• Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

Congratulations to the dkNET team - Drs. Neil McKenna and Jeffrey Grethe and co-authors on publishing a paper "Transcriptional regulatory networks of circulating immune cells in type 1 diabetes: a community knowledgebase” in iScience

Congratulations to the dkNET team - Dr. Neil McKenna, the Principal Investigator of the Signaling Pathways Project (SPP), Dr. Jeffrey Grethe, the Principal Investigator of dkNET, and co-authors on publishing a paper “Transcriptional regulatory networks of circulating immune cells in type 1 diabetes: a community knowledgebase” in iScience!

Here is the highlights and abstract from the iScience:

"Highlights

  • Re-use of transcriptomic type 1 diabetes (T1D) circulating immune cells (CICs) datasets
  • We generated transcriptional regulatory networks for T1D CICs.
  • Use case generate substantive hypotheses around signaling pathway dysfunction in T1D CICs
  • Networks are freely accessible on the web for re-use by the research community

Summary

Investigator-generated transcriptomic datasets interrogating circulating immune cell (CIC) gene expression in clinical type 1 diabetes (T1D) have underappreciated re-use value. Here we repurposed these datasets to create an open science environment for the generation of hypotheses around CIC signaling pathways whose gain or loss of function contributes to T1D pathogenesis. We firstly computed sets of genes that were preferentially induced or repressed in T1D CICs and validated these against community benchmarks. We then inferred and validated signaling node networks regulating expression of these gene sets, as well as differentially-expressed genes in the original underlying T1D case:control datasets. In a set of three use cases, we demonstrated how informed integration of these networks with complementary digital resources supports substantive, actionable hypotheses around signaling pathway dysfunction in T1D CICs. Finally, we developed a federated, cloud-based web resource that exposes the entire data matrix for unrestricted access and re-use by the research community."


Source and more information: https://doi.org/10.1016/j.isci.2022.104581


X

Are you sure you want to delete that component?