Package: sincfold
sincFoldThis is the repository for sincFold, a new RNA folding prediction tool based on deep learning.SincFold is a fast and accurate RNA secondary structure prediction method. It is an end-to-end approach that predicts the contact matrix using only the sequence of nucleotides as input. The model is based on a residual neural network that can learn short and long context interactions. Extensive experiments on several benchmark datasets were made, comparing sincFold against classical methods and new models based on deep learning. We demonstrate that sincFold achieves the best performance in comparison with state-of-the-art methods.A summary of results can be seen inthis notebook.Folding RNA sequencesWe have aweb demo(mirror) running with the latest version. This server admits one sequence at a time. We provide a model pre-trained with validated RNA datasets (ArchiveII, RNAstralign, URS-PDB). Please follow the next instructions if you want to run the model locally.InstallThis is a Python package. It is recommended to use virtualenv or conda to create a new environment. To install the package, run:pip install sincfoldAlternativelly, you can clone the repository with:git clone https://github.com/sinc-lab/sincFold
cd sincFold/and install with:pip install .on Windows, you will probably need to add the python scripts folder to the PATH.Predicting sequencesTo predict the secondary structure of a sequence using the pretrained weights:sincFold pred AACCGGGUCAGGUCCGGAAGGAAGCAGCCCUAAThis will display the predicted dot-bracket in the console.SincFold also supports files with multiple sequences in .csv and .fasta format as inputs, and providing .csv or .ct format outputs.!echo -e ">seq1\\nAACCGGGUCAGGUCCGGAAGGAAGCAGCCCUAA" > sample.fasta
!echo -e ">seq2\\nGUAGUCGUGGCCGAGUGGUUAAGGCGAUGGACUAGAAAUCCAUUGGGGUCUCCCCGCGCAGGUUCGAAUCCUGCCGACUACGCCA" >> sample.fasta

sincFold pred sample.fasta -o pred_ct_files/We also providethis notebookto run the sincFold functions.Training and testing modelsA new model can be trained using thetrainoption. For example, download this training set:wget "https://raw.githubusercontent.com/sinc-lab/sincFold/main/sample/train.csv"and then run sincFold with:sincFold -d cuda train train.csv -n 10 -o output_pathThe option "-d cuda" requires a GPU (otherwise remove it), and -n limits the maximum number of epochs to get a quick result. The output log and trained model will be saved in the directoryoutput_path.Then, a different test set can be evaluated with thetestoption. You can download this sample file form:wget "https://raw.githubusercontent.com/sinc-lab/sincFold/main/sample/test.csv"and test the model with:sincFold test test.csv -w output_path/weights.pmtThe model path (-w) is optional, if omitted the pretrained weights are used.Reproducible researchYou can run the complete train and test scheme using the following code (in this case set up benchmarkII and fold 0 data partition).importosimportpandasaspdout_path=f"working_path/"os.mkdir(out_path)# read dataset and predefined partitions (the files are available in this repository)dataset=pd.read_csv("data/benchmarkII.csv",index_col="id")partitions=pd.read_csv("data/benchmarkII_splits.csv")dataset.loc[partitions[(partitions.fold_number==0)&(partitions.partition=="train")].id].to_csv(out_path+"train.csv")dataset.loc[partitions[(partitions.fold_number==0)&(partitions.partition=="valid")].id].to_csv(out_path+"valid.csv")dataset.loc[partitions[(partitions.fold_number==0)&(partitions.partition=="test")].id].to_csv(out_path+"test.csv")then call the training and testing functionssincFold -d cuda train working_path/train.csv --valid_file working_path/valid.csv -o working_path/output/

sincFold -d cuda test working_path/test.csv -w working_path/output/weights.pmtUsing a GPU for training is recommended (with the option '-d cuda'). The complete process may take about 3hs using a RTX A5000.@article{sincFold2023,title={sincFold: end-to-end learning of short- and long-range interactions for RNA folding},author={Leandro A. Bugnon and Leandro Di Persia and Matias Gerard and Jonathan Raad andSantiago Prochetto and Emilio Fenoy and Uciel Chorostecki and Federico Ariel andGeorgina Stegmayer and Diego H. Milone},journal={under review},year={2023}}