BioFrontiers Hackathon

5/22 - 5/24

Projects have been selected and the general application is open. If you're interested in contributing to a project at the hackathon, please apply below

About

A hackathon is a means for bringing talented people together to sit in a room, focus, and solve a problem in the scientific (or other) community. These solutions often come in the form of software written to fill a gap in available tools. Part of solving the problem also includes good documentation and ensuring accessibility for all. Teams usually consist of 5-6 people selected for their particular backgrounds and complementary skill sets. Ideally, at the end, most teams will leave with a publication in progress and a solution to meet the goals defined in their project, or a clear path to future development.

What to Expect

Our hackathon is a little different than most others you may have heard of before. Namely, there is no competition. Each team is working toward its own goal in order to advance science! Most days will consist of a brief gathering each morning with the entire group, followed by time to hack until lunch. Presentations from each team will happen during lunch in order to encour age collaboration and make sure everyone is on track. After lunch, teams go back to hacking until the end of the day (5 or 6PM!). During the evenings there will be opportunities to socialize, network with your peers, and take a load off!

Who Do We Need

  • People with diverse educational backgrounds who want to learn about the hackathon process and help shape these exciting tools. Your ideas will be incorporated and make a real difference, as many of the proposed projects are just rough outlines and will greatly benefit from collaborative planning sessions at the event. Our selected teams cover a broad range of topics that can use your individual experience so you won’t feel out of your depth.
  • Researchers interested in contributing and learning about hackathons! You may not have much experience with Python or R, but your expertise and input is extremely valuable. Most of these problems, though they will be solved programmatically, are scientific problems. Join a team, contribute your knowledge this time, and submit your project as a team lead at our next hackathon to help make your idea a reality.
  • People with programming experience in any of our listed skills, or anything we may have missed! You will have a real chance to use your talents or expand your horizons and learn new things.
Application

Skills:

Projects

Measuring Effective Learning in an Epigenetics MOOC
This is an active BioNLP research project. We are investigating the representation, transmission, learning, recall, and reproduction of scientific knowledge via human natural language. We are interested in the neurocognitive structure of human scientific knowledge and its communication through written speech. We are particularly interested in how science can be taught most effectively, and expect that our results will be a contribution to the learning sciences, and the STEM disciplines in particular.
Genome Analysis and Visualization With valr
Further develop the speed and utility of the valr R package (http://rnabioco.github.io/valr). Work on the C++ back-end, develop reproducible analysis workflows using valr and Rmarkdown, or develop new genomic visualzations using valr and Shiny.
Prediction of Brain Age From EEG
Development of an algorithm or analysis software that can evaluate an individual’s brain age based on an EEG dataset.
Development of Open Educational Resources for Module-based Learning in the Cloud
The goal of this project will be to develop a dashboard or learning interface that can easily be ported to any openly available cloud resource with support for pluggable education modules designed to teach different topics.
Creation of a Python Package That Provides Utilities to Summarize RNA-Seq Experiments
The results of RNA-Seq experiments can be difficult to summarize and navigate. Another shortcoming of transcriptomics studies is that machine learning algorithms are underutilized in the literature. The principal goal of this project would be to create a Python package that facilitates the use of machine learning tools (e.g. sklearn) while producing results that are compatible with a downstream visualization tool.
Development of a Suite of Tools to Assemble and Align Long Sequencing Reads
The end product is a suite of tools to assemble and align a long read input file. We are going to develop the alignment-free filtering algorithms based on k-mer counts from scratch. We will then implement string graph algorithms for sequence assembly and NW and SW alignment algorithms.
MyFavoriteTF
A tool that predicts information about Transcription Factors including cell type activity, binding sites, associated eRNAs, regulated genes.
A Geospatial Analysis of the Relationship Between Science Education Funding and Policy Outcomes
This team will develop a public web page with interactive data visualizations demonstrating conclusions and allowing visitors to interact with the data in ways that enhance understanding.
Mining for eQTLs in the Genomic Data Commons
This project will mine the publicly available cancer genome atlas (TCGA) for gene expression markers of specific tissue types, cancer subtypes and genetic background effects.
Feature Development for PhenVar, a tool for exploring phenotypes associated with genetic mutations
PhenVar, originally developed at the January 2017 NCBI Hackathon, now has a web presence here at BioFrontiers. We’d like to enhance its functionality by adding support for generating phenotypic networks and haploblock associations. (https://phenvar.colorado.edu)
Creation of Software for Rapid Analysis of Fluorescence Binding Data
The software will collect raw data to determine relevants signals and estimate the parameters of the quadratic binding equation. Using these estimates, fit the equation to the experimental data and plot. https://github.com/Guilz/rfret
The Effect of Institutional Prestige on Academic Collaboration
Explore the roles that institutional prestige plays in shaping academic collaboration in the biosciences by comparing how interdisciplinary research is distributed over prestige rankings.
Chemical Precursor and Synthesis Pathway Identification Derived From Publicly Available Data
Develop software to leverage existing and currently underutilized data from publicly available databases and commercially available screening libraries to train a machine learning algorithm to identify chemical precursors and synthesis pathways that will lower the overall cost of manufacture for industrial scale products, pharmaceuticals, and academic lab-generated chemical tools.
Render 3D Volumes in ImageJ From Single Molecule Point Localization Data
The goal of this team will be to render a point cloud of 3D super-resolution data directly into an ImageJ 3D (XYZ) volume with appropriate image metadata. The project should use core ImageJ framework, such as ImageJ Ops, and should bypass any intermediate steps such as writing the data to a TIFF stack.

Dates and Event Items

Sponsorship Information

Sponsors for the BioFrontiers Institute Hackathon will provide funds for food and beverages for our hacking teams so that they can focus their energy and talent on creating ‘out-of-the-box’ and open source bioinformatics solutions to tackle big clinical and health research challenges. For sponsors, the BioFrontiers Hackathon provides an excellent opportunity to:

For more information about sponsorship, please download our Sponsorship Packet or contact Hilary Furlong, BioFrontiers Institute Senior Director of Development, at hilary.furlong@colorado.edu or 303-735-9073