miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions
By Chih-Hung Chou, S. Shrestha, Chi-Dung Yang, Nai-Wen Chang, Yu-Ling Lin, K. Liao, Wei-Chih Huang, Ting-Hsuan Sun, Siang-Jyun Tu, Wei-Hsiang Lee and 26 more. Nucleic Acids Res., 2017
1,585 citations recorded by the source corpus.
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/.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f8b7a3434f887ce4570b7e98c7f1b91c008042d4
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In Cancer Research
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- Sam Griffiths-Jones
- Yuan Zhou
- Juan Wang
- Zhan Tong
- Maria Birgaoanu
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In Molecular Biology
- Marinka Zitnik
- Kexin Huang
- Jure Leskovec
- Ana Kozomara
- Cao Xiao
- Qinghua Cui
- Jimeng Sun
- Sam Griffiths-Jones
Citations in the corpus
- miRBase: from microRNA sequences to function (2018, background): “The TarBase (17) and miRTarBase (18) databases curate and provide lists of microRNA targets that have some experimental support.”
- Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation (2018, background): “These data are in agreement with high rates of miRNA-bound AGO sampling of targets observed in vitro (85), i.e., miRISC is able to probe for MREs quickly, but does not form stable interactions on its own.”
- gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler (2020, methodology): “g:Profiler’s in-house database includes only reliable annotation data sources that are regularly updated such as Gene Ontology (GO) 3 , KEGG 4 , Reactome 5 , WikiPathways 17 , miRTarBase 18 , TRANSFAC 19 , Human Protein Atlas 20 , protein complexes from CORUM 21 and Human Phenotype Ontology 22 .”
- TransmiR v2.0: an updated transcription factor-microRNA regulation database (2018, positive): “Finally, experimentally validated miRNA-TF feedback regulations derived from miRNA target databases (TarBase (26) and miRTarBase (27)) were also included in TransmiR v2.”
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “MTI TDC.miRTarBase: miRTarBase is a large public database that containsMTIs that are validated experimentally after manually surveying literature related to functional studies of miRNAs (Chou et al. 2018).”