https://github.com/bce99/m6a-rna-modification-prediction
:dna: A prediction pipeline of m6A RNA modifications on all SG-NEx direct RNA-Seq samples using Nextflow and FeedForward Neural Network
https://github.com/bce99/m6a-rna-modification-prediction
aws aws-s3 m6a-rna-modifications nextflow-pipeline rna-sequencing
Last synced: 2 months ago
JSON representation
:dna: A prediction pipeline of m6A RNA modifications on all SG-NEx direct RNA-Seq samples using Nextflow and FeedForward Neural Network
- Host: GitHub
- URL: https://github.com/bce99/m6a-rna-modification-prediction
- Owner: bce99
- Created: 2023-10-31T03:40:05.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-14T09:41:01.000Z (over 2 years ago)
- Last Synced: 2025-02-26T19:52:25.481Z (over 1 year ago)
- Topics: aws, aws-s3, m6a-rna-modifications, nextflow-pipeline, rna-sequencing
- Language: Jupyter Notebook
- Homepage: https://github.com/GoekeLab/sg-nex-data
- Size: 17.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# m6A-RNA-Modification-Prediction :dna:
**Table of contents:**
- [About The Project](#about-the-project)
- [Installation](#installation)
- [a) Create an isolated environment](#a-create-an-isolated-environment)
- [b) Setup a sync copy of the project repository](#b-setup-a-sync-copy-of-the-project-repository)
- [c) Run the test script](#c-run-the-test-script)
- [Acknowledgement](#acknowledgement)
## About The Project :page_with_curl:
m6A represents a chemical modification that can occur in RNA molecules, impacting their chemical and physical attributes, especially in the context of 5-mer nucleotides. This modification has been associated with the onset of cancer, highlighting its potential as a diagnostic tool for detecting cancer in its early stages. The project leverages data from Nanopore Sequencing, a technology capturing characteristic changes in current, known as RNA-Seq signals, and measuring dwelling time (signal length) as RNA molecules traverse the nanopore. The primary objective of this project is to employ advanced machine learning techniques to accurately predict and identify m6A modifications.
## Installation :desktop_computer:
To get a copy up and running on your AWS Ubuntu machine follow these simple steps. Here we use data2 as our test run data (We encourage you to use an instance with robust CPU settings)
### a) Create an isolated environment
Change directory into /ProjectStorage
```
cd ~/studies/ProjectStorage/
```
Download Miniconda Installer
```
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
```
Run Miniconda Installer
```
bash miniconda.sh -b -p $HOME/miniconda
```
Set up conda command in the current shell session
```
source $HOME/miniconda/etc/profile.d/conda.sh
```
Initialize Conda
```
conda init
```
Activate conda commands in the current terminal session
```
source ~/.bashrc
```
Create a virtual environment (You can replace myenv with any name you prefer)
```
conda create --name myenv python=3.8
```
Activate the virtual environment
```
conda activate myenv
```
### b) Setup a sync copy of the project repository
Clone the current repo
```
git clone https://github.com/bce99/m6A-RNA-Modification-Prediction.git
```
Change directory into the project folder
```
cd m6A-RNA-Modification-Prediction/
```
Install the required packages
```
pip install -r requirements.txt
```
### c) Run the test script
Test run the project (This may take some time depending on your CPU)
```
python run_test.py
```
After this you will see a new file 'Test_Data2_Result.csv' appearing in your current directory. This is the successfully generated test run prediction.
## Acknowledgement
We extend our heartfelt gratitude to Professor Jonathan Göke and Teaching Assistants Yuk Kei Wan and Cherry Li Chengchen for their invaluable guidance and insightful contributions. Their support immensely enriched our learning experience and significantly contributed to the success of our project.
We also express our appreciation to the entire Goekelab team for their remarkable work on the SG-NEx project, which played a pivotal role in the development of our own project.