- recipe rrmscorer
RRMScorer (RRM-RNA score predictor) predicts how likely a single RRM is to bind ssRNA
- Developer docs:
GPL3 / GNU General Public v3 (GPLv3)
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¶
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install rrmscorer and update with:: mamba update rrmscorer
To create a new environment, run:
mamba create --name myenvname rrmscorer
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).
Alternatively, use the docker container:
docker pull quay.io/biocontainers/rrmscorer:<tag> (see `rrmscorer/tags`_ for valid values for ``<tag>``)
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.