ComiRNet: a database of predicted miRNA:mRNA interactions and regulatory networks (Abstract/Comunicazione in atti di convegno)

Type
Label
  • ComiRNet: a database of predicted miRNA:mRNA interactions and 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) , Malerba D(1), D'Elia D (2) (2014)
    ComiRNet: a database of predicted miRNA:mRNA interactions and regulatory networks
    in NGS and non-coding RNA data analysis" workshop II (2014), Plovdiv, Bulgaria, 15-16 May 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Pio G(1) , Ceci M(1) , Malerba D(1), D'Elia D (2) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • The workshop was organised as networking activity of the SeqAhead COST ACTION PROJECT (literal)
Note
  • Comunicazione (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
  • ComiRNet: a database of predicted miRNA:mRNA interactions and regulatory networks (literal)
Abstract
  • Background Computational methods are fundamental in the identification of miRNAs target site and in the reconstruction of interacting regulatory networks they are able to control. Understanding mechanisms and functions of microRNAs (miRNAs) is pivotal for the elucidation of many biological processes and of etiopathology of some diseases, such as tumors and neurodegenerative syndromes. ComiRNet (Co-clustered miRNA Regulatory Networks) is a new database which collects data of miRNA:mRNA interactions and interacting networks by exploiting human miRNAs target predictions from 10 different databases stored in mirDIP. These data have been produced by using a combined data mining approach based on biclustering and semi-supervised ensemble-based learning techniques. ComiRNet provides a user-friendly graphical interface (GUI) for efficient query, retrieval, export, visualization and analysis of the discovered regulatory networks. Availability: ComiRNet is available at http://193.204.187.158:9002/ Method In [1], we presented a method which learns to combine the scores 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 [3]) and unlabeled (predicted interactions, from mirDIP) instances. The algorithm HOCCLUS2 [2] 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). The ComiRNet database relies on PostgreSQL DBMS, while the web-based platform is built through the Play 2.2 Java framework and the Cytoscape library. Results ComiRNet 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 ComiRNet 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 and functional relationships between different miRNAs and their predicted functional co-targeting on different groups of genes. The interface for the analysis of biclusters also provides a graph-based visualization of the predicted miRNA-gene interaction networks. Conclusions ComiRNet represents an important contribution to the study of the regulatory mechanisms and functions of miRNAs. The use of computational predictions in place of only experimentally validated interactions offers the possibility to detect 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. This paves the way to the systematic use of ComiRNet for a comprehensive analysis of all the possible multiple interactions established by miRNAs of interest. Indeed, as shown in [1] and [2], ComiRNet can contribute to: ? the discovery of context-specific and unknown multiple miRNA functional co-operations; ? the functional characterization of genes with still poor annotation; ? the discovery of still unknown miRNA functional targets which could be worth to be experimentally validated. This possibility is due to its ability to associate objects that are apparently not related. Finally, ComiRNet also represents an easy-to-use and reliable tool for: ? biologists and clinicians, to interpret big volume of data such as those produced by NGS approaches, and ? bioinformaticians developing integrated bioinformatics tools, web services, workflows and analysis pipelines for the integrated analysis of genomics, transcriptomics and proteomics data. References [1] Pio G, Malerba D, D'Elia D. and Ceci M (2014) Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach, BMC Bioinformatics 15 (S-1): S4. doi:10.1186/1471-2105-15-S1-S4 [2] Pio G, Ceci M, D'Elia D, Loglisci C, Malerba D (2013) A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs. BMC Bioinformatics,14 (Suppl 7), S8. doi:10.1186/1471-2105-14-S7-S8 [3] Hsu SD, Lin FM,Wu WY, Liang C, HuangWC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu MC, Huang CY, Tsou AP, Huang HD (2011): miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucl Acids Res 39:163-169. [4] Ashburner M, et al: Gene Ontology: tool for the unification of biology (2000) The Gene Ontology Consortium. Nature Genetics 25:25-29. (literal)
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