Building an Interoperable Autoimmunity, Inflammation, and COVID-19 Data Ecosystem

Institute or Center: National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIAMS) in partnership with Accelerating Medicines Partnership® (AMP®) Program

Project: Building an Interoperable Autoimmunity, Inflammation, and COVID-19 Data Ecosystem

Skills sought:

  • Programming in R or Python for data wrangling and statistical data analysis
  • Experience in developing approaches to integrate and harmonize multiple data types including clinical and multi-omic data (such as those from RNA-seq and ATAC-seq)
  • Experience in Jupyter notebooks, SQL databases, Linux command line preferred

About the position: NIAMS seeks a DATA Scholar to establish data interoperability across multi-omic data sets from autoimmune diseases and neurodegenerative diseases, and at the intersection of COVID-19 infection and vaccination. The scholar will:

  • Develop an understanding of datasets produced by AMP programs and NIH intramural Systemic Autoimmunity Branch (SAB) COVID-19 project.
  • Identify high value datasets, review quality controls, annotation systems, identify opportunities for combined analysis.
  • Develop strategies to harmonize multi-omic data sets generated from different platforms, harmonize data annotation and curation, approaches to reprocess data sets to make them interoperable and identify tools to integrate high dimensional multi-omic datasets.
  • Present strategies and recommendations to senior leadership and advisory committees.
  • Work with NIH staff and AMP investigators on the AMP SBI Pilot projects. Conduct preliminary integrated data analysis of multi-omic datasets, if appropriate.

About the work: Systemic inflammation is recognized as an important contributor to tissue damage and chronicity in autoimmune diseases like rheumatoid arthritis (RA) and lupus nephritis (LN) and neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s (PD). The AMP Programs provide an opportunity to identify shared and distinct mechanisms active across these multiple chronic diseases. To explore this novel concept, NIH has launched a series of studies under an emerging AMP program named AMP Systems Biology of Inflammation (AMP SBI) to establish the feasibility of a systems level analysis across major complex diseases. Given that a significant proportion of the general population will be exposed to COVID-19 infection / vaccination there is a critical need to characterize how this modulates chronic systemic inflammation, organ damage in patients with autoimmune diseases.

Datasets involved: The  scholar will have access to extensive datasets from the  AMP RA/SLE, AMP AD programs and COVID-19 research data from NIAMS Systemic Autoimmunity Branch. These datasets include patient clinical information, high dimensional omics data including single cell and bulk transcriptomics in tissue and blood, immunoprofiling data from patients with systemic autoimmune diseases before and after COVID-19 vaccination/infection, and additional datasets as needed.

Why this project matters: The goal of the project is to begin the construction of a data ecosystem to enable studies to understand how chronic inflammation may represent a pathway shared by multiple chronic diseases and how COVID-19 infection/immunizations may affect disease outcomes by modulating inflammation. The scholar will lay the foundation to enable interoperability of multi-omic datasets that can be expanded to other inflammation and autoimmunity projects increasing the potential for new discoveries to reduce the burden of chronic systemic inflammation.

Work location: Bethesda, MD

Work environment: The scholar will be mentored by one of the Deputy Scientific Directors, NIAMS. The DATA Scholar will work in close collaboration with the Associate Director of Strategic Initiatives and the Chief of Biodata Mining and Discovery Section. There will be opportunities to interact with systems biology teams from AMP projects and NIAMS IT team.

To apply to this or other DATA Scholar positions, please see instructions here:

This page last reviewed on April 7, 2022