This tool can automatically construct interrupting and restoring silent mutation pairs within coding regions for combinatorial in silico analysis of RNA-RNA interactions. The predictions can be used for in vitro and in vivo experiments.
To run silentMutations, I recommend using Miniconda and following the steps below. If this is the first time using conda, you should probably restart your shell after the installation of Miniconda. The following will demonstrate the installation and set up of Miniconda on Linux, which should be similar on other platforms. For Windows 10 users, I advise using the Ubuntu 20.04 LTS subsystem. More information can be found on the Miniconda and Bioconda pages.
Installing Miniconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
Updating Miniconda and setting channels:
conda update conda
conda update python
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
Installing Conda packages:
conda create --name silentMutations python=3.9.7
conda activate silentMutations
conda install -c bioconda viennarna=2.5.0
conda install -c conda-forge numpy=1.22.2
conda install -c lb_arrakistx varna=3.93
git clone https://github.com/desiro/silentMutations.git
cd silentMutations
Installing the ViennaRNA package on Linux:
tar -zxvf ViennaRNA-2.5.0.tar.gz
cd ViennaRNA-2.5.0
./configure --with-python3
make
sudo make install
Installing the ViennaRNA package on MAC:
tar -zxvf ViennaRNA-2.5.0.tar.gz
cd ViennaRNA-2.5.0
./configure --enable-universal-binary --with-python3
make
sudo make install
The basic input for silentMutations.py includes the following parameters:
-p- name of the output folder-f- input fasta file, will only read the first two sequences-s1 <name>:<frame>:<start>-<end>- for the first sequence-s2 <name>:<frame>:<start>-<end>- for the second sequence
<name> is an arbitrary name for the sequence that will appear in the results
<frame> is the frame shift of the coding sequence with respect to the genome reading frame (Frame 1: 0; Frame 2: 1; Frame 3: 2)
<start>-<end> are the nucleotide positions to be included in the predictions (3-5 of AGCUA would be CUA)
python silentMutations.py -p example -f example.fa -s1 seq1:2:20-36 -s2 seq2:0:23-44 -cls ssRNA- -r -c -thr=4
python silentMutations.py -p example -f example.fa -s1 seq1:2:20-36 -s2 seq2:0:23-44 -cls ssRNA- -r -c -thr=4 -var VARNAv3-93.jar
For more command line options, see the manual.
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
Please cite SilentMutations if you find our tool useful:
D. Desirò, M. Hölzer, B. Ibrahim and M. Marz.
"SilentMutations (SIM): A tool for analyzing long-range RNA–RNA interactions in viral genomes and structured RNAs."
Virus Research, 260:135-141, 2019.
There is also an open access version at bioRxiv.