PREDICT: Pseudoexon Risk Estimation Database and In silico Clinical Testing


Our genes are organized in introns and exons. Introns typically make up 90% of a gene sequence and harbor the vast majority of genetic variation.

Despite this, intronic disease-causing mutations are often overlooked in clinical diagnostics of genetic disease, because it is very difficult to distinguish disease-causing mutations from benign intronic sequence variants, and there are no tools to help clinicians in this task.

Inclusion of pseudoexons by intronic mutations or sequence variants is a significant disease mechanism, which has previously been severely underestimated due to lack of knowledge of pseudoexons.


The aim of the project

The PREDICT project aims to develop an open access database (the PseudoExon Database, or PED).

Furthermore the project wants to develop a toolkit (the Clinical PseudoExon Toolkit, or C-PET), based on our recent discoveries on disease-causing pseudoexon inclusion.

C-PET will allow the interpretation of an enormous number of intronic variants and it will ultimately enable the development of a therapeutic option in personalized medicine.

The C-PET will be the first of such tools for hospitals and other diagnostic labs, which will foster new collaborations between SDU and the healthcare sector.

The project outcome

1. Construct the database (PED):

  1. expand our current initial version of the PED with more datasets;

  2. develop a program for the automatic integration of additional data.

2. Develop a toolkit (C-PET) for assessment of pathogenic potential of clinically detected variants and easy browsing and querying of the PED in diagnostic centers.

3. Validate experimentally the data in the PED and the predictions made by C-PET.


Work package leader: Brage Storstein Andersen, Professor, FRCPath., Department of Biochemistry and Molecular Biology


Maja Dembic, Post.Doc., Department of Biochemistry and Molecular Biology
Thomas Koed Doktor, Post.Doc., Department of Biochemistry and Molecular Biology
Fabrizio Montesi, Assistant Professor, Department of Mathematics and Computer Science

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