Cellular network biology in the Jensen Group
The Jensen Group develops state-of-the-art tools for generation and analysis of molecular interaction networks from proteomics data and text mining. The tools are freely available to the scientific community.
The group works in three broad areas: network biology, text mining, and computational proteomics.
“One of the core activities of the lab is to develop and maintain databases and tools, which are used by thousands of researchers around the world every week. The most famous of these is the STRING database, which we run in collaboration with groups at the European Molecular Biology Laboratory and the University of Zurich,” says Professor and Group Leader Lars Juhl Jensen.
Many of the group’s other resources either contribute directly to STRING (e.g. the text-mining tools), complement it with additional information (e.g. subcellular localization, expression, and disease associations), or build upon it. The group also creates tools and pipelines for analysis of mass spectrometry-based proteomics data. This includes improved statistical methods and specialized tools for analysis of phosphoproteomics data. These are developed in close collaboration with the neighbouring proteomics groups at CPR.
The group is involved in running training events on a regular basis, which take the forms of guest lectures on Masters and PhD-courses, week-long practical courses, and half-day to two-day workshops.
For more information, please see the Jensen lab website. Please note that this is a privately owned page exclusively under the responsibility of Professor Jensen.
“STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets”
In the latest version of the STRING database, the international team of developers improved data dissemination with a completely redesigned web interface and integration into the Cytoscape software framework.
“Text mining of 15 million full-text scientific articles”
In collaboration with the Technical University of Denmark, the Jensen Group performed the largest biomedical text-mining effort to data, which showed the importance of using full-text articles rather than abstracts only.
“Avoiding abundance bias in the functional annotation of posttranslationally modified proteins”
Together with the Choudhary Group, the group showed how abundance bias in proteomics data leads to false enrichment results and developed a statistical method addressing this.
Staff of the Jensen Group
Group Leader: Professor Lars Juhl Jensen