Extracellular microRNA 3’ end modification across diverse body fluids

ABSTRACTmicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene regulation. The presence of miRNAs in extracellular biofluids is increasingly recognized. However, most previous characterization of extracellular miRNAs focused on their overall expression levels. Alternative sequence isoforms and modifications of miRNAs were rarely considered in the extracellular space. Here, we developed a highly accurate bioinformatic method, called miNTA, to identify 3’ non-templated additions (NTAs) of miRNAs using small RNA-sequencing data. Using miNTA, we conducted an in-depth analysis of miRNA 3’ NTA profiles in 1047 extracellular RNA-sequencing data sets of 4 types of biofluids. This analysis identified abundant 3’ uridylation and adenylation of miRNAs, with an estimated false discovery rate of <5%. Strikingly, we found that 3’ uridylation levels enabled segregation of different types of biofluids, more effectively than overall miRNA expression levels. This observation suggests that 3’ NTA levels possess fluid-specific information insensitive to batch effects. In addition, we observed that extracellular miRNAs with 3’ uridylations are enriched in processes related to angiogenesis, apoptosis and inflammatory response, and this type of modification may stabilize base-pairing between miRNAs and their target genes. Together, our study provides a comprehensive landscape of miRNA NTAs in human biofluids, which paves way for further biomarker discoveries. The insights generated in our work built a foundation for future functional, mechanistic and translational discoveries.

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Traces of post-transcriptional RNA modifications in deep sequencing data

Biological Chemistry ◽

10.1515/bc.2011.043 ◽

2011 ◽

Vol 392 (4) ◽

Cited By ~ 44

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.

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miREV: An online database and tool to uncover potential reference RNAs and biomarkers in small-RNA sequencing data sets from Extracellular Vesicles enriched samples

Journal of Molecular Biology ◽

10.1016/j.jmb.2021.167070 ◽

2021 ◽

pp. 167070

Author(s):

Alex Hildebrandt ◽

Michael W. Pfaffl ◽

Esther N.M. Nolte-'t Hoen ◽

Benedikt Kirchner

Keyword(s):

Rna Sequencing ◽

Extracellular Vesicles ◽

Small Rna ◽

Small Rna Sequencing ◽

Data Sets ◽

Online Database ◽

Sequencing Data

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

10.21203/rs.2.16723/v1 ◽

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.

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Genome-wide analysis of changes in miRNA and target gene expression reveals key roles in heterosis for Chinese cabbage biomass

Horticulture Research ◽

10.1038/s41438-021-00474-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Peirong Li ◽

Tongbing Su ◽

Deshuang Zhang ◽

Weihong Wang ◽

Xiaoyun Xin ◽

...

Keyword(s):

Chinese Cabbage ◽

Leaf Development ◽

Target Genes ◽

Expression Patterns ◽

Seedling Stage ◽

Differentially Expressed ◽

Small Rna Sequencing ◽

Leaf Morphogenesis ◽

Expression Levels ◽

Parental Lines

AbstractHeterosis is a complex phenomenon in which hybrids show better phenotypic characteristics than their parents do. Chinese cabbage (Brassica rapa L. spp. pekinensis) is a popular leafy crop species, hybrids of which are widely used in commercial production; however, the molecular basis of heterosis for biomass of Chinese cabbage is poorly understood. We characterized heterosis in a Chinese cabbage F1 hybrid cultivar and its parental lines from the seedling stage to the heading stage; marked heterosis of leaf weight and biomass yield were observed. Small RNA sequencing revealed 63 and 50 differentially expressed microRNAs (DEMs) at the seedling and early-heading stages, respectively. The expression levels of the majority of miRNA clusters in the F1 hybrid were lower than the mid-parent values (MPVs). Using degradome sequencing, we identified 1,819 miRNA target genes. Gene ontology (GO) analyses demonstrated that the target genes of the MPV-DEMs and low parental expression level dominance (ELD) miRNAs were significantly enriched in leaf morphogenesis, leaf development, and leaf shaping. Transcriptome analysis revealed that the expression levels of photosynthesis and chlorophyll synthesis-related MPV-DEGs (differentially expressed genes) were significantly different in the F1 hybrid compared to the parental lines, resulting in increased photosynthesis capacity and chlorophyll content in the former. Furthermore, expression of genes known to regulate leaf development was also observed at the seedling stage. Arabidopsis plants overexpressing BrGRF4.2 and bra-miR396 presented increased and decreased leaf sizes, respectively. These results provide new insight into the regulation of target genes and miRNA expression patterns in leaf size and heterosis for biomass of B. rapa.

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Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

Microbiology Research ◽

10.3390/microbiolres12020022 ◽

2021 ◽

Vol 12 (2) ◽

pp. 317-334

Author(s):

Omar Alaqeeli ◽

Li Xing ◽

Xuekui Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Classification Tree ◽

Area Under The Curve ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Tree Algorithms ◽

R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Briefings in Bioinformatics ◽

10.1093/bib/bbz096 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1581-1595 ◽

Cited By ~ 6

Author(s):

Xinlei Zhao ◽

Shuang Wu ◽

Nan Fang ◽

Xiao Sun ◽

Jue Fan

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Reference Data ◽

Predictive Accuracy ◽

Cell Types ◽

Superior Performance ◽

Marker Genes ◽

Data Sets ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

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Characterizing the cancer-associated microbiome with small RNA sequencing data

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2019.11.166 ◽

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

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Computational Prediction of miRNA Genes from Small RNA Sequencing Data

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2015.00007 ◽

2015 ◽

Vol 3 ◽

Cited By ~ 25

Author(s):

Wenjing Kang ◽

Marc R. FriedlÃ¤nder

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Computational Prediction ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Mirna Genes

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

Journal of Integrative Bioinformatics ◽

10.1515/jib-2016-307 ◽

2016 ◽

Vol 13 (5) ◽

Cited By ~ 6

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.

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