if you want. Just run python src/SCRIPTNAME.py -h to see usage. But the best way to use this pipeline is to run it using looper. You will need to tell looper about your project. Example project data are in the examples/ folder. Run the pipeline across all samples in the test project with this command:
looper run examples/test_config.yaml
If the looper executable in not your $PATH, add the following line to your .bashrc or .profile:
export PATH=$PATH:~/.local/bin
Now, adapt the example project to your project. Here's a quick start: You need to build two files for your project (follow examples in the examples/ folder):
- project config file -- describes output locations, pointers to data, etc.
- sample annotation file -- comma-separated value (CSV) list of your samples.
Your annotation file must specify these columns:
- sample_name
- library
- organism
- read1
- read2
- whatever else you want
Run your project as above, by passing your project config file to looper run. More detailed instructions and advanced options for how to define your project are in the Looper documentation on defining a project. Of particular interest may be the section on using looper derived columns.
Once you've specified your project to work with this pipeline, you will also inherit all the power of looper for your project. You can submit these jobs to a cluster with a simple change to your configuration file. Follow instructions in configuring looper to use a cluster.
Looper can also summarize your results, monitor your runs, clean intermediate files to save disk space, and more. You can find additional details on what you can do with this in the looper docs.
Pull requests welcome. Active development should occur in a development or feature branch.