RGT 0.13.2-foss-2020a-Python-3.8.2Regulatory Genomics Toolbox is a python library and set of tools for the integrative analysis of high throughput regulatory genomics data.
Accessing RGT 0.13.2-foss-2020a-Python-3.8.2
To load the module for RGT 0.13.2-foss-2020a-Python-3.8.2 please use this command on the BEAR systems (BlueBEAR, BEAR Cloud VMs, and CaStLeS VMs):
module load RGT/0.13.2-foss-2020a-Python-3.8.2
BEAR Apps Version
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.
- BEAR Cloud and CaStLeS VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
To use rgt-tools you must run a few python scripts, after loading the RGT module:
This will create a folder rgtdata in your user directory due to the large amount of data you could potentially put in that folder it is best to move the folder into your project directory. To get the required genomic data do:
python rgtdata/setupGenomicData.py --all
--all can be exchanged for individual data sets.
- fisher 0.1.9
- hmmlearn 0.2.2
- logomaker 0.8
- natsort 7.1.1
- PyVCF 0.6.8
- PyX 0.15
- RGT 0.13.2
For more information visit the RGT website.
This version of RGT has a direct dependency on: Biopython/1.78-foss-2020a-Python-3.8.2 deepTools/3.5.0-foss-2020a-Python-3.8.2 foss/2020a HTSeq/0.13.5-foss-2020a-Python-3.8.2 Kent_tools/411-gompi-2020a Python/3.8.2-GCCcore-9.3.0 scikit-learn/0.23.2-foss-2020a-Python-3.8.2
Last modified on 22nd October 2021