miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database

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Abstract

MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase ( http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.

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Predicting effective microRNA target sites in mammalian mRNAs

Vikram Agarwal, George Bell, Jin-Wu Nam … (2015)

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. DOI: http://dx.doi.org/10.7554/eLife.05005.001

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Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

Lee P. Lim, Nelson C. Lau, Philip Garrett-Engele … (2005)

MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.

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

Nathan C. Wong, Xiaowei Wang (2014)

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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Author and article information

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (hwp): nar

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date (Print): 04 January 2016

Publication date (Electronic): 20 November 2015

Publication date PMC-release: 20 November 2015

Volume: 44

Issue: Database issue , Database issue

Pages: D239-D247

Affiliations

[1 ]Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan

[2 ]Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 106, Taiwan

[3 ]Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan

[4 ]Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, 300, Taiwan

[5 ]Clinical Research Center, Chung Shan Medical University Hospital, Taichung, 402, Taiwan

[6 ]Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 350, Taiwan

[7 ]Interdisciplinary Program of Life Science, National Tsing Hua University, Hsinchu, 300, Taiwan

[8 ]Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan

[9 ]Degree Program of Applied Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan

[10 ]Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Cathay General Hospital, Taipei, 106, Taiwan

[11 ]Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan

[12 ]Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu, 300, Taiwan

[13 ]Mackay Medicine, Nursing and Management College, Taipei, 112, Taiwan

[14 ]Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan

[15 ]Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan

[16 ]Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, 807, Taiwan

Author notes

[* ]To whom correspondence should be addressed. Tel: +886 3 5712121 (Ext. 56952); Fax: +886 3 5729288; Email: bryan@ 123456mail.nctu.edu.tw

Correspondence may also be addressed to Wen-Lian Hsu. Tel: +886 2 27883799 (Ext. 2202); Fax: +886 2 27824814; Email: hsu@ 123456iis.sinica.edu.tw

[† ]These authors contributed equally to this work as first authors.

Article

DOI: 10.1093/nar/gkv1258

PMC ID: 4702890

PubMed ID: 26590260

SO-VID: 62e33bc7-0547-4e9d-87ef-c228ad4d296c

License:

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

Date accepted : 30 October 2015

Date revision received : 29 October 2015

Date received : 15 September 2015

Page count

Pages: 9

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cover-date 04 January 2016

ScienceOpen disciplines: Genetics

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ScienceOpen disciplines: Genetics

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miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database

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RNA drug delivery

Most cited references 46

Predicting effective microRNA target sites in mammalian mRNAs

Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

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

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Cited by 498

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