DEEP -- Differential Expression Effector Prediction

What you can do:
A tool that takes your data on differential gene expression (i.e. SAGE or microarray data) and predicts additional molecules which may be of importance.
  • High-throughput methods for measuring transcript abundance, like SAGE or microarrays, are widely used for determining differences in gene expression between different tissue types, dignities (normal/malignant) or time points.
  • Further analysis of such data frequently aims at the identification of gene interaction networks that form the causal basis for the observed properties of the systems under examination.
  • To this end, it is usually not sufficient to rely on the measured gene expression levels alone; rather, additional biological knowledge has to be taken into account in order to generate useful hypotheses about the molecular mechanism leading to the realization of a certain phenotype.
  • We present a method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH database on signal transduction, to predict additional--and not necessarily differentially expressed--genes or gene products which might participate in processes specific for either of the examined tissues or conditions.
  • In a first step, significance values for over-expression in tissue/condition A or B are assigned to all genes in the expression data set.
  • Genes with a significance value exceeding a certain threshold are used as starting points for the reconstruction of a graph with signaling components as nodes and signaling events as edges.
  • In a subsequent graph traversal process, again starting from the previously identified differentially expressed genes, all encountered nodes 'inherit' all their starting nodes' significance values.
  • In a final step, the graph is visualized, the nodes being colored according to a weighted average of their inherited significance values.
  • Each node's, or sub-network's, predominant color, ranging from green (significant for tissue/condition A) over yellow (not significant for either tissue/condition) to red (significant for tissue/condition B), thus gives an immediate visual clue on which molecules--differentially expressed or not--may play pivotal roles in the tissues or conditions under examination.
  • Gene Expression Profiling
  • Protein Interaction Mapping
  • Signal Transduction
  • microarray
  • SAGE
  • differential gene expression
  • high-throughput
  • transcript abundance
  • gene interaction
This record last updated: 06-05-2008

The Health Sciences Library System supports the Health Sciences at the University of Pittsburgh.

© 1996 - 2014 Health Sciences Library System, University of Pittsburgh. All rights reserved.
Contact the Webmaster

University of Pittsburgh Libraries