Nanopore-Signal-Anomaly-Detection

Inspired from Dr. Sikic GIS publications

The Project: A Signal Processing Approach to Pre-processing Raw Nanopore Data.

The "Big Question" : Can classical signal processing techniques, improve the quality of raw Nanopore data before it's fed into a complex AI model like Rockfish?

Project Overview

This project aims to explore the application of classical signal processing techniques to enhance the quality of raw Nanopore data. The goal is to determine whether these techniques can effectively pre-process the data before it is analyzed by complex AI models, such as Rockfish.

The project will involve the development of a pre-processing pipeline that applies various signal processing methods to raw Nanopore data, followed by an evaluation of the impact on AI model performance.

Objectives

1. To apply classical signal processing techniques to raw Nanopore data.
2. To develop a pre-processing pipeline that prepares the data for AI models.
3. To document the methods and results of the project comprehensively.

Key Components

1. Signal Processing Techniques: Implement classical signal processing methods to filter and enhance raw Nanopore data.
2. Documentation: Maintain comprehensive documentation of the methods, results, and findings throughout the project.

Expected Outcomes

• Improved quality of raw Nanopore data through classical signal processing techniques.
• Enhanced performance of AI models like Rockfish when trained on pre-processed data.
• A detailed report on the effectiveness of signal processing methods in the context of Nanopore data