http://www.cnr.it/ontology/cnr/individuo/prodotto/ID278556

mirNET: a web-based system for the analysis of miRNA:mRNA regulatory networks (Abstract/Comunicazione in atti di convegno)

Type

Abstract/Comunicazione in atti di convegno (Classe)
Prodotto della ricerca (Classe)

Label

mirNET: a web-based system for the analysis of miRNA:mRNA regulatory networks (Abstract/Comunicazione in atti di convegno) (literal)

Anno

2014-01-01T00:00:00+01:00 (literal)

Alternative label

Pio G(1) , Ceci M(1) , D'Elia D(2) , Malerba D(1) (2014)
mirNET: a web-based system for the analysis of miRNA:mRNA regulatory networks
in BITS Annual Meeting 2014, Roma, 26-28 February 2014
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Pio G(1) , Ceci M(1) , D'Elia D(2) , Malerba D(1) (literal)

Pagina inizio

22 (literal)

Pagina fine

23 (literal)

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http://bits2014.uniroma2.it/Abstract_Book.pdf (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

BITS Annual Meeting 2014 (literal)

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2 (literal)

Note

Abstract (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

(1) Department of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy (2) CNR, Institute for Biomedical Technologies, Via Amendola 122/D, 70126, Bari, Italy (literal)

Titolo

mirNET: a web-based system for the analysis of miRNA:mRNA regulatory networks (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autoriVolume

Manuela helmer-Citterich, Elisabetta Pizzi, Anna Tramontano (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

Centro Stampa Università degli Studi di Roma La Sapienza: www.editricesapienza.it (literal)

Abstract

Motivation: Understanding mechanisms and functions of microRNAs (miRNAs) is fundamental for the elucidation of many biological processes and of etiopathology of some diseases, such as tumors and neurodegenerative syndromes. We have developed a new biclustering algorithm, i.e. HOCCLUS2 [1], which is able to significantly correlate multiple miRNAs and their target genes to identify potential miRNA:mRNA regulatory networks. More recently, we developed a new probabilistic classifier [2] working in the semi-supervised ensemble learning setting, which allowed us to apply HOCCLUS2 on large-scale prediction data. In order to allow the researchers to exploit the obtained results, we have started to develop a web-based system, called mirNET, for the efficient query, retrieval, export, visualization and analysis of the discovered regulatory networks. Method: In [2], we presented a method which learns to combine the score of several prediction algorithms, in order to improve the reliability of the predicted interactions. The approach works in the semi-supervised ensemble learning setting which exploits information conveyed by both labeled (validated interactions, from miRTarBase) and unlabeled (predicted interactions, from mirDIP) instances. The algorithm HOCCLUS2 exploits the large set of produced predictions, with the associated probability, to extract a set of hierarchically organized biclusters. The construction of the hierarchy is performed by an iterative merging, considering both distance and density-based criteria. Extracted biclusters are also ranked on the basis of the p-values obtained by the Student's T-Test which compares intra- and inter- functional similarity of miRNA targets, computed on the basis of the gene classification provided in Gene Ontology (GO). mirNET database relies on PostgreSQL DBMS, while the web-based platform is built through the Play 2.2 Java framework and the Cytoscape library. Results: The mirNET database stores the set of interactions identified in [2] and the biclusters extracted by HOCCLUS2 from such set of interactions, with different parameters. In particular, mirNET stores approximately 5 million predicted interactions between 934 human miRNAs and 30,875 mRNAs, which are exploited in the construction of the hierarchies of biclusters representing potential miRNA regulatory networks. The mirNET web interface allows users to perform extraction and visualization of single interactions (with the score/probability assigned by the learning algorithm) and of biclusters of interest, as well as to easily browse whole biclusters hierarchies. Biclusters hierarchy browsing (i.e., navigation among parents and children biclusters) helps to identify intrinsic hierarchical organization of miRNAs in each specific context. The interface for the analysis of biclusters also provides a graph-based visualization of the predicted miRNA-gene interaction network. The database query system provides a series of filters to facilitate and refine the retrieval of data on the basis of different criteria, such as the biclusters compactness and the p-values computed on the basis of GO hierarchies, that is pBP and pMF. In particular, the compactness measures the (score-) weighted percentage of interactions in the bicluster, normalized by the maximum number of possible interactions, and represents the average strength of the intra-bicluster connections. pBP and pMF values represent the p-values obtained by the Student's T-Test computed on the basis of Biological Process (BP) and Molecular Function (MF) Gene Ontology hierarchies, respectively. mirNET represents an important contribution to the study of the regulatory role and function of miRNAs. Indeed, as shown in [1] and [2], in addition to the possibility to extract multiple and significant unknown co-targeting of miRNAs, HOCCLUS2 is able to give new clues for the identification of still unknown miRNA functional targeting which could be worth to be experimentally validated. This possibility is due to its ability to associate objects that are apparently not related. This paves the way to the systematic use of mirNET for a comprehensive analysis of all the possible multiple interactions established by miRNAs of interest. Moreover, since mirNET works on computational predictions, it offers the possibility to analyze single interactions and regulatory modules that would be otherwise impossible to reconstruct by considering only experimentally validated interactions, which are strictly dependent on the cell type and experimental conditions used. References: [1] G. Pio, M. Ceci, D. D'Elia, C. Loglisci, D. Malerba, A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs, BMC Bioinformatics 14 (Suppl 7), S8, 2013 [2] G. Pio, M. Ceci, D. D'Elia, D. Malerba, Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach, BMC Bioinformatics (in press) (literal)

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