Inspiration
In the past I have worked on a lot of biology related projects and there has not been a user-friendly or accessible tool which allowed me to analyze single nucleotide polymorphisms. Projects involving analyzing them are often done by beginners, and not having access to one that is easy to use is a major roadblock. Using SNP Scan, the field is now much more accessible to anyone wether they are researchers, students, or hobbyists.
What it does
SNP Scan analyzes DNA sequences to identify SNPs which are variations at single nucleotide positions. It shows SNPs as transitions or transversions, calculates SNP density, and generates a consensus sequence.
How we built it
SNP Scan is built using Python with Streamlit for the interactive and user-friendly web-based interface, Biopython for handling FASTA file parsing and sequence analysis, and Pandas for managing and displaying SNP data in tabular form.
Challenges we ran into
The biggest roadblocks I ran into were handling different sequence input formats such as mismatched lengths or improperly formatted FASTA files and making sure SNP classification and consensus sequence generation were both accurate and efficient.
Accomplishments that we're proud of
I am most proud of how I have made this really necessary program much more accessible by integrating features like transition/transversion classification, SNP density calculation, and consensus sequence generation into a single platform.
What we learned
Although I have bioinformatics experience, this project gave me much more room to learn how to use Biopython in a way that is not complicated for the user.
What's next for SNPScan
Having more visualizations for SNP density and mutation types is certainly something I will implement in the future.
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