Integrative transcriptomics to study non-coding RNA functions

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Integrative transcriptomics to study non-coding RNA functions
by dr. ir. Pieter Mestdagh - Center for Medical Genetics, Ghent University

Over the last years, non-coding RNAs (e.g. microRNAs and long non-coding RNAs) have emerged as an important layer of the transcriptome. In order to elucidate their function in disease biology, multiple tools have been developed, ranging from miRNA target prediction algorithms to the more advanced integrative genomics approaches. Through the combination of multiple layers of information, integrative genomics allows a more accurate and comprehensive assessment of non-coding RNA functions in human disease. In this presentation, I will discuss different approaches on how to combine multi-level transcriptome data in order to functionally characterize non-coding RNA networks.

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  • To Further refine these functional predictions we can add additional information such as network models
  • Integrative transcriptomics to study non-coding RNA functions

    1. 1. integrative transcriptomics to study<br />non-coding RNA functions<br />Pieter Mestdagh<br />bioinformatics tools in research<br />September 28 - 2011<br />
    2. 2. introduction<br />the transcriptome<br />DNA<br />miRNA<br />miRNA<br />miRNA<br />mRNA<br />mRNA<br />mRNA<br />lncRNA<br />lncRNA<br />protein<br />protein<br />protein<br />cellular functions and processes<br />… – growth – differentiation – apoptosis – migration – cell cycle regulation – signal transduction – transcription - … <br />
    3. 3. introduction<br />expression levels of each coding and non-coding transcript?<br />functions of and interactions between coding and non-coding RNAs?<br />cellular functions and processes<br />… – growth – differentiation – apoptosis – migration – cell cycle regulation – signal transduction – transcription - … <br />whole-genome expression profiling<br />mRNA-lncRNA<br />hybridisation-based technology<br />RNA sequencing<br />miRNA<br />hybridisation-based technology<br />RT-qPCR technology<br />small-RNA sequencing<br />integrative transcriptomics<br />to define non-coding RNA<br />functions <br />
    4. 4. functional annotation of miRNAs<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    5. 5. functional annotation of miRNAs<br />integrated approach for functional miRNAannotation<br />guilt by association: <br />if 2 genes share the same expression pattern they share a common<br />transcriptional regulator and are likely to function in the same pathway<br />for each miRNA<br />rank mRNAs by<br />correlation coeff<br />enriched gene<br />sets per miRNA<br />Spearman’s rank<br />Matrix:<br />500 x 20 000<br />expression data<br />500 miRNAs<br />20 000 mRNAs<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    6. 6. functional annotation of miRNAs<br />Gene Set Collections<br />miR-1<br />gene A<br />gene B<br />gene C<br />…<br /> positive<br />correlation<br />CGP<br />GO<br />KEGG<br />gene set A<br />gene set I<br />gene set X<br />gene set B<br />gene set J<br />gene set Y<br />…<br />…<br />…<br />gene set I<br />gene set J<br />TGFBR2<br />TP53<br />SMAD2<br />PUMA<br />SMAD4<br />BAX<br />CDKN1A<br />CDKN1A<br />…<br />gene X<br />gene Y<br />gene Z<br />SERPINE1<br />negative<br />correlation<br />Subramanianetal., PNAS, 2005<br />
    7. 7. functional annotation of miRNAs<br />miR-1<br />gene sets enriched among <br />positively correlated mRNAs <br />gene A<br />gene B<br />gene C<br />…<br /> positive<br />correlation<br />putative<br />miRNA<br />functions<br />gene sets enriched among <br />negatively correlated mRNAs <br />…<br />gene X<br />gene Y<br />gene Z<br />negative<br />correlation<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    8. 8. functional annotation of miRNAs<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    9. 9. functional annotation<br />Integrate GSEA data with network models<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    10. 10. functional annotation of miRNAs<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    11. 11. Integrate GSEA data with network models and miRNA/TF target prediction<br />miRNA target recognition<br />SEED<br />mRNA 3’UTR<br />miRNA<br />6<br />5<br />8<br />2<br />7<br />4<br />3<br />miRNA target prediction algorithms<br />presence of 3’UTR seed site<br />conservation of seed site<br />free energy<br />…<br />
    12. 12. functional annotation<br />Integrate GSEA data with network models and miRNA/TF target prediction<br />enrichment of miRNA<br /> targets in gene set<br />enrichment of TF<br /> targets in gene set<br />AND<br />TF is predicted miRNA target<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    13. 13. functional annotation<br />SUMMARY workflow for prediction of miRNA function<br />miRNA<br />targets<br />miRNA<br />GSEA<br />network<br />models<br />miRNA<br />function<br />X<br />mRNA<br />TF<br />targets<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    14. 14. functional annotation<br />Validation of predicted miRNA functions – transcription factor targeting<br />miRNAspredicted to target the MYCN transcription factor<br />MYCN<br />targeting<br />transcription factor<br />targeting<br />miRNAs negatively correlated to gene sets<br />representing MYC/MYCN targets<br />miRNAs predicted to target MYC/MYCN using<br />MIRDB predictions<br />miR<br />miR<br />ODC1<br />T1<br />MYCN<br />C<br />PTMA<br />T2<br />…<br />miR-29 miRNA family<br />miR-29a<br />miR-29b<br />miR-29c<br />SCHUHMACHER_MYC<br />TARGETS_UP<br />SCHLOSSER_MYC_TARGETS<br />REPRESSED_BY_SERUM<br />SCHUHMACHER_MYC<br />TARGETS_UP<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    15. 15. functional annotation<br />Validation of predicted miRNA functions – transcription factor targeting<br />Validation of the predicted miR-29 – MYCN interaction<br />A<br />B<br />*<br />*<br />vector<br />WT<br />WT<br />MUT<br />MUT<br />pre-miR-29a<br />negative control<br />pre-miR<br />NC<br />29a<br />NC<br />29a<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    16. 16. functional annotation<br />The miRNA body map<br />webtool to query functional miRNA annotation<br />different datasets<br />different gene set collections from the MSigDB (GO_bp, GO_mf, CGP)<br />different network models<br />www.miRNAbodymap.org<br />Mestdagh, Lefever et al., Nucleic Acids Res, 2011<br />
    17. 17. conclusions<br />Conclusions<br />Correlative analysis of mRNA and miRNA expression data in combination with existing tools (GSEA) allows to infer gene sets associated to miRNAs<br />Gain insights in the mechanisms underlying a miRNA – gene set association by integrating TF target and miRNA target information<br />Approach not restricted to miRNAs. Similar workflow for functional annotation of lncRNAs<br />Don’t fear high-dimensional datasets<br />
    18. 18. acknowledgements<br />Center for Medical Genetics, Ghent, Belgium<br />Steve Lefever, Filip Pattyn, Annelies Fieuw, Maté Ongenaert, Frank Speleman, Jo Vandesompele<br />Applied Biosystems, Foster City, USA<br />Dana Ridzon, Linda Wong, Caifu Chen<br />Belgian<br />Cancer<br />Plan<br />

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