MutaMind

With just one click, MutaMind identifies genetic mutations in several DNA sequences along with information relating to each mutation and their associated genetic disorders.

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Inspiration

DNA is complex genetic material. One change in DNA can change the inner workings - and life - of an entire being. Mutamind was inspired by the lifelong disorders many individuals around the world endure - disorders wired into their DNA. We hope that zooming in to locate these little changes in DNA, which cause genetic disorders, will allow us to take one step closer to easier mutation locating.

What it does

An individual inputs a human gene and gene sequence (for a specific gene) that they want analyzed. The software will then compare the inputted sequence to normal homo sapien sequences for that gene. If the sequence inputted includes a mutation, the system will locate that mutation and inform the user of the likely genetic disorders a mutation in that location is associated with. The user will then be able to learn the overview of the condition(s) caused by that mutation.

How we built it

First, we researched various genetic disorders, narrowing it down to four major diseases. Then, we identified the specific mutations behind those four diseases (Sickle-cell, Hemophilia A, Huntington’s Disease, and Alpha Thalassemia) in their respective genes; while Sickle-cell and Hemophilia A each result from point mutation in their specific genes, Huntington’s results from an insertion mutation and Alpha Thalassemia results from a deletion mutation. After this preliminary research, we decided on using a Python GUI format to implement our Mutamind project. In order to accomplish the back-end code for the GUI, we referenced our research, creating a dictionary for each of the mutations, their causes, and their properties, and making a program that determines whether or not the inputted gene sequence contains a mutation. While we were accomplishing this, we also created a JSON file that contains data about each mutation from the CDC and other various sources, and we set up the GUI interface for the program. After a lot of debugging and set-backs, we were able to rely on our team chemistry and hardwork and finish the Mutamind project.

Challenges we ran into

One of the biggest challenges we went through was the integration of software and collaboration tools to work together. Separating the front-end, back-end, and research was a tough hill to climb we did not know we would have to climb, and it required constant communication to work out all the kinks. Secondly, especially due to all four of us being first-time hackathon participants and beginners in Python, we were required to learn how to collaborate as we went, sometimes falling into the wrong hole. In addition, we ran into some difficulties testing out the code because of the very long DNA sequences, but we were able to successfully test two out of the four mutations (Sickle-cell and Alpha Thalassemia), while the other two (Hemophilia A and Huntington’s Disease) generated processing errors, but the code is there for the program to operate accurately nonetheless.

## Accomplishments that we're proud of As first time hackathon participants, Noah, Al, Casey, and I were especially proud of our immediate teamwork and collaboration to make the project possible. Being complete strangers, none of us knew what to expect, especially the friendship that would sprout from our work on MutaMind.

What we learned

While we could write a dictionary full of lessons we learned, the most significant ‘moral of the story’ for Al, Casey, Noah, and I was to not over-complicate the process. Originally, Mutamind was planned out to be a web application, but because of the difficulty of handling Django errors, a much simpler GUI seemed to do the trick perfectly!

What's next for Mutamind?

The original approach to Mutamind was to create a simple way to quickly and accurately locate and understand genetic mutations that cause human disease. What’s next is a much more in-depth comprehension of this simple mission. This includes making a more accurate, reusable process, expanding data for more diseases, and creating a healthier world starting at a molecular level.

Research/Additional Info/Sources/Brainstorming

[https://docs.google.com/document/d/15DYT0Dn3oqhHmHjvvdd_FcwaOaWPpjFXyiAWlxmr7H0/edit?pli=1]

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