PhD: Reconstruction of a fish protein interactome

PhD opportunity at the University of Liverpool, UK: In recent years a range of different fish species have become promising vertebrate models in biology. supported by the sequencing and assembly and annotation of several fish genomes. Since Y2000 scientists at the University of Liverpool have pioneered the creation of genomic resources for various Euteleostean species (trout, carp, flounder, zebrafish, eel) including customised microarrays which generated unique data sets relating to abiotic stress responses, ecotoxicity, hydrostatic pressure, and disease tolerance. These datasets contains dimensions that have not been fully explored. Thus, gene co-expression between different experimental conditions indicates a functional coupling and functional links in functional connectivity matrices. However, there is currently no generate a taxon-wide integration of gene lists along with functional attributes, that might provide insights into genome evolution, and physiological adaptation. Also, there is no reliable fish-specific ‘reactome’ available to the fish community.

This bioinformatics project will focus first on a thorough cross-species gene mapping of data from array repositories, and establishing homologies and paralogies across the wide taxonomic range of fishes. Second, it will seek to identify patterns of co-expression that are conserved across species. Using new methods of organism-specific functional networks and of network alignment we will reconstruct a weighted fish-specific ‘reactome’ using the existing resources (BioGrid, INtAct, BioMart, FunCoup, InParanoid etc). We will also seek to identify gene neighbourhoods in Danio rerio and Tetrodion genomes and use the conserved links as an additional indication of functional coupling between pairs of genes. This and expression meta-analysis data will help us to refine the reconstructed network and to weight probabilities of predicted interactions. The project should lead to the development of the valuable resource for the systemic analysis of fish ‘Omics’ datasets.

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