MirDIP 5.2: tissue context annotation and novel microRNA curation

Author:

Hauschild Anne-Christin1,Pastrello Chiara2ORCID,Ekaputeri Gitta Kirana Anindya1,Bethune-Waddell Dylan2,Abovsky Mark2,Ahmed Zuhaib2,Kotlyar Max2,Lu Richard2,Jurisica Igor234

Affiliation:

1. Department of Medical Informatics, University Medical Center Göttingen, Georg-August University , Göttingen, Lower Saxony  37075 , Germany

2. Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network , Toronto  M5T 0S8, Canada

3. Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto , Toronto , ON, Canada

4. Institute of Neuroimmunology, Slovak Academy of Sciences , Bratislava , Slovakia

Abstract

AbstractMirDIP is a well-established database that aggregates microRNA-gene human interactions from multiple databases to increase coverage, reduce bias, and improve usability by providing an integrated score proportional to the probability of the interaction occurring. In version 5.2, we removed eight outdated resources, added a new resource (miRNATIP), and ran five prediction algorithms for miRBase and mirGeneDB. In total, mirDIP 5.2 includes 46 364 047 predictions for 27 936 genes and 2734 microRNAs, making it the first database to provide interactions using data from mirGeneDB. Moreover, we curated and integrated 32 497 novel microRNAs from 14 publications to accelerate the use of these novel data. In this release, we also extend the content and functionality of mirDIP by associating contexts with microRNAs, genes, and microRNA–gene interactions. We collected and processed microRNA and gene expression data from 20 resources and acquired information on 330 tissue and disease contexts for 2657 microRNAs, 27 576 genes and 123 651 910 gene–microRNA–tissue interactions. Finally, we improved the usability of mirDIP by enabling the user to search the database using precursor IDs, and we integrated miRAnno, a network-based tool for identifying pathways linked to specific microRNAs. We also provide a mirDIP API to facilitate access to its integrated predictions. Updated mirDIP is available at https://ophid.utoronto.ca/mirDIP.

Funder

Ontario Research Fund

Natural Sciences Research Council

Canada Foundation for Innovation

Schroeder Arthritis Institute

Buchan Foundation

Toronto General and Western Hospital Foundation

Publisher

Oxford University Press (OUP)

Subject

Genetics

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