Discovery of mirna-gene regulatory networks by using an integrative data-mining approach: the ComiRNet database

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1 Dipartimento DI INFORMATICA Discovery of mirna-gene regulatory networks by using an integrative data-mining approach: the ComiRNet database Domenica D Elia InterOmics & MIMOmics Symposium 2014 Rome, September 2014

2 Task & Challenging Issues mirna: biological role and functional properties regulate the expression level of thousands of gene in almost all organisms can act in multiple biological processes control cell cycle, differentiation and apoptosis alteration of mirnas expression are found in almost all type of human cancer 2

3 Task & Challenging Issues Computational approaches for the detection of mirnas direct targets are essential for: to complement experimental approaches the formulation of new functional hypothesis to guide experimental validations Pitfalls: high uncertainty of target predictions: false-positive rate 50% results from different prediction algorithms are often uncorrelated to each other and their degree of overlap is poor Domenica D Elia InterOmics & MIMOmics Symposium 2014 Rome, September

4 Problem-specific issues: one mirna can targets many mrnas in the same pathway (pathway-specific) in multiple pathways (universal) Task & Challenging Issues one mrnas can be targeted by many mirnas the effect of a mirna can change depending on which other mirnas are co-expressed or silenced mirna:mrna interactions can differ among tissues and in different conditions the expression level of mirnas is not always anti-correlated with those of targeted mrnas (ultimately mirnas affect proteins level rather than mrnas level) mirnas expressed at low levels are difficult to detect Domenica D Elia InterOmics & MIMOmics Symposium 2014 Rome, September

5 Task & Challenging Issues The Task Improve predictions of mirna targets Support the elucidation of mirnas functional role in the context of gene regulatory networks Domenica D Elia InterOmics & MIMOmics Symposium 2014 Rome, September

6 Predicted interactions (mirdip) o Large datasets o High level of noise (false positives) Verified interactions (mirtarbase) o Context-specific o Small datasets The Approach HOCCLUS2 Hierarchical and Overlapping Co-CLUSterinG A semi-supervised ensemble learning approach which exploits information conveyed by both positive/labeled instances (validated interactions), and unlabeled instances (predicted interactions) Performs better than single prediction algorithms and baseline averaging approaches 6

7 HOCCLUS2 : Hierarchical and Overlapping Co-CLUStering 2 HOCCLUS2 1. Extraction of a set of biclusters in form of bicliques, through an iterative bottom-up strategy This step exploits statistical properties of data and does not require the number of biclusters to extract 7

8 HOCCLUS2 2. Iterative overlapping and merging process, which defines the hierarchy of biclusters Density- and distancebased merging. SVM-based identification of common objects between each pair of biclusters. Hierachies of overlapping biclusters The stopping criterion is based on a cohesiveness threshold 8

9 HOCCLUS2 3. ranking of the extracted biclusters, for each level of the hierarchy. R A N K I N G Ranking is based on the p-value obtained by Student s t-test through which we compare the average intra-bicluster similarity (red edges) to the average inter-bicluster similarity (blue dashed edges), among mrnas. The similarities are pairwise computed, according to the simgic measure, on the basis of the gene classification provided by Gene Ontology. 9

10 HOCCLUS2 Results Based on the principles of our approach genes in a bicluster are likely to function together as a network and mirnas in the same bicluster are likely to cooperatively target groups of networked genes. 10

11 HOCCLUS2 Results HOCCLUS2 for network prediction Discovered Network (bicluster) mirna mrna Possible high similarity among objects of the same type Possible unknown interations among objects of different types. 11

12 The ComiRNet Database Aim Support the biological and clinical research in the elucidation of mechanisms and functional role of mirnas in complex biological processes. Content - approximately 5 million predicted interactions between 934 human mirnas and 30,875 gene transcripts (mrnas) - 15 different hierarchies of overlapping biclusters representing potential mirna regulatory networks Domenica D Elia InterOmics & MIMOmics Symposium Rome, September

13 The ComiRNet Database T-GENE mirna FILTER EXPORT 13

14 The ComiRNet Database H-level Alpha is the minimum cohesiveness value a bicluster must satisfy after merging. It represents the strength of the intra-bicluster connections Beta is the minimum score that must be associated to an interaction to be considered as reliable 14

15 The ComiRNet Database T-GENE mirna Compactness pbp&pmf measures the bicluster cohesiveness and represents the weighted percentage of interactions in the bicluster, normalised by the number of all possible interactions p-values computed on functional similarity among target genes in the biclusters 15

16 The ComiRNet Database 16

17 The ComiRNet Database DEMO 17

18 The ComiRNet Database Domenica D Elia 18

19 Biclusters in Hierarchies A) C) B) 19

20 Conclusions ComiRNet can represent a valuable resource to elucidate the mirnas role in complex biological processes and pathological conditions by exploiting data on their putative functional interactions in the context of predicted MGRN the analysis of mirna:mrna interactions seen in the framework of global regulatory interactions can aid in the: elucidation of mirnas functional mechanisms discovery of new mirna functions discovery of new gene functions interpretation of complex transcriptional patterns Domenica D Elia InterOmics & MIMOmics Symposium 2014 Rome, September

21 Conclusions The approach can be applied to any type of organism an the system integrated in analyses pipeline for omics studies 21

22 The Approach 3

23 The Approach In press 3

24 The Team

25 Consiglio Nazionale delle Ricerche Many thanks for your attention!