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In metazoan, regulatory molecules tightly control gene expression. Among them, microRNAs (miRNAs) are key regulators of several important features, like cell proliferation, differentiation, and homeostasis. During miRNA biogenesis, the canonical strand that loads onto RISC can be switched, in a process called “arm switching.” Due to the miRNA-to-target pairing peculiarities, switching events can lead to changes on the gene-targeted repertoire, promoting the modulation of a distinct set of biological routes. To understand how these events affect cell regulation, we conducted an extensive and detailed in silico analysis of RNA-seq datasets from several tissues and key developmental stages of zebrafish. We identified interesting patterns of miRNA arm switching occurrence, mainly associated with the control of protein coding genes during embryonic development. Additionally, our data show that miRNA isoforms (isomiRs) play an important role in differential arm usage. Our findings provide new insights on how such events emerge and coordinate gene expression regulation, opening perspectives for novel investigations in the area.

MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.

MicroRNAs (miRNAs) are small noncoding RNAs that are recognized as posttranscriptional regulators of gene expression. These molecules have been shown to play important roles in several cellular processes. MiRNAs act on their target by guiding the RISC complex and binding to the mRNA molecule. Thus, it is recognized that the function of a miRNA is determined by the function of its target (s). By using high-throughput methodologies, novel miRNAs are being identified, but their functions remain uncharted. Target validation is crucial to properly understand the specific role of a miRNA in a cellular pathway. However, molecular techniques for experimental validation of miRNA-target interaction are expensive, time-consuming, laborious, and can be not accurate in inferring true interactions. Thus, accurate miRNA target predictions are helpful to understand the functions of miRNAs. There are several algorithms proposed for target prediction and databases containing miRNA-target information. However, these available computational tools for prediction still generate a large number of false positives and fail to detect a considerable number of true targets, which indicates the necessity of highly confident approaches to identify bona fide miRNA-target interactions. This chapter focuses on tools and strategies used for miRNA target prediction, by providing practical insights and outlooks.

Additional file 7 Supplementary data in pdf format.

Additional file 7 Supplementary data in pdf format.

Additional file 5 Supplementary data in xls format.

Additional file 3 Supplementary data in pdf format.

Additional file 5 Supplementary data in xls format.

Summary: Network-level visualization of functional data is a key aspect of both analysis and understanding of biological systems. In a continuing effort to create clear and integrated visualizations that facilitate the gathering of novel biological insights despite the overwhelming complexity of data, we present here the GrAph LANdscape VisualizaTion (GALANT), a Cytoscape plugin that builds functional landscapes onto biological networks. By using GALANT, it is possible to project any type of numerical data onto a network to create a smoothed data map resembling the network layout. As a Cytoscape plugin, GALANT is further improved by the functionalities of Cytoscape, the popular bioinformatics package for biological network visualization and data integration. Availability: http://www.lbbc.ibb.unesp.br/galant. Contact: esther@ibb.unesp.br Supplementary Information: Supplementary data are available at Bioinformatics online.

Made available in DSpace on 2014-12-03T13:10:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-10-25Bitstream added on 2014-12-03T13:22:57Z : No. of bitstreams: 1 WOS000326155400041.pdf: 1362323 bytes, checksum: 15a9fd413757e700b9377edcfce7622c (MD5) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype. Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo, Brazil Univ Estadual Paulista, Dept Phys & Biophys, Botucatu Biosci Inst, Sao Paulo, Brazil FAPESP: 10/20684-3 FAPESP: 12/13450-1 FAPESP: 12/00741-8 FAPESP: 13/02018-4