Skip to content

Home

Jump to bottom
Xiaofeng Liao edited this page Oct 19, 2023 · 21 revisions

Welcome to the Radboudumc <> X-omics TWOC demonstrator Wiki !

Rationale

The COVID-19 pandemic caused an enormous influx of information. This information varies from a continuous flow of information in social media with no form of trustworthiness or control, to peer-reviewed publications in scientific literature, and to privacy sensitive real world data about patients. Information and data is siloed in various systems or databases and scattered in various standards and languages.

Trusted World Of Corona

The Trusted World Of Corona (TWOC) project aims to create a platform in which humans and machines can meet based on FAIR data and algorithms. Privacy sensitive data 'stays at its source' and is visited rather than actually shared or moved. This allows data to flow seamlessly within healthcare and scientific systems, and be accessible to relevant stakeholders to drive patient impact.

TWOC demonstrator project

Within the TWOC consortium we have defined a demonstrator project consisting of a use case on COVID-19 treatment with Tocilizumab, a monoclonal antibody therapy targeting the IL-6 receptor. The main aim of this use case is to prove TWOC technical feasibility and to collaborate around federated TWOC architectures. Various partners within the TWOC consortium will deploy a FAIR Data Point (FDP) containing (meta)data pointing to data relating to COVID-19, measurements, Tocilizumab treatment, ICU admittance and the need for mechanical ventilation. The overall goal of this is to demonstrate (federated) SPARQL queries through GDPR-compliant reuse of personal and scientific data.

FAIR Data Cube-based FDP with FAIRified research data

The Radboudumc (CMBI department) in collaboration with X-omics will fill a FAIR Data Point (FDP) with public COVID-19 multi-omics research datasets from Su et al., 2020, Cell [see below], in order to have COVID-19 relevant biomedical data that we can query in (between) the various TWOC FDP. For this, we will make use of the FAIR Data Cube (FDCube, from the X-omics National Initiative), which is a set of tools and services that help researchers in different stages of the Research Data Life Cycle, including creating and describing new data, and finding, understanding and reusing existing FAIR multi-omics data. In addition, we have defined a specific (meta)data structure to use as part of our FDP which is based on the ISA framework, together with Phenopackets for phenotypic information. A further and important novelty of this Radboudumc <> X-omics FDP, is that we aim to enable the direct querying of biomedical omics data at the feature level, for transcript-, metabol-, and proteomics for Tocilizumab-relevant molecular features.

The Radboudumc <> X-omics FDP itself is currently fully operational and can be visited at https://fdp.cmbi.umcn.nl/.

Su et al., 2020, Cell, COVID-19 multi-omics study

This referenced work by Su et al., 2020, Cell, presents an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis.

Example queries on the TWOC FDP (human readable)

As one goal of this project is to demonstrate (federated) SPARQL queries on the TWOC FAIR data points, we have defined a number of human readable questions which are to be translated to SPARQL queries. Please see below for this list of human readable queries, and this link to the translated SPARQL queries.

  1. How many patients in the dataset(s) available in the FDP are diagnosed with COVID-19 ?

  2. What is the distribution in hospitalization status for these COVID-19 patients ?

  3. What is the distribution of the COVID-19 Disease Severity scores for these COVID-19 patients ?

  4. For how many patients in the dataset who are diagnosed with COVID-19 and who are ICU admitted do we have omics data available ?

  5. What type of biological samples are these measurements derived from ? ( e.g. systemic, from blood; or local, from a nasal wash, etc. )

  6. Which omics datasets contain (targeted) measurements about IL6, CRP or ferritin ? ( e.g. on the level of Transcriptomics, Proteomics and/or Metabolomics, etc. )

  7. What is the range, mean and median value of IL6, CRP or ferritin for COVID-19 patients admitted at the ICU, split to patient subgroups with a mild, moderate or severe COVID-19 infection Disease Severity score according to the WHO ordinal scale definition ?

  8. A more complex example:

    • Retrieve Study Identifier where COVID is mentioned, for example in title/description
    • Query Subject id/individual id/patients that have COVID-19 status information (COVID-19-YES/NO)
    • Query Sample id based on individual id
    • Based on sample ID, to find the link to the transcriptomics (metabolomic) feature data file
Clone this wiki locally