Omics Pipe is a Python framework for automating ‘best practice’ next generation sequencing pipelines. Omics Pipe can be run from the command-line by providing it with a YAML parameter file specifying your directory structure and software specific parameters. This executes a parallel automated pipeline on a Distributed Resource Management system (local cluster or Amazon Web Services (AWS)) that efficiently handles job resource allocation, monitoring and restarting. The goals of Omics Pipe are to provide researchers with an open-source computational solution to implement ‘best practice’ pipelines with minimal development overhead and providing visual outputs to aid the researcher in biological interpretation.
To install Omics Pipe, first determine if you are going to be using it on a local compute cluster or on AWS. If you are going to be installing it on your local cluster, follow the directions below (or have your system administrator install it globally). If you are going to create a local installation in your home directory on your cluster but you do not have administrative permissions, you can create a Python Virtual Environment and then follow the instructions below within the virtual environment.
HPC Cluster or AWS Star Cluster (Resource Requirements)
Python >=2.6
Third Party Software Dependencies (Third Party Software Dependencies)
Reference Databases (Reference Databases Needed)
Option 1: Install from pypi using pip:
pip install Omics Pipe
Option 2: Install from pypi using easy_install:
easy_install Omics Pipe
Option 3: Install from source: Download/extract the source code and run:
python setup.py install
Option 4: Install the latest code directly from the repository:
pip install -e hg+https://bitbucket.org/sulab/Omics Pipe#egg=Omics Pipe
Once you have successfully installed Omics Pipe, you can run a pipeline by typing the command:
Omics Pipe [-h] [--custom_script_path CUSTOM_SCRIPT_PATH]
[--custom_script_name CUSTOM_SCRIPT_NAME]
[--compression {gzip, bzip}]
{RNAseq_Tuxedo, RNAseq_count_based, RNAseq_cancer_report, RNAseq_TCGA, RNAseq_TCGA_counts, Tumorseq_MUTECT, miRNAseq_count_based, miRNAseq_tuxedo, WES_GATK, WGS_GATK, SomaticInDels, ChIPseq_MACS, ChIPseq_HOMER, custom}
parameter_file