## Inspiration

Genes are the code of life, a sequencing that determines who you are, what you look like, what you do and how you behave. Sequencing is the process of determining the order of bases in an organisms genome. Knowing one's genetic sequence can give insight into inherited genetic disorders, one's ancestry, and even one's approximate lifespan.

Next-generation sequencing (NGS) is a term for the massive advancements made in genetic sequencing technologies made over the past 20 years. Since the first fully sequenced genome was released in 2000, the price of sequencing has dropped drastically, resulting in a wealth of biotech start-ups looking to commercialize this newfound scientific power.

Given that the human genome is very large (about 3 GB for an individual), the combination of computational tools and biology represent a powerful duo for medical and scientific applications. The field of bioinformatics, as it is known, represents a growth area for life sciences that will only increase in years to come.

## What it does

Reactive Genetics is a web portal. Individuals who have either paid to have their genes sequenced, or done it themselves (an increasing probability in coming years), can paste in their sequence into the home page of the web portal. It then returns another web page telling them whether they hold a "good" or "bad" gene for one of six common markers of genetic disease.

## How I built it

Reactive Genetics uses a flask server that queries the National Center for Biotechnology Information's Basic Local Alignment Search Tool (BLAST) API. "BLASTING" is commonly used in modern biological research to find unknown genes in model organisms. The results are then returned to a React app that tells the user whether they are positive or negative for a certain genetic marker.

## Challenges I ran into

The human genome was too large to return reliably or host within the app, so the trivial solution of querying the sequence against the reference genome wasn't possible. We resorted to BLASTing the input sequence and making the return value a boolean about whether the gene is what it "should" be.

## Accomplishments that I'm proud of

One team member hopes to enter serious bioinformatics research one day and this is a major first step. Another team member gave a serious shot at learning React, a challenging endeavour given the limited time frame.

## What I learned

One team member learned use of the BLAST API. Another team member became familiar with Bootstrap.

## What's next for Reactive genetics

The app is currently running both a React development server and a Flask server. Eventually, porting everything over to one language and application would be ideal. More bioinformatics tools are released on a regular basis, so there is potential to use other technologies in the future and/or migrate completely to React.
## Inspiration

In an AI course, an assignment was to build a simple chatbot. We took concepts learned in class and worked it into a web application that focuses on QHacks.

## What it does

It's an AI that chats with you - answer it's questions or say anything and it'll respond!

## How I built it

First we built the application using Javascript/JQuery using a simple textbox and console output. Then we added CSS and "chat bubbles" to make it feel like a regular conversation.

## Challenges I ran into

* Figuring out RegEx in Javascript
* Getting the response format correct using CSS
## Accomplishments that I'm proud of
The more you interact with the chatbot, the more it seems like it could be human. We made our responses conversational, and are proud of the outcome.
## What I learned
How to manipulate and then map user input segments to custom responses in a way that seems almost human-like.
## What's next for QHacks Chatbot
* Adding more responses
* Add response animations or delays
## Inspiration

We saw that lots of people were looking for a team to work with for this hackathon, so we wanted to find a solution

## What it does

I helps developers find projects to work, and helps project leaders find group members.
By using the data from Github commits, it can determine what kind of projects a person is suitable for.

## How we built it

We decided on building an app for the web, then chose a graphql, react, redux tech stack.

## Challenges we ran into

The limitations on the Github api gave us alot of troubles. The limit on API calls made it so we couldnt get all the data we needed. The authentication was hard to implement since we had to try a number of ways to get it to work. The last challenge was determining how to make a relationship between the users and the projects they could be paired up with.

## Accomplishments that we're proud of

We have all the parts for the foundation of a functional web app. The UI, the algorithms, the database and the authentication are all ready to show.

## What we learned

We learned that using APIs can be challenging in that they give unique challenges.

## What's next for Hackr\_matchr

Scaling up is next. Having it used for more kind of projects, with more robust matching algorithms and higher user capacity.