hackquest logo

DNAStoreAI

Our project uses an ML-powered encoder to transform digital data into DNA sequences. It features a user-friendly web interface for encoding, decoding, downloading, and displaying metrics like GC conte

Videos

Tech Stack

React
Python
Tailwind
Flask

Description

I’ve built a smart web application that allows users to convert digital data into DNA sequences, and then decode it back — all through a simple, intuitive interface.

Here’s how it works:

  • You can either type your message or upload a file.

  • Behind the scenes, my system uses machine learning to optimize how this data is encoded into DNA — avoiding error-prone patterns and ensuring the DNA is stable and efficient.

  • The system also tracks the quality of your DNA sequence, such as GC content and size, and allows you to download it in a .dna file format — just like downloading a document.

  • On the other side, there’s a DNA Decoder page, where you can paste or upload a DNA sequence and instantly get your original data back.

Not only does this project demonstrate a real-world application of machine learning in biology, but it also showcases how software can simulate the futuristic idea of DNA-based data storage — all with user-friendly visuals, metrics, and downloadable results.

Whether you're a biologist, a tech enthusiast, or just curious about how we might store data in the future, this tool offers a glimpse into how biology and AI can come together in beautiful, useful ways.

Progress During Hackathon

We began by finalizing the concept: a web app that encodes and decodes digital data into DNA sequences using machine learning for optimization. We structured the project into two main modules — an Encoder and a Decoder — and designed the frontend with React and Tailwind CSS for a clean user experience. On the frontend, we implemented file input, text input, user detail fields, and output sections with stats like GC content and sequence length. Users can download both encoded and decoded files, and toggle between ML-based and traditional encoding. For the backend, we set up a Flask API and integrated an SQLite database to store user submissions and encoding history. We also implemented an ML-based encoder that generates biologically stable sequences by balancing GC content and avoiding error-prone patterns. We tested the full pipeline, ensured proper error handling, and created a functional, user-friendly application that demonstrates the potential of DNA as a data storage medium enhanced by AI.
Team Leader
SSouvik Mandal
Project Link
Sector
AISocialFiOther