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      Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis

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          Abstract

          MicroRNAs (miRNAs) are small and non-coding RNA molecules that inhibit gene expression posttranscriptionally. They play important roles in several biological processes, and in recent years there has been an interest in studying how they are related to the pathogenesis of diseases. Although there are already some databases that contain information for miRNAs and their relation with illnesses, their curation represents a significant challenge due to the amount of information that is being generated every day. In particular, respiratory diseases are poorly documented in databases, despite the fact that they are of increasing concern regarding morbidity, mortality and economic impacts. In this work, we present the results that we obtained in the BioCreative Interactive Track (IAT), using a semiautomatic approach for improving biocuration of miRNAs related to diseases. Our procedures will be useful to complement databases that contain this type of information. We adapted the OntoGene text mining pipeline and the ODIN curation system in a full-text corpus of scientific publications concerning one specific respiratory disease: idiopathic pulmonary fibrosis, the most common and aggressive of the idiopathic interstitial cases of pneumonia. We curated 823 miRNA text snippets and found a total of 246 miRNAs related to this disease based on our semiautomatic approach with the system OntoGene/ODIN. The biocuration throughput improved by a factor of 12 compared with traditional manual biocuration. A significant advantage of our semiautomatic pipeline is that it can be applied to obtain the miRNAs of all the respiratory diseases and offers the possibility to be used for other illnesses .

          Database URL: http://odin.ccg.unam.mx/ODIN/bc2015-miRNA/

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          Most cited references22

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          miRDB: an online resource for microRNA target prediction and functional annotations

          MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes regulated by miRNAs. To this end, we have developed an online resource, miRDB (http://mirdb.org), for miRNA target prediction and functional annotations. Here, we describe recently updated features of miRDB, including 2.1 million predicted gene targets regulated by 6709 miRNAs. In addition to presenting precompiled prediction data, a new feature is the web server interface that allows submission of user-provided sequences for miRNA target prediction. In this way, users have the flexibility to study any custom miRNAs or target genes of interest. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles or may even have been falsely identified as miRNAs from high-throughput studies. To address this issue, we have performed combined computational analyses and literature mining, and identified 568 and 452 functional miRNAs in humans and mice, respectively. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB.
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            miRecords: an integrated resource for microRNA–target interactions

            MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.
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              DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions

              microRNAs (miRNAs) are short non-coding RNA species, which act as potent gene expression regulators. Accurate identification of miRNA targets is crucial to understanding their function. Currently, hundreds of thousands of miRNA:gene interactions have been experimentally identified. However, this wealth of information is fragmented and hidden in thousands of manuscripts and raw next-generation sequencing data sets. DIANA-TarBase was initially released in 2006 and it was the first database aiming to catalog published experimentally validated miRNA:gene interactions. DIANA-TarBase v7.0 (http://www.microrna.gr/tarbase) aims to provide for the first time hundreds of thousands of high-quality manually curated experimentally validated miRNA:gene interactions, enhanced with detailed meta-data. DIANA-TarBase v7.0 enables users to easily identify positive or negative experimental results, the utilized experimental methodology, experimental conditions including cell/tissue type and treatment. The new interface provides also advanced information ranging from the binding site location, as identified experimentally as well as in silico, to the primer sequences used for cloning experiments. More than half a million miRNA:gene interactions have been curated from published experiments on 356 different cell types from 24 species, corresponding to 9- to 250-fold more entries than any other relevant database. DIANA-TarBase v7.0 is freely available.
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                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                database
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2017
                22 April 2017
                22 April 2017
                : 2017
                : bax030
                Affiliations
                [1 ]Facultad de Ciencias, Departamento Biología Celular, Universidad Nacional Autónoma de México, Ciudad Universitaria, Circuito Exterior s/n, Coyoacán, CP 04510, Ciudad de México, CDMX, México
                [2 ]CONACYT-INER Ismael Cosío Villegas, Departamento Investigación, Calzada de Tlalpan 4502 Sección XVI, Tlalpan, CP Ciudad de México, CDMX, México
                [3 ]Swiss Institute of Bioinformatics and Institute of Computational Linguistics, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland
                [4 ]Center for Genomics Sciences, Computational Genomics Program, Universidad Nacional Autónoma de México, Av. Universidad s/n, Chamilpa, CP 62210, Cuernavaca, Morelos, México
                [5 ]Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Dirección de Investigación Calzada de Tlalpan 4502 Sección XVI, Tlalpan, CP Ciudad de México, CDMX, México
                Author notes
                [* ]Corresponding author: Tel.: +52 5554871771; Fax: +52 5556654523; Email: yibalderas@ 123456conacyt.mx

                Citation details: Balderas-Martínez,Y.I., Rinaldi,F., Contreras,G. et al. Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis. Database (2017) Vol. 2017: article ID bax030; doi:10.1093/database/bax030

                Article
                bax030
                10.1093/database/bax030
                5467562
                28605770
                93f4db83-377c-4027-ac2a-ca6036578de1
                © The Author(s) 2017. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 August 2016
                : 22 February 2017
                : 25 March 2017
                Page count
                Pages: 9
                Funding
                Funded by: Swiss National Science Foundation
                Funded by: National Institute of Health
                Categories
                Original Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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