This repository accompanies the article: "NCYPred: A Bidirectional LSTM Network with Attention for Y RNA and short non-coding RNA classification", published in the journal IEEE/ACM Transactions on Computational Biology and Bioinformatics (https://ieeexplore.ieee.org/document/9627779).
If you use this work, please cite: "D. De Souza Lima, L. J. A. Amichi, A. A. Constantino, M. A. Fernandez and F. A. V. Seixas, "NCYPred: A Bidirectional LSTM Network with Attention for Y RNA and short non-coding RNA classification," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2021.3131136."
Python 3.7
Dependencies:
- pandas 1.1.1
- biopython 1.78
- numpy 1.19
- tensorflow 2.3+
Usage:
python predict.py -i input_file.fasta -o output_file
NCYPred is a deep learning model trained to classify non-coding RNA transcripts into 13 distinct classes: 5.8S rRNA, 5S rRNA, CD-box, HACA-box, Intron-gpI, Intron-gpII, Leader, miRNA, Riboswitch, Ribozyme, tRNA, Y RNA (vertebrates), sbRNA and CeY RNA (nematodes), sbRNA (insects), Y RNA like (bacterial). The model receives as input a non-coding RNA sequence in FASTA format and outputs the predicted class. NCYPred was trained on a dataset consisting of 31,000 non-coding RNA sequences obtained from Rfam 14.0 (https://rfam.xfam.org/). It is capable of extracting features directly from nucleotides sequences, learn sequence representations and classify them in 1 of 13 classes.
The model architecture was built based on bidirectional long short-term memory networks (biLSTM) with attention mechanism, using TensorFlow and Keras. Before feeding it to the neural network, input sequences are preprocessed: I) Sequences are decomposed into overlapping 3-mers (subsequences of 3 nt); II) Each unique 3-mer is “tokenized”. III) Sequences are concatenated with zeros until the maximum length is reached (500 nt). After this process, input sequences are fed to an Embedding layer, which maps each token into a 10-dimensional representation that is optimized during training. Then, the biLSTM layers encode sequential information, and the Attention layer assigns importance to each position, embedding the most relevant information into a context vector. This final sequence representation is used by a 128-layer feed-forward neural network to classify each sequence into 1 of 13 classes.
NCYPred is also available at: https://www.gpea.uem.br/ncypred/
NCYPred web-server allows the user to send RNA strings (single or multiple) in FASTA format. If you want to get forecast results by email, please provide your email address. Sequences can be sent via a file or by inserting the RNA sequences in the text box. If both are sent, only the file will be considered and the informed text will be ignored.
The dataset used to train NCYPred is available in: /dataset/dataset-NCYPred/
Full dataset:
Rfam-5000-undersample-full-dataset.csv
Training set: training-set-rfam-corrected.csv
Validation set: validation-set-rfam-corrected.csv
Species count: species-count.xlsx
The nRC-dataset used in this work is available in: /dataset/dataset-nRC/
Training set: dataset_Rfam_6320_13classes.fasta
Validation set: dataset_Rfam_validated_2600_13classes.fasta
Dataset analysis: nRC-dataset.ipynb
Source: http://tblab.pa.icar.cnr.it/public/nRC/paper_dataset/
NCYPred: The NCYPred model is available in: /models/NCYPred_model/ This model was trained using the biLSTM + Attention architecture, using TensorFlow 2.3. The Attention layer used in this work was adapted based on the implementation created by Christos Baziotis (https://gist.github.com/cbaziotis/6428df359af27d58078ca5ed9792bd6d).
biLSTM: The model trained using the biLSTM architecture, without the Attention layer, is available in: /models/biLSTM_model/
Code used in this work is available as Jupyter Notebooks.
Training code: /notebooks/NCYPred_training.ipynb
Validation code: /notebooks/NCYPred_validation.ipynb
biLSTM + Attention validation on the nRC dataset: /notebooks/biLSTMAttention-nRC-validation.ipynb
additional methods described in the paper:
Profile Hidden Markov Models (nhmmer): /notebooks/HMM-profile-model.ipynb
Alignment based prediction (nhmmer): /notebooks/alignment-based-model.ipynb