Z03: Bioinformatic analysis platform

Research Details

  • Project Leaders  
    Prof. Dr. Julien Gagneur
    Institute of Informatics
    Technical University of Munich
    gagneur@in.tum.de

    Prof. Dr. Marcel Schulz
    Institute of Cardiovascular Regeneration
    Goethe University Frankfurt
    marcel.schulz@em.uni-frankfurt.de
  • Research Staff Dr. Fatemeh Behjati Ardakani (Postdoc)
    behjatiardakani@med.uni-frankfurt.de

    Dr. Ranjan Kumar Maji (Postdoc)
    maji@med.uni-frankfurt.de

    Dr. Leonhard Wachutka (Postdoc)
    wachutka@in.tum.de
     
    Vangelis Theodorakis (PhD Student)
    theodora@in.tum.de

    Nina Baumgarten (PhD Student)
    n.baumgarten@em.uni-frankfurt.de

    Dennis Hecker (PhD Student)
    d.hecker@em.uni-frankfurt.de

    Laura Martens (PhD Student)
    martensl@in.tum.de

    Nikoletta Katsaouni (PhD Student)
    katsaouni@em.uni-frankfurt.de

    Gihanna Gaye ST Galindez (PhD student)
    ghanna.galindez@tum.de

The Z03 project supports the research projects of the collaborative network through infrastructure, knowhow transfer and joint technological developments. Building on the first funding period successes, we propose four work packages. In WP 1 (Data and code sharing), we will maintain our central data server for pre-publication, within-consortium, data sharing, and a metadata repository to collect publications and pointers to corresponding datasets deposited in public repositories (menoci.io framework). Further efforts are for integration with national data sharing infrastructures (GHGA, DZHK). Codes will be made open source and trained predictive models shared via the Kipoi repository. In WP 2 (Workshops and training), we will offer our Introduction to R workshop (4-days, once a year), a hands-on initiation into data science, and yearly workshops on bioinformatics tools on focused topics, including single-cell analysis & regulation, RNA-seq & ChIP-seq analysis, and annotation of RNA regulatory sequence elements (RBP-binding, miRNA, and triplex). In WP 3 (Prediction of regulatory sequence elements and regulatory states), we will apply and further develop computational methods inferring activities of transcription factor (TF) and RNA degradation factors from scATAC-seq and/or scRNA-seq data. In collaboration with Z02, we will develop a workflow for the interpretation of RNA-Protein Mass-spec pulldowns, modeling the effect of RBP interactions. In WP 4 (ncRNA functional prediction with multimodal data), we will develop novel methods to predict RNA-DNA interactions extending our work on Triplex prediction. Further, we will provide a web server hosting predicted RNA-DNA interactions for human and mouse non-coding genes. Moreover, we will develop methods to predict the function of non-coding RNAs by leveraging self-supervised representation of multi-modal and multi-species data.

Team Z03