The popularity of specific genes over time tells the story of society's focus on biomedicine and genomics. We will extend and refine Python code to extract and transform longitudinal gene page view counts from Wikipedia, and gene publication citations counts from PubMed. These count data will be updated via GitHub Actions, hosted on GitHub. Visualizations will leverage reusable web components -- e.g. interactive plots and genome visualizations -- and any other interfaces that participants build. We'll also explore this data on interactive notebooks in Terra. Let's surface this history of genes and make it easily accessible to all. 

Why this Project is Applicable to Others in the Community: 

Use cases: 

Data and source code will be freely and openly accessible. Concrete examples will demonstrate reusability, and foster it in ways that directly add value for engineers and data scientists. The project itself is, essentially, to make data that is already public much more findable and accessible in ways that add value for researchers, funding agencies, historians, and anyone in the public who is interested in genes.

About the Project:
We will be integrating the Pebblescout and other similar SRA (Sequence Read Archive) search indexes into computational workflows that also leverage cloud-hosted, precalculated data from the SRA Taxonomy Analysis Tool (STAT), preassembled contigs, and metadata associated with SRA samples, and ElasticBLAST. 

The more than 40 petabytes – and growing – of data available from the NIH Sequence Read Archive (SRA) and other public resources provide a vast reservoir of information that can be tapped to accelerate biomedical discovery. Working with this data, participants will apply tools and resources to several large-scale biological questions including but not limited to: 

To fully realize the potential of SRA, it must be findable and accessible. To this end, SRA has been distributed via the Amazon Web Services Open Data Sponsorship Program (AWS ODP) and the Google Cloud Platform (GCP), enabling efficient access to the entire sequence data corpus as well as the metadata describing the technical, biological, and environmental context under which individual sequence samples were obtained. The NIH and Department of Energy (DOE) are now collaborating to bring sequenced-based search tools capable of scanning the entire petabyte scale SRA to the scientific community.

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Project 3: Using MYC Amplification as a Prior in Gene Expression Analysis in Congenital Heart Disease and Cancer in Kids First and INCLUDE Data Set
NIH

About the Project:

MYC is an oncoprotein and often implies worse outcomes, however it also seems to have a role in cardiovascular disease. Using open data from both the INCLUDE Data Hub and the Kids First Data Resource Portal, this Hackathon will:

• Explore MYC expression and its segregating effects at the intersection of congenital heart defects and cancer
• Build platform agnostic workflows and analysis notebooks using the elements of style method (https://nih-nichd.github.io) that will use both WGCNA and limma to probe the dual role of this gene.
• Result both in a tutorial of how to proceed from both https://portal.includedcc.org/ and https://portal.kidsfirstdrc.org to https://cavatica.sbgenomics.com/ as well as produce results in notebooks that could be used as figures in a paper promoting open transparent, reproducible results.

Outcomes:
The workflows and notebooks will be on GitHub, all processes will be containerized. We will build and load the workflow created onto CAVATICA. We will push the data from Kids First and INCLUDE onto CAVATICA. 

 What is the Gabriella Miller Kids First Pediatric Research Program? The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH Common Fund program whose goal is to help researchers uncover new insights into the biology of childhood cancer and structural birth defects, including the discovery of shared genetic pathways between these disorders. To achieve this goal, the program has developed the Gabriella Miller Kids First Data Resource, a cloud-based platform which publicly shares genetic and clinical data from childhood cancer and structural birth defect cohorts, and includes a portal (https://portal.kidsfirstdrc.org/) and other tools to foster analysis and collaboration.

What is the INCLUDE Project? The NIH INCLUDE (IN vestigation of C o-occurring conditions across the L ifespan to U nderstand D own syndromE ) Project seeks to accelerate the discovery of etiology and biologic pathways underlying conditions that co-occur with Down syndrome. The mission of the INCLUDE Data Hub is to connect and empower the Down syndrome stakeholder and research community on behalf of accelerated impact through scientific discoveries that will improve and enrich the lives of people with Down syndrome. The Data Hub is a collaborative, secure cloud-based resource that includes accessible, multimodal data, reproducible bioinformatic tools and pipelines, and workspaces supporting scalable computation and analysis.  

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Project 4: Building Knowledge Graphs to Capture Disease Sub-Typing and Subsequent Drug Efficacy Information
DNAnexus

About the Project: 

We are aware that many folks are interested in subtyping disease for coherent drug treatment. We have done some preliminary work demonstrating that disease subtyping can be predicted and validated using large biobank-style data. We'd like to demonstrate the capture of that information in knowledge graphs such that they can both be loaded into and validate cutting edge data models.

How Project is Open Source and/or FAIR: 
The entirety of the project will be on github, and will pull from a variety of open-source datasets.

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Project 5: kidSIDES
Regeneron Pharmaceuticals

About the Project:

kidSIDES is the largest, open database with 500K pediatric drug safety signals (https://kidsides.nickg.bio/) where ~20,000 significant pediatric drug safety signals were identified and the association between Montelukast and Psychiatric disorders was corroborated. The resource allows generation of more hypotheses such as adverse effect profiles, drug toxicity during childhood, and genetic susceptibility of pediatric adverse drug effects.

Why this Project is Applicable to Others in the Community: 
The database is downloaded and cached on your machine using the R package kidsides on CRAN. This sqlite database contains 17 tables with drug, adverse event, gene, effect class and drug class identifiers that allow for analysis and making knowledge graphs.

How Project is Open Source and/or FAIR: 
The project would be based on open data, from kidsides or elsewhere such as the OpenPTBA from the ccdatalab.org. The analysis and code would be published on github.