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Reconstruction of regulatory modules based on heterogeneous data sources

Reconstruction of regulatory modules based on heterogeneous data sources. Karen Lemmens PhD Defence September 29th 2008. Outline. 1. Introduction 2. Strategy 3. Achievements 4. Conclusions. Introduction & objectives Strategy Data integration

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Reconstruction of regulatory modules based on heterogeneous data sources

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  1. Reconstruction of regulatory modules based on heterogeneous data sources Karen Lemmens PhD Defence September 29th 2008

  2. Outline 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Introduction & objectives • Strategy • Data integration • Association rule mining algorithms • Main achievements • ReMoDiscovery: Unraveling the yeast transcriptional network • DISTILLER: Condition-dependent combinatorial regulation in E. coli • Conclusions and perspectives Karen Lemmens

  3. DNA 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  4. DNA & genes 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 Karen Lemmens

  5. DNA & genes 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1 GENE 2 Karen Lemmens

  6. DNA & genes 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1 GENE 2 Karen Lemmens

  7. DNA & genes 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1 GENE 2 TRANSCRIPTION Karen Lemmens

  8. DNA & genes 1. Introduction 2. Strategy 3. Achievements 4. Conclusions TATCCCTCCCTGTTTATCATTAATTTCTAATTATCAGCGTTTTTGGCTGGCGGCGTAGCGATGCGCTGGTTACTCTGAAAAC GTCTATGCAAATTAACAAAAGAGAATAGCTATGCATGATGCAAACATCCGCGTTGCCATCGCGGGAGCCGGGGGGCGTA GGGCCGCCAGTTGATTCAGGCGGCGCTGGCATTAGAGGGCGTGCAGTTGGGCGCTGCGCTGGAGCGTGAAGGATCTTCT GAGATCACCCATAAGGCGTCCAGCCGTATGACATTTGCTAACGGCGCGGTAAGATCGGCTTTGTGGTTGAGTGGTAAGGA AAGCGGTCTTTTTGATATGCGAGATGTACTTGATCTCAATAATTTGTAACCACAAAATATTTGTTATGGTGCAAAAATAACAC ATTTAATTTATTGATTATAAAGGGCTTTAATTTTTGGCCCTTTTATTTTTGGTGTTATGTTTTTAAATTGTCTATAAGTGCCAAA TCGTCGGTAAGCAGATTTGCATTGATTTACGTCATCATTGTGAATTAATATGCAAATAAAGTGAGTGAATATTCTCTGGAGG GTGTTTTGATTAAGTCAGCGCTATTGGTTCTGGAAGACGGAACCCAGTTTCACGGTCGGGCCATAGGGGCAACAGGTTCG CCTGACCATCGTTCCGGCGCAAACTTCTGCGGAAGATGTGCTGAAAATGAATCCAGACGGCATCTTCCTCTCCAACGGTCC TGGCGACCCGGCCCCGTGCGATTACGCCATTACCGCCATCCAGAAATTCCTCGAAACCGATATTCCGAATTACATGTTTTG DNA mRNA protein GENE 1 GENE 2 GENE 1 GENE 2 TRANSCRIPTION TRANSLATION Karen Lemmens

  9. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 Karen Lemmens

  10. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 Karen Lemmens

  11. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 TRANSCRIPTION TRANSLATION Karen Lemmens

  12. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 GENE 1 TRANSCRIPTION TRANSLATION Karen Lemmens

  13. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 GENE 1 TRANSCRIPTION TRANSLATION Karen Lemmens

  14. Condition-dependent transcription 1. Introduction 2. Strategy 3. Achievements 4. Conclusions DNA mRNA protein GENE 1 GENE 1 TRANSCRIPTION TRANSLATION Karen Lemmens

  15. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions GENE 1 Karen Lemmens

  16. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Karen Lemmens

  17. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Regulators GENE 1 Karen Lemmens

  18. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Regulators GENE 1 Karen Lemmens

  19. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Regulators GENE 1 Karen Lemmens

  20. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Regulators GENE 1 GENE 1 Karen Lemmens

  21. Transcriptional regulation 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Regulatory motifs GENE 1 Regulators GENE 1 GENE 1 Karen Lemmens

  22. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  23. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  24. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  25. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  26. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  27. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  28. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  29. Transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  30. Transcriptional modules 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  31. Outline 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Introduction & objectives • Strategy • Data integration • Association rule mining algorithms • Main achievements • ReMoDiscovery: Unraveling the yeast transcriptional network • DISTILLER: Condition-dependent combinatorial regulation in E. coli • Conclusions and perspectives Karen Lemmens

  32. Data integration 1. Introduction 2. Strategy 3. Achievements 4. Conclusions GENE 1 Karen Lemmens

  33. Data integration 1. Introduction 2. Strategy 3. Achievements 4. Conclusions ChIP-chip data GENE 1 Karen Lemmens

  34. Data integration 1. Introduction 2. Strategy 3. Achievements 4. Conclusions ChIP-chip data GENE 1 Regulatory motifs Karen Lemmens

  35. Data integration 1. Introduction 2. Strategy 3. Achievements 4. Conclusions ChIP-chip data Microarray data GENE 1 Regulatory motifs Karen Lemmens

  36. Network reconstruction 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Several methods for reconstruction of the transcriptional network exist Not all aspects of transcription taken into account by these methods ** Van den Bulcke T., Lemmens K., Van de Peer Y., Marchal K. (2006) Inferring Transcriptional Networks by Mining Omics Data. Current Bioinformatics, vol. 1, no. 3, pp. 301-313. ** Dhollander T., Sheng Q., Lemmens K., De Moor B., Marchal K., Moreau Y. (2007) Query-driven module discovery in microarray data. Bioinformatics, vol. 23, no. 19, pp. 2573-2580. Expression data Data integration Boolean ODE Bayesian Association (CLR, ARACNE) Bayesian SEREND Individual interactions Clustering Biclustering Query-driven biclustering Method of Segal et al. LeMoNe GRAM MA-Networker SAMBA Inferelator COGRIM Transcriptional modules Karen Lemmens

  37. Association rule mining 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Association rule mining algorithms • Advantages: • Enable exhaustive search • Elegant and concurrent data integration • No co-expression assumption between regulator and target • Overlapping modules • Problems • Binary or discretized data • Filtering method necessary Karen Lemmens

  38. Outline 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Introduction & objectives • Strategy • Data integration • Association rule mining algorithms • Main achievements • ReMoDiscovery: Unraveling the yeast transcriptional network • DISTILLER: Condition-dependent combinatorial regulation in E. coli • Conclusions and perspectives Karen Lemmens

  39. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Karen Lemmens

  40. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions Represent data in a mathematical way Karen Lemmens

  41. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Transcriptional module • Genes are regulated by a minimum number of regulators • Genes share minimum number of common regulatory motifs • Genes are co-expressed Karen Lemmens

  42. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Transcriptional module • Genes are regulated by a minimumnumber of regulators • Genes share minimum number of common regulatory motifs • Genes are co-expressed Karen Lemmens

  43. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Transcriptional module • Genes are regulated by a minimumnumber of regulators • Genes share minimum number of common regulatorymotifs • Genes are co-expressed Karen Lemmens

  44. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Transcriptional module • Genes are regulated by a minimumnumber of regulators • Genes share minimum number of common regulatorymotifs • Genes are co-expressed Karen Lemmens

  45. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Regulatory program: Regulators: Motifs: MBP1 SWI4 SWI6 STB1 • Co-expressed genes: YDL003W YER001W YGR109C YGR189C YGR221C YHR149C YER070W YPL256C YNL300W YPL163C YPL267W YPR120C YMR199W YMR199W YMR179W YML027W YKL113C Karen Lemmens

  46. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • ReMoDiscovery outperforms related methods for module detection • GRAM • SAMBA • Conclusions • Meaningful biological results • Better performance than related methods association rule mining algorithms are well suited for identification of regulatory modules through data integration Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderickx J., De Moor B., Marchal K. (2006) Inferring transcriptional module networks from ChIP-chip-, motif- and microarray data. Genome Biology, vol. 7, no. 5, pp. R37. Karen Lemmens

  47. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Many aspects of transcription into account: • Regulatory motifs • Regulators • Co-expression of genes Condition dependency of the interactions is missing Karen Lemmens

  48. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Many aspects of transcription into account: • Regulatory motifs • Regulators • Co-expression of genes Condition dependency of the interactions is missing Karen Lemmens

  49. ReMoDiscovery: Unraveling the yeast transcriptional network 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Many aspects of transcription into account: • Regulatory motifs • Regulators • Co-expression of genes Condition dependency of the interactions is missing Karen Lemmens

  50. Outline 1. Introduction 2. Strategy 3. Achievements 4. Conclusions • Introduction & objectives • Strategy • Data integration • Association rule mining algorithms • Main achievements • ReMoDiscovery: Unraveling the yeast transcriptional network • DISTILLER: Condition-dependent combinatorial regulation in E. coli • Conclusions and perspectives Karen Lemmens

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