Principal Investigator
Peter Stadler
Co-Investigator:
Ivo Hofacker,
Christoph Flamm
Abstract
Structural genomics, the systematic determination of all macro-molecular structures represented in a genome, is at present focused almost exclusively on proteins. Although it is commonplace to speak of ``genes and their encoded protein products'', thousands of human genes produce transcripts that exert their function without ever producing proteins. Furthermore, even though the sequence of the human DNA is known by now, the contents of about half of it remains unknown. It is quite likely that a large class of genes has gone relatively undetected so far because they do not make proteins.
The list of functional non-coding RNAs includes key players in the biochemistry of the cell, such as transfer RNAs, ribosomal RNAs, tmRNA, and the RNA components of RNAse P and signal recoginition particles. Another level of RNA function is presented by functional motifs within protein-coding genes, located mostly in the non-translated 5' or 3' regions of the immature messenger RNA.
It is not hard to argue therefore that "RNomics", i.e., the understanding of functional RNAs and their interactions at a genomic level, is of utmost practical and theoretical importance in modern life sciences: The comprehensive understanding of the biology of a cell obviously requires the knowledge of identity of all encoded RNAs, the molecules with which they interact, and the molecular structures of these complexes.
The first step toward this goal is the development of versatile and reliable computational methods that can detect and classify functional RNAs, preferably within a single genome, or in case this proves impossible, from a very small set of related genomes.
We propose here to develop a suite of bioinformatics methods that are specifically geared toward detecting, verifying, and classifying functional RNAs. Our comprehensive approach to "Computational RNomics" will provide