Investigation of the Dynamic Regulation Mechanisms of Small Non-Coding RNA in Response to Drought Stress in Qingke Barley

2019 ◽  
Author(s):  
Sang Zha ◽  
Thondup Gyalpo ◽  
Xingquan Zeng ◽  
Hongjun Yuan ◽  
Mingzhai Yu ◽  
...  
Keyword(s):  
Drought Stress ◽  
Rna Sequencing ◽  
Drought Resistance ◽  
Small Rna ◽  
Target Genes ◽  
Great Influence ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Characteristic Analysis ◽  
Non Coding Rna

Abstract Background Drought is a common abiotic stressor that exerts a great influence on grain security worldwide. The mechanisms underlying the small non-coding RNA regulation of plant drought responses remain unclear.Results In this study, drought-resistant (Xila-16, Xila) and drought-sensitive (DingqingHYG, Diqing) ecotypes were selected. A systematic analysis was performed on the microRNAs and tRNA-derived fragments in small RNA sequencing data. By predicting the target genes of differentially expressed miRNAs and analysing their pathway, this study identified HVUL2H01030.2, HVUL2H06276.2, HVUL2H00175.2 and other important target genes. The analysis also identified base excision repair and other adversity stress-related pathways. tRNA-derived fragments (tRFs), as a novel type of small noncoding RNA, exist widely in organisms, but no study has shown that tRFs play a role in the drought resistance of qingke barley. Based on systematic identification and characteristic analysis of tRFs in small RNA sequencing data, this study found that qingke barley had a tendency to produce more tRFs under drought stress. These tRFs were widely expressed, showed specific tRNA cleavage modes and had conservative intertreatment cleavage and other characteristics.Conclusions Our findings lay the foundation for further investigation of the action mechanism of such novel small noncoding RNAs in the drought resistance of qingke barley.

scholarly journals miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale

2021 ◽  
Author(s):  
Tobias Fehlmann ◽  
Fabian Kern ◽  
Omar Laham ◽  
Christina Backes ◽  
Jeffrey Solomon ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Small Rna Sequencing ◽  
Analysis Tool ◽  
Sequencing Data ◽  
Effect Analysis ◽  
Non Coding Rna ◽  
Wide Range

Abstract Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.

scholarly journals Characterizing the cancer-associated microbiome with small RNA sequencing data

2020 ◽  
Vol 522 (3) ◽  
pp. 776-782
Author(s):  
Wei-Hao Lee ◽  
Kai-Pu Chen ◽  
Kai Wang ◽  
Hsuan-Cheng Huang ◽  
Hsueh-Fen Juan
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data

scholarly journals miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data

2016 ◽  
Vol 13 (5) ◽  
Author(s):  
Matthew Kanke ◽  
Jeanette Baran-Gale ◽  
Jonathan Villanueva ◽  
Praveen Sethupathy
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Cell Types ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Additional Information ◽  
Disease States ◽  
Non Coding Rnas ◽  
The Rich

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.

scholarly journals Evidence for human microRNA-offset RNAs in small RNA sequencing data

2009 ◽  
Vol 25 (18) ◽  
pp. 2298-2301 ◽  
Author(s):  
D. Langenberger ◽  
C. Bermudez-Santana ◽  
J. Hertel ◽  
S. Hoffmann ◽  
P. Khaitovich ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data

scholarly journals In silico genome wide mining of conserved and novel miRNAs in the brain and pineal gland of Danio rerio using small RNA sequencing data

Genomics Data ◽  
2016 ◽  
Vol 7 ◽  
pp. 46-53 ◽  
Author(s):  
Suyash Agarwal ◽  
Naresh Sahebrao Nagpure ◽  
Prachi Srivastava ◽  
Basdeo Kushwaha ◽  
Ravindra Kumar ◽  
...  
Keyword(s):  
Pineal Gland ◽  
Rna Sequencing ◽  
Danio Rerio ◽  
Small Rna ◽  
In Silico ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Genome Wide ◽  
The Brain

Traces of post-transcriptional RNA modifications in deep sequencing data

2011 ◽  
Vol 392 (4) ◽  
Author(s):  
Sven Findeiß ◽  
David Langenberger ◽  
Peter F. Stadler ◽  
Steve Hoffmann
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Data Sets ◽  
Sequencing Data ◽  
Rna Modifications ◽  
Deep Sequencing Data ◽  
Complex Processes ◽  
Mature Micrornas ◽  
Rna Maturation

Abstract Many aspects of the RNA maturation leave traces in RNA sequencing data in the form of deviations from the reference genomic DNA. This includes, in particular, genomically non-encoded nucleotides and chemical modifications. The latter leave their signatures in the form of mismatches and conspicuous patterns of sequencing reads. Modified mapping procedures focusing on particular types of deviations can help to unravel post-transcriptional modification, maturation and degradation processes. Here, we focus on small RNA sequencing data that is produced in large quantities aimed at the analysis of microRNA expression. Starting from the recovery of many well known modified sites in tRNAs, we provide evidence that modified nucleotides are a pervasive phenomenon in these data sets. Regarding non-encoded nucleotides we concentrate on CCA tails, which surprisingly can be found in a diverse collection of transcripts including sub-populations of mature microRNAs. Although small RNA sequencing libraries alone are insufficient to obtain a complete picture, they can inform on many aspects of the complex processes of RNA maturation.

scholarly journals Integrated transcriptome and small RNA sequencing analyses reveal a drought stress response network in Sophora tonkinensis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying Liang ◽  
Kunhua Wei ◽  
Fan Wei ◽  
Shuangshuang Qin ◽  
Chuanhua Deng ◽  
...  
Keyword(s):  
Drought Stress ◽  
Rna Sequencing ◽  
Small Rna ◽  
Stress Responses ◽  
Molecular Mechanisms ◽  
Differentially Expressed ◽  
Small Rna Sequencing ◽  
Severe Drought ◽  
A Genome ◽  
Sophora Tonkinensis

Abstract Background Sophora tonkinensis Gagnep is a traditional Chinese medical plant that is mainly cultivated in southern China. Drought stress is one of the major abiotic stresses that negatively impacts S. tonkinensis growth. However, the molecular mechanisms governing the responses to drought stress in S. tonkinensis at the transcriptional and posttranscriptional levels are not well understood. Results To identify genes and miRNAs involved in drought stress responses in S. tonkinensis, both mRNA and small RNA sequencing was performed in root samples under control, mild drought, and severe drought conditions. mRNA sequencing revealed 66,476 unigenes, and the differentially expressed unigenes (DEGs) were associated with several key pathways, including phenylpropanoid biosynthesis, sugar metabolism, and quinolizidine alkaloid biosynthesis pathways. A total of 10 and 30 transcription factors (TFs) were identified among the DEGs under mild and severe drought stress, respectively. Moreover, small RNA sequencing revealed a total of 368 miRNAs, including 255 known miRNAs and 113 novel miRNAs. The differentially expressed miRNAs and their target genes were involved in the regulation of plant hormone signal transduction, the spliceosome, and ribosomes. Analysis of the regulatory network involved in the response to drought stress revealed 37 differentially expressed miRNA-mRNA pairs. Conclusion This is the first study to simultaneously profile the expression patterns of mRNAs and miRNAs on a genome-wide scale to elucidate the molecular mechanisms of the drought stress responses of S. tonkinensis. Our results suggest that S. tonkinensis implements diverse mechanisms to modulate its responses to drought stress.

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