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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/29126174
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. 2018 Jan 4;46(D1):D296-D302.
doi: 10.1093/nar/gkx1067.

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

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miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

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

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

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Figures

Figure 1.
Figure 1.
Overall system flow of miRTarBase. miRTarBase aims to be a more comprehensive database of experimentally verified miRNA-targets interaction. This database was released in 2011. The current database contains more than 420,000 MTIs.
Figure 2.
Figure 2.
Top 10 Human miRNAs, genes, and diseases with strong evidence of MTIs in miRTarBase.
Figure 3.
Figure 3.
Natural language processing (NLP) techniques for identifying MTI articles. (1) Collecting Articles from public literature database, PubMed; (2) detecting sentence boundary of each abstract; (3) preprocessing articles in the following steps; split sentences, tokenization, stemming and stopword removal; (4) Named Entity Recognition (NER) based on statistical principle-based approach (SPBA), Conditional Random Fields (CRFs) and dictionary-based approach; (5) all sentences will be labeled as labeled sentences for principle matching; (6) following construction and evaluation of model, an inference engine for MTI extraction was developed in SPBA; (7) the inference engine is constructed for assist domain exports to manually check the generated positive principles (the * element is not required, e.g., experiment); (8) all curated articles were manually evaluated by biological domain experts.

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