Literature DB >> 32181856

sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis.

Kuan-Chieh Tseng1, Yi-Fan Chiang-Hsieh2, Hsuan Pai3, Nai-Yun Wu2, Han-Qin Zheng2, Chi-Nga Chow4,5, Tzong-Yi Lee5, Song-Bin Chang1, Na-Sheng Lin3, Wen-Chi Chang1,2,4.   

Abstract

Small RNA (sRNA), such as microRNA (miRNA) and short interfering RNA, are well-known to control gene expression based on degradation of target mRNA in plants. A considerable amount of research has applied next-generation sequencing (NGS) to reveal the regulatory pathways of plant sRNAs. Consequently, numerous bioinformatics tools have been developed for the purpose of analyzing sRNA NGS data. However, most methods focus on the study of sRNA expression profiles or novel miRNAs predictions. The analysis of sRNA target genes is usually not integrated into their pipelines. As a result, there is still no means available for identifying the interaction mechanisms between host and virus or the synergistic effects between two viruses. For the present study, a comprehensive system, called the Small RNA Illustration System (sRIS), has been developed. This system contains two main components. The first is for sRNA overview analysis and can be used not only to identify miRNA but also to investigate virus-derived small interfering RNA. The second component is for sRNA target prediction, and it employs both bioinformatics calculations and degradome sequencing data to enhance the accuracy of target prediction. In addition, this system has been designed so that figures and tables for the outputs of each analysis can be easily retrieved and accessed, making it easier for users to quickly identify and quantify their results. sRIS is available at http://sris.itps.ncku.edu.tw/.
© The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  Degradome; Gene regulation; MicroRNA; Small RNA sequencing

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Year:  2020        PMID: 32181856     DOI: 10.1093/pcp/pcaa034

Source DB:  PubMed          Journal:  Plant Cell Physiol        ISSN: 0032-0781            Impact factor:   4.927


  3 in total

Review 1.  Machine learning: its challenges and opportunities in plant system biology.

Authors:  Mohsen Hesami; Milad Alizadeh; Andrew Maxwell Phineas Jones; Davoud Torkamaneh
Journal:  Appl Microbiol Biotechnol       Date:  2022-05-16       Impact factor: 4.813

2.  LINC00152 knockdown suppresses tumorigenesis in non-small cell lung cancer via sponging miR-16-5p.

Authors:  Hang Hu; Chen Chen; Fugang Chen; Naitong Sun
Journal:  J Thorac Dis       Date:  2022-03       Impact factor: 2.895

Review 3.  The Multiverse of Plant Small RNAs: How Can We Explore It?

Authors:  Zdravka Ivanova; Georgi Minkov; Andreas Gisel; Galina Yahubyan; Ivan Minkov; Valentina Toneva; Vesselin Baev
Journal:  Int J Mol Sci       Date:  2022-04-02       Impact factor: 5.923

  3 in total

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