Back to search

FRIPRO-Fri prosjektstøtte

Bioinformatics for Proteogenomics - looking up the answer in the back of the book

Alternative title: Forbedret persontilpasset medisin via bioinformatikk for proteogenomikk.

Awarded: NOK 8.6 mill.

Project Manager:

Project Number:


Project Period:

2020 - 2025

Funding received from:


Monogenic diabetes is a rare form of pediatric diabetes. Depending on their genetic profile, patients can avoid lifelong insulin medication and associated complications. Yet, it is extremely challenging to (1) infer the consequences of genetic mutations harbored by patients, and (2) predict response to alternative treatment. Consequently, many cases that could benefit from adapted care remain undiagnosed or misdiagnosed as type 1 or type 2 diabetes, with life-threatening consequences. The same mutation can indeed affect two patients differently based on their genome, environment, clinical history, etc. To gain further insight into the clinical profile of a patient, this project aims to complement genetic testing with information on biological signaling through the monitoring of protein levels. First, we will establish new software for researchers to better interpret data sets combining genomics and protein signaling information. Second, we will use our novel software tools to analyze vast amounts of data made available by the scientific community and create an open knowledgebase on the effect of genetic variation on biological systems. Finally, we will design targeted assays monitoring signaling events to complement the genetic diagnostic of monogenic diabetes. By providing the scientific community with new informatic tools, the project will enable the routine joint analysis of genomic and protein signaling data. The new knowledge generated by the project will provide a novel and essential resource for biomedical scientists. Finally, by combining the new knowledge and software we will design a pioneer assay for early diagnosis of monogenic diabetes and patient classification based on predicted response to alternative treatment strategies. Beyond diabetes, the approach has the potential to help capture the bigger picture for various disease conditions, hence providing much-needed data for precision diagnosis and treatment guidance.

Recent advances in genetics and protein signaling have revolutionized modern medicine, enabling the refinement of diagnosis and the discovery of new treatments. However, accounting for genetic variation in protein analysis remains highly challenging, impairing our ability to adapt treatments based on the genetic profiles of patients. I propose to establish a research group that will use state of the art computer science to alleviate this problem. Aim 1: establish novel software for the joint handling of gene- and protein-level information; this will allow researchers to investigate signaling events respectively to a genetic background. Aim 2: use our pipeline in a big data strategy to build a knowledgebase on the effects of genetic variation on proteins using public datasets gathered by the scientific community; this will allow scientists to gain new knowledge on genetic variation, better understand its possible consequences, and evaluate potential pathogenicity. Aim 3: enable the creation of assays monitoring sets of proteins in a specific genetic background; this will provide clinicians and the biotechnology sector with a new analytical platform holding the promise for for improved diagnostic and patient care. As a pilot, we will establish such an assay for diagnostic and treatment guidance in monogenic diabetes. Monogenic diabetes is a rare form of diabetes where patients can avoid lifelong insulin medication through diet management or alternative treatment. However, to date, most patients remain undiagnosed or misdiagnosed as type 1 or type 2 diabetes, and hence do not benefit from adapted care. Our assay will complement genetic testing with signaling information to test hypotheses on the cause of the disease and on possible responsiveness to alternative treatment.

Publications from Cristin

No publications found

No publications found

No publications found

No publications found

Funding scheme:

FRIPRO-Fri prosjektstøtte

Funding Sources