A Fully Automatic Method for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis
RNAcmap predictor requires only a standard computer with around 32 GB RAM to support the in-memory operations for RNAs sequence length less than 500.
RNAcmap has been tested on Ubuntu 14.04, 16.04, and 18.04 operating systems.
To install RNAcmap and it's dependencies following commands can be used in terminal:
git clone https://github.com/jaswindersingh2/RNAcmap.gitcd RNAcmap
Either follow virtualenv column steps or conda column steps to create virtual environment and to install RNAcmap python dependencies given in table below:
| virtualenv | conda | |
|---|---|---|
| 3. | virtualenv -p python3.6 venv_rnacmap |
conda create -n venv_rnacmap python=3.6 |
| 4. | source ./venv_rnacmap/bin/activate |
conda activate venv_rnacmap |
| 5. | pip install -r requirements.txt && deactivate |
while read p; do conda install --yes $p; done < requirements.txt && conda deactivate |
If Infernal tool is alread installed in the system, please add path to binary files in line no. 9 of 'run_rnacmap.sh' file. In case, Infernal tool is not installed in the system, please use follwing 2 command to download and extract it. In case of any problem and issue regarding Infernal download, please refer to Infernal webpage as following commands only tested on Ubuntu 18.04, 64 bit system.
wget 'eddylab.org/infernal/infernal-1.1.3-linux-intel-gcc.tar.gz'tar -xvzf infernal-*.tar.gz && rm infernal-*.tar.gz
If BLASTN tool is alread installed in the system, please add path to binary files in line no. 7 of 'run_rnacmap.sh' file. In case, BLASTN tool is not installed in the system, please use follwing 2 command to download and extract it. In case of any problem and issue regarding BLASTN download, please refer to BLASTN webpage as following commands only tested on Ubuntu 18.04, 64 bit system.
wget 'ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ncbi-blast-*+-x64-linux.tar.gz'tar -xvzf ncbi-blast-*+-x64-linux.tar.gz && rm ncbi-blast-*+-x64-linux.tar.gz
Either install RNAfold or SPOT-RNA predictor depending upon which Secondary Structure predictor you want to use. Installation of RNAfold will take 15-20 mins and 2-3 mins for SPOT-RNA. Both the secondary structure can be installed as well if you want to predict for both predictors. In case of issue regarding installation of these predictors, please refer to more specific and detailed guide for ViennaRNA and SPOT-RNA.
./install_RNAfold.shor/and./install_SPOT-RNA.sh
If NCBI's nt database already available in your system, please add path to database in line no. 8 and line 10 of 'run_rnacmap.sh' file. Otherwise, download the reference database (NCBI's nt database) for BLASTN and INFERNAL. The following command can used for NCBI's nt database. Make sure there is enough space on the system as NCBI's nt database is of size around 270 GB after extraction and it can take couple of hours to download depending on the internet speed. In case of any issue, please rerfer to NCBI's database website.
wget -c "ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nt.gz" -O ./nt_database/nt.gz && gunzip ./nt_database/nt.gz
This NCBI's database need to formated to use with BLASTN tool. To format the NCBI's database, the following command can be used. Please make sure system have enough space as formated database is of size around 120 GB in addition to appox. 270 GB from previous step and it can few hours for it.
./ncbi-blast-2.10.0+/bin/makeblastdb -in ./nt_database/nt -dbtype nucl
To install the DCA predictor, please run the following command:
./install_GREMLIN.shor/and./install_plmc.sh
To run the RNAcmap, the following command can be used. Use either RNAfold or SPOT-RNA for secondary structure predictor and one DCA method among GREMLIN, plmc, and mfDCA as input argument.
./run_rnacmap.sh inputs/sample_seq.fasta RNAfold/SPOT-RNA GREMLIN/plmc/mfDCA
The final output will be the "*.dca" file in the "outputs" folder consists of predicted Direct Coupling Analysis (DCA) by RNAcmap for a given input RNA sequence.
Zhang, T., Singh, J., Litfin, T., Zhan, J., Paliwal, K. and Zhou, Y., 2021. RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis. Bioinformatics, 37(20), pp.3494-3500.
[1] Nawrocki, E.P. and Eddy, S.R., 2013. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics, 29(22), pp.2933-2935..
[2] Hofacker, I.L., 2003. Vienna RNA secondary structure server. Nucleic acids research, 31(13), pp.3429-3431.
[3] H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne. (2000) The Protein Data Bank Nucleic Acids Research, 28: 235-242.
[4] Singh, J., Hanson, J., Paliwal, K. and Zhou, Y., 2019. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nature communications, 10(1), pp.1-13.
[5] Kamisetty, H., Ovchinnikov, S. and Baker, D., 2013. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era. Proceedings of the National Academy of Sciences, 110(39), pp.15674-15679.
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jaswinder.singh3@griffithuni.edu.au, tongchuan.zhang@griffithuni.edu.au, yaoqi.zhou@griffith.edu.au