Inspiration

With the sequencing of people's genomes becomingly increasingly common and the difficulty that comes with analyzing this patient data, we wanted to create an interface that allows physicians to more easily interpret this information. We strove to take an overwhelming amount of patient sequencing data and turn it into an application so that physicians can have better access to tools that will help them use this information for the good of their patients.

What it does

A user friendly, completely scaleable web based data analysis system for analyzing and interpreting patient genomic sequencing data. We start with short reads from various genome sequencing technologies (Illumina, Ion Torrent, Roche, etc.), which are then run through an industry standard variant calling and annotation pipeline. This is then cross referenced with sources from scientific literature on clinical significance. Data is then ranked on the apparent clinical significance of the variants and returned to the user in an easy to understand data visualization.

How we built it

We developed a scalable system based on AWS backends, running a custom image of Linux loaded with analysis software. We leveraged the power of AWS to create multiple instances running in parallel which significantly cuts down on the time and start up costs of analyzing patient sequencing data.

Challenges we ran into

We ran into issues with networking and dealing with programmatically creating instances and being able to run commands on them. Additionally, we had problems with filtering data in order to achieve a low processing time for the variant rankings, and displaying these final values.

Accomplishments that we're proud of

Making it scalable, user friendly, a marketable product, and integrating multiple high level systems together. We're also proud of the ease of use that can revolutionize the field of personalized medicine.

What we learned

We learned that large scale projects are very challenging to fully integrate and complete in thirty five hours with a team of four.

What's next for Ingeneious

We want to continue to improve its scalability, its security, ranking algorithms, and work on further integration of other patient data and correlate that with other genomic variants.

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