- recipe rrmscorer
RRMScorer (RRM-RNA score predictor) predicts how likely a single RRM is to bind ssRNA
- Homepage:
- Documentation:
- Developer docs:
- License:
MIT / MIT
- Recipe:
- Links:
RRMScorer (RRM-RNA score predictor) allows the user to easily predict how likely a single RRM is to bind ssRNA using a carefully generated alignment for the RRM structures in complex with RNA, from which we analyzed the interaction patterns and derived the scores (please address to the publication for more details on the method REF).
RRMScorer has several features to either calculate the binding score for a specific RRM and RNA sequences, for a set of RRM sequences in a FASTA file, or to explore which are the best RNA binders according to our scoring method.
RRMScorer has been developed by Bio2Byte within the RNAct project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813239.
Wim Vranken, Bio2Byte group within the RNAct project, VUB, Belgium.
- package rrmscorer¶
- Versions:
1.0.11-0,1.0.10-0,1.0.9-0,1.0.8-0- Depends:
on biopython
on hmmer
on logomaker
on matplotlib-base
on numpy
on pandas
on python
>=3.9on requests
on scikit-learn
on seaborn-base
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install rrmscorer
to add into an existing workspace instead, run:
pixi add rrmscorer
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install rrmscorer
Alternatively, to install into a new environment, run:
conda create -n envname rrmscorer
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/rrmscorer:<tag>
(see rrmscorer/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Notes¶
More details are available from the publication related to this work. Please also reference this publication if you use this code:
Roca-Martínez J, Dhondge H, Sattler M, Vranken WF (2023) Deciphering the RRM-RNA recognition code: A computational analysis. PLOS Computational Biology 19(1): e1010859.
DOI: https://doi.org/10.1371/journal.pcbi.1010859
Download stats¶
Link to this page¶
Render an 
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[](http://bioconda.github.io/recipes/rrmscorer/README.html)
