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RECOMB/ISCB 2013 Conference on Regulatory and Systems Genomics, with DREAM Challenges

RECOMB/ISCB 2013 Conference on Regulatory and Systems Genomics, with DREAM Challenges. Systematic exploration of autonomous modules in noisy microRNA-target networks for testing the generality of the ceRNA hypothesis Danny Kit-Sang Yip 1 and Kevin Y. Yip 1,2,3

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RECOMB/ISCB 2013 Conference on Regulatory and Systems Genomics, with DREAM Challenges

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  1. RECOMB/ISCB 2013Conference on Regulatory and Systems Genomics, with DREAM Challenges Systematic exploration of autonomous modules in noisy microRNA-target networks for testing the generality of the ceRNAhypothesis Danny Kit-Sang Yip1and Kevin Y. Yip1,2,3 1 Department of Computer Science and Engineering, 2 Hong Kong Bioinformatics Centre, 3 CUHK-BGI Innovation Institute of Trans-omics,

  2. Regulatory roles of miRNAs • Two main roles: • Repress translation • Promote mRNA degradation • Consequence: anti-correlation between expression levels of a miRNA and its target • If they are not in a network with other miRNAs and mRNAs • Biological relations among the group of miRNA targets

  3. miRNA network • Many-to-many relationships • One miRNA can have many targets • One mRNA can be targeted by many miRNAs TSSK2 SNX2 miR-140-5p C14orf101 SLC33A1 (Based on union of multiple target prediction methods)

  4. Previous work on miRNA networks • Mostly overlaying expression on predicted miRNA-target network • To find out more confident pairs/modules • Reviewed in Liu et al., Briefings in Bioinformatics 2012: • Yoon and De Micheli, Bioinformatics 2005 • Huang et al., Nature Methods 2007 • Joung et al., Bioinformatics 2007 • Tran et al., Bioinformatics 2008 • Joung et al., Bioinformatics 2009 • Liu et al., BMC Bioinformatics 2009 • Liu et al., Journal of Biomedical Informatics 2009 • Pang et al., BMC Genomics 2009 • Bonnet et al., PLoS ONE 2010 • Liu et al., Bioinformatics 2010 • Nunez-Iglesias et al., PLoS ONE 2010 • Zhang et al., Bioinformatics 2011

  5. How can we get a more conceptual understanding about the quantitative roles of miRNA in gene regulation?

  6. The ceRNA hypothesis • Competing endogenous RNA (Salmena et al., 2011, Pier Paolo Pandolfi’s group) • Competition between targets for finite copies of miRNA Buffering between targets of a miRNA • Back-regulation • Example: PTEN and PTENP1 in cancer Image credit: Salmena et al., Cell 146(3):353-358, (2011)

  7. Modeling mRNA levels due to miRNAs • Recent work has shown possibility for quantitative modeling (Ala et al., 2013) • S: miRNA; R1, R2: mRNA targets of S Image credit: Ala et al., PNAS 110(18):7154-7159, (2013)

  8. Applying globally • Ultimate goal: Model expression changes of mRNAs due to targeting miRNAs • Difficulties: • Extensive connections: Many model parameters • Need a lot of expression data • Expensive computations • Imperfect input network • Noisy predictions • Few validated pairs • Other regulatory mechanisms, indirect effects • Only partial effects at transcriptional level

  9. Extensive connections: Many model parameters • Need a lot of expression data • Expensive computations mRNA4 mRNA1 mRNA2 mRNA3 miRNA1 miRNA2 miRNA3 miRNA4

  10. Imperfect input network • Noisy predictions • Few validated pairs LPR6 Has interaction? Tarbase TargetScan PicTar … etc miRGen PITA miRanda … etc hsa-miR-7-5p

  11. Other regulatory mechanisms, indirect effects Histone modification Transcription factor DNA methylation Other non-coding RNAs

  12. Only partial effects at transcriptional level mRNA Inhibition? Degradation? miRNA

  13. Our solution: finding autonomous modules (without using expression data)

  14. Materials • miRNA-mRNA Target Interaction Pairs • Validated Interactions: Tarbase v6 • Predicted Interactions: TargetScan v6.0, PITA v6, PicTar Hg18, miRGen v2.0 and miRanda Aug 2010 • RNASeq Expression Data for mRNAs and miRNAs • RNASeq Cell line data available from ENCODE Project (RNA Dashboard Hg18, TSS v7) • 12113 mRNAs and 316 miRNAs in 10 cell lines • Gene Annotation Files (Gene Ontology) • Term-term association file • Term-gene association file

  15. Autonomous modules • Idealized definition:A group of miRNAs and mRNAs in which • The miRNAs do not target other mRNAs • The mRNAs are not targeted by other miRNAs • All the miRNAs target all the mRNAs (total buffering) mi1 mi2 mi3 mi4 mi5 mi6 mi7 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10

  16. Autonomous modules • Idealized definition:A group of miRNAs and mRNAs in which • The miRNAs do not target other mRNAs • The mRNAs are not targeted by other miRNAs • All the miRNAs target all the mRNAs (total buffering) mi1 mi2 mi3 mi4 mi5 mi6 mi7 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 MiRNA-Target Bicluster (MTB), Type R (Restrictive)

  17. miRNAs Type-R MTBs R mRNAs • Potential issues: • Sensitive to errors in network • Too restrictive • Few cases found • Very small • Solutions: • Define less restrictive types • Allowing the mRNAs to be targeted by extra miRNAs (mi) • Allowing the miRNAs to target extra mRNAs (m) • Or the general case that allow both (gen) • Allowing the miRNAs not targeting all the mRNAs (Loose, L) Not obvious which definition is better. Found all of them and performed empirical comparisons Lmi Rm L Rmi Lm Rgen Lgen

  18. Questions about MTBs • Computational questions: • How many MTBs of each type can there be in a network? Practical to study all? • How to find MTBs from a network? • Biological questions: • Are the expression of miRNAs and mRNAs strongly anti-correlated? • Validity check of biological relevance • Do the mRNAs buffer each other? • Is ceRNA a general phenomenon? • Do the mRNAs share similar functions? (#1-#3: As compared to random miRNA-mRNA pairs and miRNA-target pairs not in MTBs) • Any other applications?

  19. Computational questions Tractable number of MTBs: return all Intractable number of MTBs: either 1. Return all maximal MTBs OR 2. Return high-scoring ones, with little violations to the idealized definition Corresponding algorithms

  20. Computational difficulties • Within the MTB: • All miRNAs and mRNAs target each another. • Outside the MTB: • Both miRNAs and mRNAs can target some extra RNAs. Type Rgen • Identifying type Rgen MTBs is the same as identifying bi-cliques in the bipartite graph • Known to be an NP-complete problem.

  21. Biological questions • #1: Are the miRNAs and mRNAs in our MTBs more anti-correlated than background? • Workflow: MTB score: Inputs miRNA-target pairs Expression levels MTB Identification Background construction Anti-correlated Correlated Computing MTB scores Computing background scores Score: 7/9 (correlations < t=-0.2) MTB score distribution: 7/9, 6/12, 8/10, 19/21, ... Background score distribution: 3/12, 2/15, 4/10, 3/18, ... Statistical test: The two score distributions significantly different?

  22. An example • Human miRNA-target network:union of • High-confidence predictions of 5 computational methods • TarBase validated pairs • Expression: ENCODE RNA-seq • MTB type: Lgen

  23. Statistical significance • Background: Random miRNA-mRNA pairs • Significant results in general • More significant for the less idealized cases

  24. Statistical significance • Background: miRNA-target pairs not in same MTB • Also tested: • High-coverage set, without validated pairs, grouping of related miRNAs – MTBs are biologically meaningful in general

  25. Biological questions • #2: Do the mRNAs in the same MTB buffer each other in expression? • Similar workflow. Testing statistics: • Positive correlations among the mRNAs • Could be simply due to co-regulation (e.g., saturated miRNA) • d(R, T1, T2) =(R, T1 | T2) – (R, T1) • R: Regulator (miRNA) • T1: Target 1 (mRNA) • T2: Target 2 (mRNA) • : Correlation • d is negative if correlation between R and T1 is improved by having the information of T2 T1 R T2

  26. Significant results • Type-Lgen MTBs • mRNAs in the same MTB buffer each other Statistic 1: Positive mRNA correlation Statistic 2: Extra miRNA-target1 anti-correlation due to target2

  27. Biological questions • #3: Do mRNAs in the same MTB share similar functions? • Enrichment analysis using Gene Ontology • Compare distributions of most significant p-values • Even more significant with the high-coverage set • mRNAs in the same MTB are functionally related

  28. Biological questions • #4: Any other applications of MTBs? • Annotating miRNAs • Associating with annotated mRNAs/miRNAs • (Observed significant positive correlations among miRNAs in the same MTB) • Refining miRNA-target networks • E.g., increasing confidence of predictions within MTBs • Hierarchical modeling of expression changes due to miRNAs • Applying to other types of regulatory networks

  29. Acknowledgement • The department of computer science and engineering, the Chinese University of Hong Kong • The Hong Kong Bioinformatics Centre • Dr. Sarah Djebali

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