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J Struct Funct Genomics. 2003;4(2-3):87-96.

The German cDNA network: cDNAs, functional genomics and proteomics.

Author information

  • 1Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany. s.wiemann@dkfz.de

Abstract

Among the greatest challenges facing biology today is the exploitation of huge amounts of genomic data, and their conversion into functional information about the proteins encoded. For example, the large-scale cDNA sequencing project of the German cDNA Consortium is providing vast numbers of open reading frames (ORFs) encoding novel proteins of completely unknown function. As a first step towards their characterization we have tagged over 500 of these with the green fluorescent protein (GFP), and examined the subcellular localizations of these fusion proteins in living cells. These data have allowed us to classify the proteins into subcellular groups which determines the next step towards a detailed functional characterization. To make further use of these GFP-tagged constructs, a series of functional assays have been designed and implemented to assess the effect of these novel proteins on processes such as cell growth, cell death, and protein transport. Functional assays with such a large set of molecules is only possible by automation. Therefore, we have developed, and adapted, functional assays for use by robotic liquid handling stations and reading stations. A transport assay allows to identify proteins which localize to distinct organelles of the secretory pathway and have the potential to be new regulators in protein transport, a proliferation assay helps identifying proteins that stimulate or repress mitosis. Further assays to monitor the effects of the proteins in apoptosis and signal transduction pathways are in progress. Integrating the functional information that is generated in the assays with data from expression profiling and further functional genomics and proteomics approaches, will ultimately allow us to identify functional networks of proteins in a morphological context, and will greatly contribute to our understanding of cell function.

PMID:
14649292
[PubMed - indexed for MEDLINE]
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