Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling
Hardware Requirments: It is recommended that your system should have 32 GB RAM, 500 GB disk space to support the in-memory operations for RNA sequence length less than 500. Multiple CPU threads are also recommended as the MSA generating process is computationally expensive.
Software Requirments:
- Python3.6
- Perl-5.4 or later
- Anaconda or Miniconda
- CUDA 10.0 (Optional if running on GPU)
- cuDNN (>= 7.4.1) (Optional if running on GPU)
SPOT-RNA-2D has been tested on Ubuntu 14.04, 16.04, and 18.04 operating systems.
Clone SPOT-RNA-2D github repo:
git clone https://github.com/jaswindersingh2/SPOT-RNA-2D.git && cd SPOT-RNA-2D
To create Conda virtual environment:
conda create -n venv_spotrna_2d python=3.6 && conda activate venv_spotrna_2d
To install dependencies:
while read p; do conda install --yes $p; done < requirements.txt
To install RNAfold predictor for base pair probability features:
conda install -c bioconda viennarna
To install BLAST-N and INFERNAL tools for mulitple-sequence-alignment search:
conda install -c bioconda blastconda install -c bioconda infernalconda install -c bioconda easel
To install PLMC for DCA features:
git clone https://github.com/debbiemarkslab/plmc && cd plmc && make all-openmp && cd -
SPOT-RNA-2D-Single only requires RNA sequence in directory mentioned with flag --input_feats and list of name of RNA sequences with flag --list_rna_ids. Set --single_seq flag to 1 for single-sequence-based prediction.
./run.py --list_rna_ids datasets/TS1_ids --input_feats input_features/ --single_seq 1 --outputs outputs/
To run SPOT-RNA-2D-Single for 1 RNA sequence instead of multiple RNAs:
./run.py --rna_id 6ol3_C --input_feats input_features/ --single_seq 1 --outputs outputs/
SPOT-RNA-2D require RNAcmap based MSA features (PSSM, DCA) as an input. RNAcmap is computationally expensive and require NCBI's database of size around 400 GB. To obtain RNAcmap based features, following command can be used. For first run, it can take several hours to few days as it will download, unzip, and format NCBI's database to use with BLAST-N and INFERNAL.
./run_rnacmap.sh input_features/1eiy_C
Above command creates a folder 1eiy_C_features in input file directory (input_features/ in this case). 1eiy_C_features/ contains all alignment files (MSA-1, MSA-2) and features (PSSM and DCA) generated from RNAcmap pipeline.
./run.py --rna_id 1eiy_C --input_feats input_features/1eiy_C_features/ --outputs outputs/
If RNAcmap based features (PSSM and DCA) for mutiple RNAs already exists than SPOT-RNA-2D can be run for batch of sequences as follows:
./run.py --list_rna_ids datasets/TS1_ids --input_feats input_features/ --outputs outputs/
For more options:
./run.py --help
usage: run.py [-h] [--rna_id] [--list_rna_ids] [--input_feats] [--single_seq]
[--outputs] [--gpu] [--cpu]
optional arguments:
-h, --help show this help message and exit
--rna_id name of RNA sequence file; default = 6p2h_A
--list_rna_ids file consists of list name of RNA sequence files for batch
prediction; default = datasets/TS1_ids
--input_feats Path to directory consists of input features files with the
same name as specified with --rna_id flag; default =
inputs_features/
--single_seq set equal to 1 for SPOT-RNA-2D-Single; default = 0 for
SPOT-RNA-2D
--outputs Path to directory to save output files; default = outputs/
--gpu To run on GPU, specifiy GPU number. If only one GPU in
computer specifiy 0; default = -1 (no GPU)
--cpu Specify number of cpu threads that program can use; default
= 16
Datasets used for training, validation, and testing is available in datasets folder of this repo.
Refer to benchmarking folder of this repo.
https://github.com/tlitfin/SPOT-RNA-2D-tertiary
- cmbuild, cmcalibrate, and cmsearch from INFERNAL tool version 1.1.4
- esl-reformat from easel tool version 0.48
- blastn and makeblastdb from BLAST tool version 2.11.0
- RNAfold from ViennaRNA version 2.4.18
- utils/reformat.pl from HHsuite-github-repo
- utils/getpssm.pl and utils/parse_blastn_local.pl from RNAsol standalone program
- utils/seqkit from seqkit toolkit
- PLMC from plmc-github-repo
If use SPOT-RNA-2D for your research, please cite the following papers:
Singh, J., Paliwal, K., Litfin, T., Singh, J. and Zhou, Y., 2022. Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling. Bioinformatics, 38(16), pp.3900-3910.
If use SPOT-RNA-2D input feature pipeline, please consider citing the following papers:
RNAcmap Pipeline:
[1] 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.
RNAfold features:
[2] Lorenz, R., Bernhart, S.H., Zu Siederdissen, C.H., Tafer, H., Flamm, C., Stadler, P.F. and Hofacker, I.L., 2011. ViennaRNA Package 2.0. Algorithms for molecular biology, 6(1), pp.1-14.
INFERNAL:
[3] Nawrocki, E.P. and Eddy, S.R., 2013. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics, 29(22), pp.2933-2935.
BLAST-N:
[4] Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D.J., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research, 25(17), pp.3389-3402.
SeqKit:
[5] Shen, W., Le, S., Li, Y. and Hu, F., 2016. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PloS one, 11(10), p.e0163962.
PLMC:
[6] Hopf, T.A., Ingraham, J.B., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C. and Marks, D.S., 2017. Mutation effects predicted from sequence co-variation. Nature biotechnology, 35(2), pp.128-135.
If use SPOT-RNA-2D datasets, please consider citing the following papers:
Protein Data Bank (PDB):
[7] Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N. and Bourne, P.E., 2000. The protein data bank. Nucleic acids research, 28(1), pp.235-242.
CD-HIT-EST:
[8] Fu, L., Niu, B., Zhu, Z., Wu, S. and Li, W., 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23), pp.3150-3152.
SPOT-RNA-1D:
[9] Singh, J., Paliwal, K., Singh, J. and Zhou, Y., 2021. RNA backbone torsion and pseudotorsion angle prediction using dilated convolutional neural networks. Journal of Chemical Information and Modeling.
Mozilla Public License 2.0
jaswinder.singh3@griffithuni.edu.au, yaoqi.zhou@griffith.edu.au