gene-prediction

Introduction

This project is based on the 2008 paper 'Gene prediction in metagenomic fragments: A large scale machine learning approach' by Hoff et at. The goal of the gene_prediction_pipeline.ipynb notebook is to train the gene prediction algorithm using a 2-stage approach - linear discriminant and neural network - and provide predictions for sequences in an easy to use way. The linear discriminant model reduces high dimensional features and the neural network predicts if a given sequence is a gene or not. My intentions for starting this project were to gain a deeper understanding of machine learning approaches behind the Orphelia gene prediction algorithm and sharpen my ML/programming skills.

Contents

1. Preprocess Genome
   • extract coding sequences/noncoding sequences
   • shuffle/split data
2. Extract Features
   • monocodon (tricodon) frequency,
   • dicodon (hexcodon) frequency,
   • tis,
   • gc content
3. Train Linear Discriminant for Dimensional Reduction
4. Train Binary Neural Network for Gene Prediction
5. Predict Sequences (FASTA)
   • Prediction from Genome Sequence
   • Prediction from FASTA Input File

TODO

• Implement batch training (to overcome large amount of training data)

Setup

With Google Colab

First open a new Google Colab file. Mount your Google Drive and move to your working directory.

from google.colab import drive
drive.mount('/content/gdrive')

# Change working directory
%cd gdrive/MyDrive/

Next, clone this repository and move into the directory.

# Clone git repository (copy all files to Google Colab)
!git clone https://github.com/viggy-ravi/gene-prediction.git

# Go to gene-prediction folder
%cd gene-prediction/

Open the gene_prediction_pipeline.ipynb file and install the necessary dependendies. You will then be able to replicate the results from this notebook.