Computational Medicine in the Cloud

06/10/2019 @ 9:00 AM – 06/11/2019 @ 6:00 PM
Johns Hopkins University, Hodson Hall 210 & 2nd floor lobby
3101 Wyman Park Drive
Batimore MD 21218

We are pleased to announce the first ever Computational Medicine in the Cloud Hackathon! NCBI will help run a bioinformatics hackathon in Baltimore, Maryland hosted by the Johns Hopkins University.

We’re specifically looking for folks who have experience in working with complex haplotypes, complex disease, precision medicine, and similar genomic analysis. If this describes you, please apply! This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for large scale genomic analyses from high-throughput experiments (please note that the event itself will focus on open access public human data).

Potential topics include:

  • Coherent-phenotype mapping to haplotypes
  • Mapping haplotype blocks to ontologies
  • Structural Variants in Health and Disease
  • Haplotypes and RNA-seq
  • Complex Variant Structure
  • Annotation Structures for Complex Variants
  • Visualization of Complex Variants

Hackathon Logistics

The hackathon runs from 9 am – 6 pm each day, with the potential to extend into the evening hours each day. There will also be optional social events at the end of each day. Working groups of five to six individuals, with various backgrounds and expertise, will be formed into five to eight teams with an experienced leader. These teams will build pipelines and tools to analyze large datasets within a cloud infrastructure. On both days, we will come together to discuss progress on each of the topics, bioinformatics best practices, coding styles, etc.

There will be no registration fee associated with attending this event.

Note: Participants will need to bring their own laptop to this program. No financial support for travel, lodging, or meals is available for this event.



Datasets will come from open access public repositories, with a focus on a number of trios produced by long read sequencing as a base graph and short read datasets in the sequence read archive that have been ported to cloud infrastructure, as well as derivative contigs of the above.



All pipelines and other scripts, software, and programs generated in this hackathon will be added to a public GitHub repository designed for that purpose.

Manuscripts describing the design and usage of the software tools constructed by each team may be submitted to an appropriate journal such as the F1000Research hackathons channel, BMC Bioinformatics, GigaScience, Genome Research or PLoS Computational Biology. Ideally, we will present a graph genome, a number of protocols for associating short read omics data with it, and some derived datasets (e.g. variant calls) from such protocols.



To apply, please complete this form. Initial applications are due on May 22, 2019 by 3 pm EDT. We will select participants based on the experience and motivation they indicate on the form.

Prior participants and applicants are especially encouraged to apply. The first round of accepted applicants will be notified on May 24 by 3 pm EDT, and will have until noon EDT on May 28 to confirm their participation (especially qualified applicants or those traveling internationally may receive acceptances earlier). If you confirm, you must be willing to commit to both days of the event, as confirming and not attending prevents other data scientists from attending this event.



Entrants retain ownership of all intellectual property rights (including moral rights) in the code submitted to as well as developed in the hackathon. Employees of the U.S. Government attending as part of their official duties retain no copyright in their work and their work is in the public domain in the U.S.

The Government disclaims any rights in the code submitted or developed in the hackathon.

Participants agree to publish the code and any related data in GitHub.

For more information, or with any questions, please contact Ben Busby (

JHU - Institute for Computational Medicine