title: SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification.
abstract: BACKGROUND
  snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and γ; and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms.
  
  
  RESULTS
  Results of the training and test phases of boxes H/ACA and C/D snoRNAs, in both versions of snoReport, are discussed. Validation on real data was performed to evaluate the predictions of snoReport 2.0. Our program was applied to a set of previously annotated sequences, some of them experimentally confirmed, of humans, nematodes, drosophilids, platypus, chickens and leishmania. We significantly improved the predictions for vertebrates, since the training phase used information of these organisms, but H/ACA box snoRNAs identification was improved for the other ones.
  
  
  CONCLUSIONS
  We presented snoReport 2.0, to predict H/ACA box and C/D box snoRNAs, an efficient method to find true positives and avoid false positives in vertebrate organisms. H/ACA box snoRNA classifier showed an F-score of 93 % (an improvement of 10 % regarding the previous version), while C/D box snoRNA classifier, an F-Score of 94 % (improvement of 14 %). Besides, both classifiers exhibited performance measures above 90 %. These results show that snoReport 2.0 avoid false positives and false negatives, allowing to predict snoRNAs with high quality. In the validation phase, snoReport 2.0 predicted 67.43 % of vertebrate organisms for both classes. For Nematodes and Drosophilids, 69 % and 76.67 %, for H/ACA box snoRNAs were predicted, respectively, showing that snoReport 2.0 is good to identify snoRNAs in vertebrates and also H/ACA box snoRNAs in invertebrates organisms.
authors.affiliation: Department of Computer Science, University of Brasilia, Brasília, BR-70910-900, Brazil. joaovicers@gmail.com.
authors.forename: João Victor
authors.initials: JV
authors.lastname: de Araujo Oliveira
authors.affiliation: Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany.
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Costa
authors.affiliation: Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg, 79110, Germany.
authors.forename: Rolf
authors.initials: R
authors.lastname: Backofen
authors.affiliation: Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Haertelstraße 16-18, Leipzig, D-04107, Germany.|German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany.|Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, Vienna, A-1090, Austria.|Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg, DK-1870, Denmark.|Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany.|RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1, Leipzig, D-04103, Germany.|Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501, USA.|Young Investigators Group Bioinformatics & Transcriptomics, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, Leipzig, D-04318, Germany.
authors.forename: Peter Florian
authors.initials: PF
authors.lastname: Stadler
authors.affiliation: Department of Computer Science, University of Brasilia, Brasília, BR-70910-900, Brazil.
authors.forename: Maria Emília
authors.initials: ME
authors.lastname: Machado Telles Walter
authors.affiliation: Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Haertelstraße 16-18, Leipzig, D-04107, Germany.
authors.forename: Jana
authors.initials: J
authors.lastname: Hertel
mesh_terms: D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D056890:Eukaryota / Q000737:chemistry / Q000235:genetics*; D006801:Humans; D008969:Molecular Sequence Data; D020537:RNA, Small Nucleolar / Q000737:chemistry* / Q000235:genetics; D060388:Support Vector Machine; D014714:Vertebrates / Q000235:genetics
chemical_list: D020537:RNA, Small Nucleolar
keywords: C/D box snoRNA; H/ACA box snoRNA; Machine learning; Non-coding RNA; Support Vector Machine (SVM); snoRNA