Analysis of Time Courses: Analysis of gene expression time-courses

The molecular processes of life are dynamic over time. Microarray experiments measuring the expression levels of a multitude of genes over time are one way of gaining insight into the dynamic processes. As a first analysis groups of similar expression patterns are routinely identified. We have developed an approach which allows to use prior knowledge, is flexible and very robust to noise. The method is implemented in the software GQL which allows control of the analysis process by use of graphical user interfaces. Currently, we are extending our framework to allow integration of further data related to transcription or protein interactions. Furthermore, we are also investigating methodologies for validating clustering of genes with functional annotation.

MASCAAT: Meta-Learning for Selection and Combination of Clustering Algorithms Applied to Gene Expression Analysis

Whether to cluster at all, which clustering method to use and how many clusters to choose are pressing questions in bioinformatics. Mostly, decisions are made by users of clustering software based on experience guided by benchmarking or indicators for reliability of solutions or model-fit. However, as clustering algorithms always produce solutions, often inappropriate methods or parameters are used and invalid results produced. Meta-learning refers to the application of machine learning techniques in choosing methods and guiding in setting parameters. We intend to build a computational framework to perform cluster validation and apply meta-learning to the problem of analyzing gene expression time-courses. More information at the Project Page. Joint work funded funded by CAPES (Brazil) and DAAD (Germany) under the program Probral.

Cellular Differentiation: Understanding transcriptional regulation in cell differentiation

The regulatory processes that govern cell proliferation and differentiation are central to developmental biology. Particularly well studied in this respect is the hematopoietic system. Gene expression data of cells of various distinguishable developmental stages fosters the elucidation of the underlying molecular processes, which change gradually over time and lock cells in certain lineages. We developed a statistical framework for tasks ranging from visualization, querying, and finding clusters of similar genes, to answering detailed questions about the functional roles of individual genes and their similarities and differences.

Drosophila Development: Gene regulation during early Drosophila development

In-Situ Hybridization experiments elucidate the spatial distribution of expressed mRNA in organisms. In particular for Drosophila large amounts of data for several developmental stages are available, complementing the DNA-microarray gene expression experiments. We have developed a image processing pipeline and a framework for joint analysis, which allows to detect co-located co-expressed genes from fused data sets.

External Collaborations

  • Prof. Alexander Schliep, Rutgers University
  • Prof. Hugues Richard, University Paris VI
  • Prof. Ricardo Campelo, Sao Paulo University
  • Prof. Ana Carolina Lorena, Federal University of ABC
  • Dr. Alexander Schoenhut, Centrun Wiskunde Informatica, Amsterdam.
  • Dr. Helge Roider, Max Planck Institute for Molecular Genetics

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