Literature DB >> 25863133

PlantMirnaT: miRNA and mRNA integrated analysis fully utilizing characteristics of plant sequencing data.

S Rhee1, H Chae2, S Kim3.   

Abstract

miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used. Fortunately, due to the low sequencing cost, small RNA and RNA sequencing are routinely processed and we may be able to infer regulation relationships between miRNAs and mRNAs more accurately by using sequencing data. However, no method is developed specifically for sequencing data. Thus we developed PlantMirnaT, a new miRNA-mRNA integrated analysis system. To fully leverage the power of sequencing data, three major features are developed and implemented in PlantMirnaT. First, we implemented a plant-specific short read mapping tool based on recent discoveries on miRNA target relationship in plant. Second, we designed and implemented an algorithm considering miRNA targets in the full intragenic region, not just 3' UTR. Lastly but most importantly, our algorithm is designed to consider quantity of miRNA expression and its distribution on target mRNAs. The new algorithm was used to characterize rice under drought condition using our proprietary data. Our algorithm successfully discovered that two miRNAs, miRNA1425-5p, miRNA 398b, that are involved in suppression of glucose pathway in a naturally drought resistant rice, Vandana. The system can be downloaded at https://sites.google.com/site/biohealthinformaticslab/resources.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Optimization; RNA-seq; Split-ratio; miRNA

Mesh:

Substances:

Year:  2015        PMID: 25863133     DOI: 10.1016/j.ymeth.2015.04.003

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  6 in total

1.  The miRNA biogenesis in marine bivalves.

Authors:  Umberto Rosani; Alberto Pallavicini; Paola Venier
Journal:  PeerJ       Date:  2016-03-07       Impact factor: 2.984

2.  Literature-based condition-specific miRNA-mRNA target prediction.

Authors:  Minsik Oh; Sungmin Rhee; Ji Hwan Moon; Heejoon Chae; Sunwon Lee; Jaewoo Kang; Sun Kim
Journal:  PLoS One       Date:  2017-03-31       Impact factor: 3.240

3.  ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features.

Authors:  Prabina Kumar Meher; Shbana Begam; Tanmaya Kumar Sahu; Ajit Gupta; Anuj Kumar; Upendra Kumar; Atmakuri Ramakrishna Rao; Krishna Pal Singh; Om Parkash Dhankher
Journal:  Int J Mol Sci       Date:  2022-01-30       Impact factor: 5.923

Review 4.  MicroRNAs As Potential Targets for Abiotic Stress Tolerance in Plants.

Authors:  Varsha Shriram; Vinay Kumar; Rachayya M Devarumath; Tushar S Khare; Shabir H Wani
Journal:  Front Plant Sci       Date:  2016-06-14       Impact factor: 5.753

Review 5.  Non-coding RNAs and Their Roles in Stress Response in Plants.

Authors:  Jingjing Wang; Xianwen Meng; Oxana B Dobrovolskaya; Yuriy L Orlov; Ming Chen
Journal:  Genomics Proteomics Bioinformatics       Date:  2017-10-07       Impact factor: 7.691

Review 6.  Computational tools for plant small RNA detection and categorization.

Authors:  Lionel Morgado; Frank Johannes
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

  6 in total

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