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AgeWa: An integrated approach for Antisense Experiment design

AgeWa: An integrated approach for Antisense Experiment design. P.Arrigo (1) , P.Romano (2) , P.Scartezzini (3) CNR IIET, sezione di Genova e-mail: arrigo@ice.ge.cnr.it Natl. Cancer Res.Institute, Genova,Italy (3) Dept of Neonatology, E.O Ospedali Galliera, Genova,Italy.

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AgeWa: An integrated approach for Antisense Experiment design

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  1. AgeWa: An integrated approach for Antisense Experiment design P.Arrigo(1), P.Romano(2), P.Scartezzini(3) CNR IIET, sezione di Genova e-mail: arrigo@ice.ge.cnr.it Natl. Cancer Res.Institute, Genova,Italy (3) Dept of Neonatology, E.O Ospedali Galliera,Genova,Italy

  2. Structural and Functional Genomics Structural Genomics: Investigation of the biological functionality by using structural biology data ( cristallographic,NMR) Functional Genomics: Investigation of the gene function in its context (pathaway) starting from the outcome of structural Genomics

  3. Integrative approach for drug target validation Small molecule phenotype Expression pattern Knockouts Gene 3D structure Disease Polymorphism Orthologs Family members Genome region Species Function Pathways

  4. Antisense and Functional Genomics HTS Gene expression Genetic screening Antisense design Data Integration Candidate gene Validation

  5. Potential Antisense Target Pre mRNA splicing RNA targets mRNA transport • Cap Site • 3’ UTR • AUG downstream elements Splicing sites Dna targets Major groove Transcriptional inhibition

  6. Target Selection methods 1) Walk the gene 2) Combinatorial approach Optimal hybridisation site selection 3) Rnase H mapping 4) Secondary structure prediction 1) Tethered ASO 2) Triplex forming ASO Screening of Structured RNA binding motifs 3) Minimization of non specific binding 4) Empirical search

  7. AgeWa structure EST Hybridisation simulator AgeWa STS Experimental Validation Remote search Local search

  8. AgeWa Kernel Custom sequence Sequence Tag Selection Learning phase ASO selection rule Complementary Data mining selected

  9. Learning & Tag selection • Preprocessing phase ( segmentation of the custom sequence into the training set X and synaptic score matrix initialisation) • Learning phase  Partition of the X dataset into Cj Classes by using a Winner Take All algorithm.

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