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C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. [EN] Non-coding RNA transcripts such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are important genetic regulators. However, the functions of many of these transcripts are still not clearly understood. Recently, it has become apparent that there is significant crosstalk between miRNAs and lncRNAs and that this creates competition for binding between the miRNA, a lncRNA and other regulatory targets. Indeed, various competitive endogenous RNAs (ceRNAs) have already been identified where a lncRNA acts by sequestering miRNAs. This implies the down-regulation in the interaction of the miRNAs with their mRNA targets, what has been called a sponge effect. Multiple approaches exist for the prediction of miRNA targets in mRNAs. However, few methods exist for the prediction of miRNA response elements (MREs) in lncRNAs acting as ceRNAs (sponges). Here, we present spongeScan (http://spongescan.rc.ufl.edu), a graphical web tool to compute and visualize putative MREs in lncRNAs, along with different measures to assess their likely behavior as ceRNAs. FP7 STATegra project [agreement number 36000]; MINECO, co-funded with European Regional Development Funds (ERDF) [BIO2012-40244]; European Molecular Biology Laboratory and the European Union and ERC Seventh Framework Programme (FP7/2007-2013) [ERC-2012-StG-310325]. Funding for open access charge: University of Florida funds. Furió-Tarí, P.; Tarazona Campos, S.; Gabaldón, T.; Enright, AJ.; Conesa, A. (2016). spongeScan: A web for detecting microRNA binding elements in lncRNA sequences. Nucleic Acids Research. 44((W1)):176-180. https://doi.org/10.1093/nar/gkw443 Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005). Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell, 120(1), 15-20. doi:10.1016/j.cell.2004.12.035 Agarwal V. Bell G.W. Nam J.W. Bartel D.P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015;4. doi:10.7554/eLife.05005. Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., … Rajewsky, N. (2005). Combinatorial microRNA target predictions. Nature Genetics, 37(5), 495-500. doi:10.1038/ng1536 Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., … Rajewsky, N. (2013). Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 495(7441), 333-338. doi:10.1038/nature11928 Paraskevopoulou, M. D., Vlachos, I. S., Karagkouni, D., Georgakilas, G., Kanellos, I., Vergoulis, T., … Hatzigeorgiou, A. G. (2015). DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Research, 44(D1), D231-D238. doi:10.1093/nar/gkv1270 Li, J.-H., Liu, S., Zhou, H., Qu, L.-H., & Yang, J.-H. (2013). starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Research, 42(D1), D92-D97. doi:10.1093/nar/gkt1248 Cunningham, F., Amode, M. R., Barrell, D., Beal, K., Billis, K., Brent, S., … Flicek, P. (2014). Ensembl 2015. Nucleic Acids Research, 43(D1), D662-D669. doi:10.1093/nar/gku1010 Kozomara, A., & Griffiths-Jones, S. (2013). miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 42(D1), D68-D73. doi:10.1093/nar/gkt1181 Medina, I., Salavert, F., Sanchez, R., de Maria, A., Alonso, R., Escobar, P., … Dopazo, J. (2013). Genome Maps, a new generation genome browser. Nucleic Acids Research, 41(W1), W41-W46. doi:10.1093/nar/gkt530 ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489:57-74. Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., … Mortazavi, A. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology, 17(1). doi:10.1186/s13059-016-0881-8 Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4), R36. doi:10.1186/gb-2013-14-4-r36 Anders, S., Pyl, P. T., & Huber, W. (2014). HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics, 31(2), 166-169. doi:10.1093/bioinformatics/btu638

spongeScan: A web for detecting microRNA binding elements in lncRNA sequences

Abstract