BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites

Overview

This repository contains the source code for paper "BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites". If you have questions, or you have problem using my tools on test other dataset， don't hesitate to open an issue or ask me via 121106022704@njust.edu.cn. We are happy to hear from you!

Setup Environment

This is an example for how to set up a working conda environment to run the code. In this example, we have cuda version==12.1, and we install torch==2.0.1.

To make sure the pyg packages are installed correctely, we directly install them from whl.

conda create --name BLAM6A python=3.8
conda activate BLAM6A

conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install --upgrade pip
pip install pandas numpy matplotlib seaborn scikit-learn gensim

Data

We provide the .fasta in data/ folder.

You can also generate the sequence yourself from the R script in data_prepare/ folder.

Training process

This File has included the process of extracting features, network training.

# type_name = ["FullTranscript", "matureRNA"]
# cell_name = ["A549","CD8T","Hek293_abacm","Hek293_sysy","HeLa","MOLM13"]
# for example
python train.py --type_name FullTranscript --cell_name A549
python blastn_process.py --type_name FullTranscript --cell_name A549 --task_name generate_fasta

The use of Blastn

Please firstly install the Blastn of version 2.14.0.

And you can process the result of Blastn.

We also provide the results generated by the Blastn tool in the Blastn folder.

cd data
#for example
cd FullTranscript+A549
makeblastdb -in train.fasta -dbtype nucl -out train -parse_seqids
blastn -db train -query test.fasta -word_size 4 -outfmt 6 -out test.out
cd ../..
python blastn_process.py --type_name FullTranscript --cell_name A549 --task_name blastn_results

Testing process

This file can predict the m6A sites in test.fasta according provided type_name and cell_name.

# for example
python test.py --type_name FullTranscript --cell_name A549 --if_blastn True
# This will generate a .csv file in result File to save the predicted values.