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Genome Informatics 2005 Meeting Report Cold Spring Harbor, Oct 28-Nov1 Peter E.M. Taschner PT 11-05 Genome Informatics 2005 • • • • ~ 220 participants 1 keynote speaker: David Haussler 47 talks 121 posters Rodger Voelker:Two classes of splice junctions • Search for 5-7 base motifs in exonic and intronic flanking sequences of known splice junctions • Computational analysis of collocations between different motifs • Many collocations between exonic and intronic sequences • Known ESEs display collocations with intronic sequences (including ISEs) • Nearly all introns (89%) can be classified into 2 classes Chip Lawrence: futility of optima in inferences • The strong focus in bioinformatics on optimal solutions is fundamentally flawed, because the asymptotic underpinnings of these solutions, such as consistency, do not apply • The curse of dimensionality can render optimal solutions very unlikely and misleading • Example: minimum free energy predictions of RNA structures • Reason: incomplete energy function used, only sec structure considered, no tertiary Minimum free energy predictions of RNA structures • Assumption: – molecule folds into lowest energy state – unique solution to folding problem (optimum) • Many programs (e.g. Zuker's Mfold) use the Boltzmann probability function – Most include calculations of suboptimal structures – but not all structures are computed – PPV of MFE: 48 % Alternative prediction of RNA structures • Sample the ensemble of sec structures in proportion to their Boltzmann weights • Cluster the structures • Use centroid structure in predictions – Improved PPV compared to MFE • Srna module of Sfold (http://sfold.wadsworth.org/ ) A.tumefaciens 5S rRNA energy landscape Alternative prediction of RNA structures • Improved PPV compared to MFE: – Ensemble centroid + 30 % – Largest cluster centroid +18 % – Best centroid + 47 % Data mining • Geneseer – searchable name-translation database (http://geneseer.cshl.org/ ) • Access to genomic information through gene names • Mapping sequences to gene names • Identification of homologs across several species for a given gene • Used in RNAi Codex (http://codex.cshl.edu ) Data mining • Ulysses – annotate human genes based on gene interactions in model organisms (http://www.cisreg.ca:8080/ulysses/ ) • Interologs: conserved protein-protein interactions • Regulogs: conserved protein-DNA interactions • Almost no overlap between data in interaction databases • BIND DIP: 984 refs; BIND 5 DB's: 3 refs Data mining • Integrated Genome Browser (IGB) – visualize: – Genomic annotations from multiple data resources – Experimental data from Affymetrix arrays (http://www.affymetrix.com/support/developer/ tools/download_igb.affx ) Gene expression and pathways • Skypainter tool in Reactome database: – allows overlay of gene expression data on pathway graphs – allows generation of a "movie" of a time series • (http://www.reactome.org/ ) Gene expression • ArrayBlast: • Compares gene expression signatures generated on different platforms • Uses public microarray data sets (GEO) • Used to create conserved cancer-related expression signature • (http://seq.mc.vanderbilt.edu/arrayBlast/ ) Gene expression • C. elegans Gene Expression Consortium: • SAGE data from specific stages, tissues and cell types • Database of gene expression data/pictures/movies of transgenic worms with promoter::GFP fusions for 2000 genes with human orthologs (http://elegans.bcgsc.ca/home/ge_consortium.html ) Michael Caudy: Whole genome analysis of combinatorial and architectural transcription codes • Search for TFBS in known neural pathway genes • Determine architecture: number, type, order, orientation and spacing of TFBS • Compare architecture of activated and repressed genes • Determine activity of promoters with TFBS mutations • Architecture is critical for differential response to Notch signalling Regulatory sequence identification • Evoprinter: • highlights multi-species conserved sequences within orthologous DNAs in the context of a single species of interest • (http://evoprinter.ninds.nih.gov/ ) Regulatory sequence identification • NestedMICA: – method for discovering many over-represented short motifs in large sets of strings in a single run – candidate transcription factor binding sites • (http://www.sanger.ac.uk/Software/analysis/n mica/ )