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. 2016 Jan 4;44(D1):D239-47.
doi: 10.1093/nar/gkv1258. Epub 2015 Nov 20.

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

Affiliations

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

Chih-Hung Chou et al. Nucleic Acids Res. .

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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Figures

Figure 1.
Figure 1.
Snapshot of major improvements provided by miRTarBase interface. (A) New miRTarBase home page and three new major interfaces, including miRNA disease word cloud (B), miRNA-target site viewer based on CLIP-seq data (C) and miRNA and gene expression profiles obtained using TCGA data set (D).
Figure 2.
Figure 2.
Natural language processing (NLP) techniques for finding MTI articles. (i) Articles collected from PubMed; (ii) preprocessing articles in the following steps; split sentences, tokenization, stemming and stopword removal; (iii) Named Entity Recognition (NER) based on principle-based approach (PBA), Conditional Random Field (CRF) and dictionary-based approach; (iv) following construction and evaluation of model, MTI extraction protocol was developed using PBA; (v) all curated articles were manually evaluated by biological domain experts.

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