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In silico pathway analízisek a genomikai kutatásban

In silico pathway analízisek a genomikai kutatásban. Falus András Semmelweis Egyetem. Biological Complexity in „ omics ” era. transcriptome. genome. proteome. metabolome. DNA. protein. mRNA. RNA. metabolites. Intron. Exon. Polymorphism. …. Promoter.

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In silico pathway analízisek a genomikai kutatásban

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  1. In silico pathway analízisek a genomikai kutatásban Falus András Semmelweis Egyetem

  2. Biological Complexityin „omics” era transcriptome genome proteome metabolome DNA protein mRNA RNA metabolites Intron Exon Polymorphism … Promoter Chromosome-level alterations microRNA Splicing microvesiculum

  3. 1000000 900000 800000 700000 600000 500000 400000 300000 200000 100000 0 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 ID gene networks pathway analysis GCAATCGATCTGGTACAGTAGCTA GCAATTGATCGGGTACATTAGCTA appr. 20 million SNP / human genome SNP prot CGH databases expression microarrays mRNA/miRNA

  4. gene A gene B Gene networks? gene C gene D Cells Expression levels measure for several conditions A E B D C microarray experiment gene network model Data from single time points, single conditions vs phenotypes (e.g. diseases) based on complex gene networks Reverse modelling of gene network based on microarray data

  5. international bibliographic data mining, „pathway analysis” biobank SYSTEMS BIOLOGY

  6. Gene pathway analysis

  7. Literature Sequence databases Example TEXT MINING IUIS Allergen repository PubMed abstracts MANUAL Cross-reactivity data Allergen sequences Miotto O., Tan T.W., Brusic V. (2005). Extraction by example: induction of structural rules for the analysis of molecular sequence data from heterogeneous sources. Lecture Notes in Computer Science 3578, 398-405. Miotto O, Tan TW, Brusic V. (2005). Supporting the curation of biological databases with reusable text mining. Genome Informatics 16(2), 32-44. TOOLS: Search, BLAST, 3D visualisation, allergenicity, allergic cross-reactivity ALLERDB data warehouse

  8. Networks: need of standardisation of visualisation http://www.cytoscape.org/ http://www.genome.ad.jp/kegg/ KEGG: Kyoto Encyclopedia of Genes and Genomes http://www.reactome.org

  9. Genome-wide susceptibility regions in asthma 15 chromosomes/20 cluster Szalai et al

  10. simplified view of gene networks in asthma (2006) http://www.snps3d.org/

  11. Mouse asthma model 29 0 14 2830 31 I.p. OVA (I.p.) I.p. OVA OVA inhalation + Al(OH)3 + Al(OH)3 1 % Induction Sensitisation I SensitisationII Airway hypersensitivity at day 31 Gene expression analysis at days 28, 30, 31 Stage I, II, III Lung tissue samples 4 hours after OVA inhalation, RNA isolation —microarray 44K genes (Whole Mouse Genome 60-mer Oligo Microarrays, Agilent Technologies)

  12. Ovalbumin-induced allergic asthma model in mouse level of significance number of molecules involved comprehensive categories

  13. magyarazo abrak: https://analysis.ingenuity.com/pa/info/help/help.htm#ipa_help.htm

  14. What kind of evidences do underscore the eosinophil infiltration process

  15. The different signaling activation patterns represent the development of asthma in our mouse model The significance (p-value) is better at asking, "Is there an association between a specific pathway and my uploaded dataset and is it due to chance?" The null hypothesis is that there is no association. If a p-value is very small you can be confident that the pathway is associated with the uploaded dataset. This may be an indication of that certain pathways are more likely to explain the phenotype that is observed. OVA inhalation, days 28, 30, 31 The number of molecules in a given pathway that meet cutoff criteria, divided by total number of molecules that make up that pathway.

  16. “Eosinophil recruitment” signaling: early induction Stadium 1.

  17. “Eosinophil recruitment” signaling: early induction Stadium 2.

  18. “Eosinophil recruitment” signaling: early induction Stadium 3.

  19. Involvement of chitinase family in the pathogenesis of allergic asthma AMCASE acidic chitinase Which chitinases are affected in our model, evidences in literature

  20. Validation by RT-PCR

  21. Categorized evidences in literature: CHIA (AMCASE)

  22. Categorized evidences in literature: CHIA (AMCASE) potential diagnostic utilization

  23. Categorized evidences in literature: CHIA (AMCASE)

  24. Paraoxonase-1

  25. Down-regulation of paraoxonase I gene in asthmatic lung of mice control asthma HE 10x 10x PON1 20x 20x

  26. Listing mRNAs inhibited by miRNA

  27. miRNA mechanism of regulation Integrating the miRNA-knowledge to the network analysis concept • miRNA: new layer in regulation of gene expression • How to integrate? • Pathway analysis applications • (e.g. Ingenuity): • Other integrative approaches • Connection point: target prediction algorithms • Target prediction • based on • the sequence complementarity, • free energy calculation and • evolutional conservation Analysis of the function

  28. Conclusion • Pathway analysis provides a „bibliomics” approach in visualization of literature • Explanations, concepts and new ideas are raised • More direct link is understood between gene network and phenotype (e.g. diseases)

  29. Köszönet • Buzás Edit • Molnár Viktor • Pócza Péter • Szalai Csaba • Tölgyesi Gergely • Ungvári Ildikó • Wiener Zoltán Dept. Genetics, Cell- and Immunobiology, Semmelweis University Budapest

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