29
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures

      research-article
      1 , 2 , 3 , , 1
      BMC Bioinformatics
      BioMed Central
      RNA-protein interaction, Support vector machine

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico.

          Results

          We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59.

          Conclusions

          Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: found
          • Article: not found

          Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase.

          L Gold, C Tuerk (1990)
          High-affinity nucleic acid ligands for a protein were isolated by a procedure that depends on alternate cycles of ligand selection from pools of variant sequences and amplification of the bound species. Multiple rounds exponentially enrich the population for the highest affinity species that can be clonally isolated and characterized. In particular one eight-base region of an RNA that interacts with the T4 DNA polymerase was chosen and randomized. Two different sequences were selected by this procedure from the calculated pool of 65,536 species. One is the wild-type sequence found in the bacteriophage mRNA; one is varied from wild type at four positions. The binding constants of these two RNA's to T4 DNA polymerase are equivalent. These protocols with minimal modification can yield high-affinity ligands for any protein that binds nucleic acids as part of its function; high-affinity ligands could conceivably be developed for any target molecule.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The UCSC Genome Browser database: update 2011

            The University of California, Santa Cruz Genome Browser (http://genome.ucsc.edu) offers online access to a database of genomic sequence and annotation data for a wide variety of organisms. The Browser also has many tools for visualizing, comparing and analyzing both publicly available and user-generated genomic data sets, aligning sequences and uploading user data. Among the features released this year are a gene search tool and annotation track drag-reorder functionality as well as support for BAM and BigWig/BigBed file formats. New display enhancements include overlay of multiple wiggle tracks through use of transparent coloring, options for displaying transformed wiggle data, a ‘mean+whiskers’ windowing function for display of wiggle data at high zoom levels, and more color schemes for microarray data. New data highlights include seven new genome assemblies, a Neandertal genome data portal, phenotype and disease association data, a human RNA editing track, and a zebrafish Conservation track. We also describe updates to existing tracks.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Fast folding and comparison of RNA secondary structures

                Bookmark

                Author and article information

                Contributors
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2014
                29 April 2014
                : 15
                : 123
                Affiliations
                [1 ]Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 5, Trento, Italy
                [2 ]Current address: Gene Function and Evolution, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, Barcelona, Spain
                [3 ]Universitat Pompeu Fabra (UPF), Barcelona, Spain
                Article
                1471-2105-15-123
                10.1186/1471-2105-15-123
                4098778
                24780077
                a0232f93-79c3-4020-8555-8d6268ffcc9e
                Copyright © 2014 Livi and Blanzieri; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 October 2013
                : 16 April 2014
                Categories
                Research Article

                Bioinformatics & Computational biology
                rna-protein interaction,support vector machine
                Bioinformatics & Computational biology
                rna-protein interaction, support vector machine

                Comments

                Comment on this article