scholarly journals DPMIND: degradome-based plant miRNA–target interaction and network database

2017 ◽  
Vol 34 (9) ◽  
pp. 1618-1620 ◽  
Author(s):  
Yuhan Fei ◽  
Rui Wang ◽  
Haoyuan Li ◽  
Shu Liu ◽  
Hongsheng Zhang ◽  
...  
Keyword(s):  
Mirna Target ◽  
Target Interaction ◽  
Network Database ◽  
Plant Mirna

WPMIAS: Whole-degradome-based Plant MicroRNA–target Interaction Analysis Server

2019 ◽  
Vol 36 (6) ◽  
pp. 1937-1939 ◽  
Author(s):  
Yuhan Fei ◽  
Yiyang Mao ◽  
Chengji Shen ◽  
Rui Wang ◽  
Hongsheng Zhang ◽  
...  
Keyword(s):  
Mirna Target ◽  
Interaction Analysis ◽  
Supplementary Information ◽  
Future Research ◽  
Microrna Target ◽  
Web Based ◽  
Target Interaction ◽  
Target Analysis ◽  
Target Mrnas ◽  
Plant Mirna

Abstract Summary A critical aspect for exploring the biological function of a microRNA (miRNA) lies on exact detection and validation of its target mRNAs. However, no convenient and efficient web-based server is available for plant biologists to identify the experimentally verified target mRNAs of miRNAs. In this work, we built a comprehensive web-based platform for miRNA–target analysis, named as Whole-degradome-based Plant MiRNA–target Interaction Analysis Server (WPMIAS), for validation of predicted interactions of miRNAs and their target mRNAs (MTIs) by user-submitted data or all available pre-loaded degradome data. Besides, the server can construct degradome-based miRNA regulatory networks (MRNs) based on the validated MTIs to help study the functions and relations among miRNAs and target mRNAs. WPMIAS is also suitable for other small RNAs (sRNAs), such as 21-nt phased siRNAs and natural antisense siRNAs, which direct cleavage of target mRNAs. Currently, WPMIAS supports 68 plant species with 189 cDNA and 271 pre-loaded plant degradome datasets. The user can identify all validated MTIs by analyzing all degradome data at a time and understand when and where MTIs take place and their cleavage levels. With the data obtained from WPMIAS, the user can build a plant miRNA–target map, where it is convenient to find interesting research ideas on miRNAs. In summary, WPMIAS is able to support a comprehensive web-based plant miRNA–target analysis and expected to greatly promote future research on plant miRNAs. Availability and implementation It can be freely accessed at https://cbi.njau.edu.cn/WPMIAS/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MTide: an integrated tool for the identification of miRNA-target interaction in plants

2014 ◽  
Vol 31 (2) ◽  
pp. 290-291 ◽  
Author(s):  
Z. Zhang ◽  
L. Jiang ◽  
J. Wang ◽  
P. Gu ◽  
M. Chen
Keyword(s):  
Mirna Target ◽  
Target Interaction

Obtaining miRNA‐Target Interaction Information from miRWalk2.0

2016 ◽  
Vol 55 (1) ◽  
Author(s):  
Alisha Parveen ◽  
Norbert Gretz ◽  
Harsh Dweep
Keyword(s):  
Mirna Target ◽  
Target Interaction ◽  
Interaction Information

Asthma MicroRNA Regulome Development Using Validated miRNA-Target Interaction Visualization

2018 ◽  
Vol 22 (9) ◽  
pp. 607-615 ◽  
Author(s):  
Simon Fekonja ◽  
Peter Korošec ◽  
Matija Rijavec ◽  
Taja Jeseničnik ◽  
Tanja Kunej
Keyword(s):  
Mirna Target ◽  
Target Interaction

scholarly journals miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database

2019 ◽  
Author(s):  
Hsi-Yuan Huang ◽  
Yang-Chi-Dung Lin ◽  
Jing Li ◽  
Kai-Yao Huang ◽  
Sirjana Shrestha ◽  
...  
Keyword(s):  
Mirna Target ◽  
Target Genes ◽  
Biological Significance ◽  
Transcriptional Level ◽  
Biological Databases ◽  
Microrna Target ◽  
Mining System ◽  
Target Interaction ◽  
Interaction Database ◽  
Comprehensive Information

Abstract MicroRNAs (miRNAs) are small non-coding RNAs (typically consisting of 18–25 nucleotides) that negatively control expression of target genes at the post-transcriptional level. Owing to the biological significance of miRNAs, miRTarBase was developed to provide comprehensive information on experimentally validated miRNA–target interactions (MTIs). To date, the database has accumulated >13,404 validated MTIs from 11,021 articles from manual curations. In this update, a text-mining system was incorporated to enhance the recognition of MTI-related articles by adopting a scoring system. In addition, a variety of biological databases were integrated to provide information on the regulatory network of miRNAs and its expression in blood. Not only targets of miRNAs but also regulators of miRNAs are provided to users for investigating the up- and downstream regulations of miRNAs. Moreover, the number of MTIs with high-throughput experimental evidence increased remarkably (validated by CLIP-seq technology). In conclusion, these improvements promote the miRTarBase as one of the most comprehensively annotated and experimentally validated miRNA–target interaction databases. The updated version of miRTarBase is now available at http://miRTarBase.cuhk.edu.cn/.

scholarly journals A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 348 ◽  
Author(s):  
Prashant K Srivastava ◽  
Taraka Moturu ◽  
Priyanka Pandey ◽  
Ian T Baldwin ◽  
Shree P Pandey
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Prediction Tools ◽  
Genome Wide ◽  
Plant Mirna

scholarly journals TarDB: an online database for plant miRNA targets and miRNA-triggered phased siRNAs

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Liu ◽  
Xiaonan Liu ◽  
Siju Zhang ◽  
Shanshan Liang ◽  
Weijiang Luan ◽  
...  
Keyword(s):  
Stress Responses ◽  
Mirna Target ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Online Database ◽  
Small Interfering Rnas ◽  
High Confidence ◽  
Web Resource ◽  
Mirna Targets ◽  
Plant Mirna

Abstract Background In plants, microRNAs (miRNAs) are pivotal regulators of plant development and stress responses. Different computational tools and web servers have been developed for plant miRNA target prediction; however, in silico prediction normally contains false positive results. In addition, many plant miRNA target prediction servers lack information for miRNA-triggered phased small interfering RNAs (phasiRNAs). Creating a comprehensive and relatively high-confidence plant miRNA target database is much needed. Results Here, we report TarDB, an online database that collects three categories of relatively high-confidence plant miRNA targets: (i) cross-species conserved miRNA targets; (ii) degradome/PARE (Parallel Analysis of RNA Ends) sequencing supported miRNA targets; (iii) miRNA-triggered phasiRNA loci. TarDB provides a user-friendly interface that enables users to easily search, browse and retrieve miRNA targets and miRNA initiated phasiRNAs in a broad variety of plants. TarDB has a comprehensive collection of reliable plant miRNA targets containing previously unreported miRNA targets and miRNA-triggered phasiRNAs even in the well-studied model species. Most of these novel miRNA targets are relevant to lineage-specific or species-specific miRNAs. TarDB data is freely available at http://www.biosequencing.cn/TarDB. Conclusions In summary, TarDB serves as a useful web resource for exploring relatively high-confidence miRNA targets and miRNA-triggered phasiRNAs in plants.

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