Marinka Zitnik

Fusing bits and DNA

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PLoS CompBio: Gene Prioritization by Compressive Data Fusion

Our paper on Gene prioritization by compressive data fusion and chaining has been published in PLoS Computational Biology.

In the paper, we present Collage, a new data fusion approach to gene prioritization. Together with collaborators from Baylor College of Medicine, we tested Collage by prioritizing bacterial response genes in Dictyostelium as a novel model system for prokaryote-eukaryote interactions.

We started from four bacterial response genes and 14 different data sets ranging from gene expression to pathway and literature information. Collage proposed eight candidate genes that were tested in the wet laboratory. Mutations in all eight candidates reduced the ability of the amoebae to grow on Gram-negative bacteria. Furthermore, five out of the eight candidate genes were required for growth on Gram-negative bacteria but had no discernible effect on growth on Gram-positive bacteria. This is a remarkably accurate result since only about a hundred of the 12,000 Dictyostelium genes are estimated to be responsible for bacterial response.