Small RNA Sequencing Datasets Research Articles

A Comprehensive Analysis of miRNA/isomiR Expression with Gender Difference.

Although microRNAs (miRNAs) have been widely studied as epigenetic regulation molecules, fewer studies focus on the gender difference at the miRNA and isomiR expression levels. In this study, we aim to understand the potential relationships between gender difference and miRNA/isomiR expression through a comprehensive analysis of small RNA-sequencing datasets based on different human diseases and tissues. Based on specific samples from males and females, we determined that some miRNAs may be diversely expressed between different tissues and genders. Thus, these miRNAs may exhibit inconsistent and even opposite expression between males and females. According to deregulated miRNA expression profiles, some dominantly expressed miRNA loci were selected to analyze isomiR expression patterns using rates of dominant isomiRs. In some miRNA loci, isomiRs showed statistical significance between tumor and normal samples and between males and females samples, suggesting that isomiR expression patterns are not always invariable but may vary between males and females, as well as among different tissues, tumors, and normal samples. The divergence implicates the fluctuation in the expression of miRNA and its detailed expression at the isomiR levels. The divergence also indicates that gender difference may be an important factor that affects the screening of disease-associated miRNAs and isomiRs. This study suggests that miRNA/isomiR expression and gender difference may be more complex than previously assumed and should be further studied according to specific samples from males or females.

MiR-isomiRExp: a web-server for the analysis of expression of miRNA at the miRNA/isomiR levels.

MicroRNA (miRNA) locus has been found that can generate a series of varied isomiR sequences. Most studies always focus on determining miRNA level, however, the canonical miRNA sequence is only a specific member in the multiple isomiRs. Some studies have shown that isomiR sequences play versatile roles in biological progress, and the analysis and research should be simultaneously performed at the miRNA/isomiR levels. Based on the biological characteristics of miRNA and isomiR, we developed miR-isomiRExp to analyze expression pattern of miRNA at the miRNA/isomiR levels, provide insights into tracking miRNA/isomiR maturation and processing mechanisms, and reveal functional characteristics of miRNA/isomiR. Simultaneously, we also performed expression analysis of specific human diseases using public small RNA sequencing datasets based on the analysis platform, which may help in surveying the potential deregulated miRNA/isomiR expression profiles, especially sequence and function-related isomiRs for further interaction analysis and study. The miR-isomiRExp platform provides miRNA/isomiR expression patterns and more information to study deregulated miRNA loci and detailed isomiR sequences. This comprehensive analysis will enrich experimental miRNA studies. miR-isomiRExp is available at http://server.malab.cn/miRisomiRExp/.

MiTRATA: a web-based tool for microRNA Truncation and Tailing Analysis.

We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis--the analysis of 3' modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATA's strengths lie in (i) its biologist-focused web interface, (ii) improved scalability via parallel processing and (iii) its uniqueness as a webtool to perform microRNA truncation and tailing analysis. miTRATA is developed in Python and PHP. It is available as a web-based application from https://wasabi.dbi.udel.edu/∼apps/ta/. meyers@dbi.udel.edu Supplementary data are available at Bioinformatics online.

Evolutionary and Expression Analysis of miR-#-5p and miR-#-3p at the miRNAs/isomiRs Levels

We mainly discussed miR-#-5p and miR-#-3p under three aspects: (1) primary evolutionary analysis of human miRNAs; (2) evolutionary analysis of miRNAs from different arms across the typical 10 vertebrates; (3) expression pattern analysis of miRNAs at the miRNA/isomiR levels using public small RNA sequencing datasets. We found that no bias can be detected between the numbers of 5p-miRNA and 3p-miRNA, while miRNAs from miR-#-5p and miR-#-3p show variable nucleotide compositions. IsomiR expression profiles from the two arms are always stable, but isomiR expressions in diseased samples are prone to show larger degree of dispersion. miR-#-5p and miR-#-3p have relative independent evolution/expression patterns and datasets of target mRNAs, which might also contribute to the phenomena of arm selection and/or arm switching. Simultaneously, miRNA/isomiR expression profiles may be regulated via arm selection and/or arm switching, and the dynamic miRNAome and isomiRome will adapt to functional and/or evolutionary pressures. A comprehensive analysis and further experimental study at the miRNA/isomiR levels are quite necessary for miRNA study.

In-Depth Exploration of miRNA: A New Approach to Study miRNA at the miRNA/isomiR Levels

Accumulating evidences suggest that microRNA (miRNA) is not a single sequence, but a cluster of isomiR sequences with various 5’/3’ ends and length distributions. The typical analysis always focuses on the annotated canonical miRNAs, although the multiple isomiRs have been reported as potential regulatory molecules with versatile biological roles. Herein, we develop an approach to analyze miRNA at the miRNA/isomiR levels. Firstly, isomiR expression profiles are estimated based on miRNA locus. Secondly, isomiR expression profiles are compared between clustered/homologous miRNAs. Thirdly, isomiR expression profiles are assessed at the isomiR level. Those isomiRs with the same “seed sequences” are classified as a special miRNA class. Finally, isomiR:mRNA interaction is predicted at the isomiR level, and target mRNAs and functional enrichment analysis can be performed to explore biological roles. Furthermore, the approach is applied to analyze miRNA at the miRNA/isomiR levels using small RNA sequencing datasets of tumor and normal cells. The analysis at the miRNA/isomiR levels will track miRNA maturation process and biological genesis, and provide more points to explore versatile biological roles of the small non-coding RNAs. Keywords: Approach, expression profile, microRNA (miRNA), isomiR.

A survey of overlooked viral infections in biological experiment systems.

It is commonly accepted that there are many unknown viruses on the planet. For the known viruses, do we know their prevalence, even in our experimental systems? Here we report a virus survey using recently published small (s)RNA sequencing datasets. The sRNA reads were assembled and contigs were screened for virus homologues against the NCBI nucleotide (nt) database using the BLASTn program. To our surprise, approximately 30% (28 out of 94) of publications had highly scored viral sequences in their datasets. Among them, only two publications reported virus infections. Though viral vectors were used in some of the publications, virus sequences without any identifiable source appeared in more than 20 publications. By determining the distributions of viral reads and the antiviral RNA interference (RNAi) pathways using the sRNA profiles, we showed evidence that many of the viruses identified were indeed infecting and generated host RNAi responses. As virus infections affect many aspects of host molecular biology and metabolism, the presence and impact of viruses needs to be actively investigated in experimental systems.

Fast-evolving microRNAs are highly expressed in the early embryo of Drosophila virilis

MicroRNAs are short non-protein-coding RNAs that regulate gene expression at the post-transcriptional level and are essential for the embryonic development of multicellular animals. Comparative genome-scale analyses have revealed that metazoan evolution is accompanied by the continuous acquisition of novel microRNA genes. This suggests that novel microRNAs may promote innovation and diversity in development. We determined the evolutionary origins of extant Drosophila microRNAs and estimated the sequence divergence between the 130 orthologous microRNAs in Drosophila melanogaster and Drosophila virilis, separated by 63 million years of evolution. We then generated small RNA sequencing data sets covering D. virilis development and explored the relationship between microRNA conservation and expression in a developmental context. We find that late embryonic, larval, and adult stages are dominated by conserved microRNAs. This pattern, however, does not hold for the early embryo, where rapidly evolving microRNAs are uniquely present at high levels in both species. The group of fast-evolving microRNAs that are highly expressed in the early embryo belong to two Drosophilid lineage-specific clusters: mir-310 ∼ 313 and mir-309 ∼ 6. These clusters have particularly complex evolutionary histories of duplication, gain, and loss. Our analyses suggest that the early embryo is a more permissive environment for microRNA changes and innovations. Fast-evolving microRNAs, therefore, have the opportunity to become preferentially integrated in early developmental processes, and may impact the evolution of development. The relationship between microRNA conservation and expression throughout the development of Drosophila differs from that previously observed for protein-coding genes.

ViRome: an R package for the visualization and analysis of viral small RNA sequence datasets

Summary: RNA interference (RNAi) is known to play an important part in defence against viruses in a range of species. Second-generation sequencing technologies allow us to assay these systems and the small RNAs that play a key role with unprecedented depth. However, scientists need access to tools that can condense, analyse and display the resulting data. Here, we present viRome, a package for R that takes aligned sequence data and produces a range of essential plots and reports.Availability and implementation: viRome is released under the BSD license as a package for R available for both Windows and Linux http://virome.sf.net. Additional information and a tutorial is available on the ARK-Genomics website: http://www.ark-genomics.org/bioinformatics/virome.Contact: mick.watson@roslin.ed.ac.uk

Genome-Wide Analysis Reveals Diversity of Rice Intronic miRNAs in Sequence Structure, Biogenesis and Function

Intronic microRNAs (in-miRNAs) as a class of miRNA family that regulates gene expression are still poorly understood in plants. In this study, we systematically identified rice in-miRNAs by re-mining eight published small RNA-sequencing datasets of rice. Furthermore, based on the collected expression, annotation, and putative target data, we investigated the structures, potential functions, and expression features of these in-miRNAs and the expression patterns of their host genes. A total of 153 in-miRNAs, which account for over 1/4 of the total rice miRNAs, were identified. In silico expression analysis showed that most of them (∼63%) are tissue or stage-specific. However, a majority of their host genes, especially those containing clustered in-miRNAs, exhibit stable high-level expressions among 513 microarray datasets. Although in-miRNAs show diversity in function and mechanism, the DNA methylation directed by 24 nt in-miRNAs may be the main pathway that controls the expressions of target genes, host genes, and even themselves. These findings may enhance our understanding on special functions of in-miRNAs, especially in mediating DNA methylation that was concluded to affect the stability of expression and structure of host and target genes.

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.

Small RNAs derived from structural non-coding RNAs

It has been shown in small RNA sequencing-based studies that some small RNA fragments are specifically processed from known structural non-coding RNAs, either through Dicer-dependent or Dicer-independent pathways. Although these small RNAs are often less abundant compared to microRNAs in normal mammalian tissues, they are always present in all sequenced libraries. In this paper, we use the ncPRO-seq pipeline, to describe different profiles of these small RNA fragments, and to discuss their potential processing pathways and functions. To assess whether more small RNA fragments can be detected in small RNA sequencing datasets, we decided to focus on small nuclear RNAs, abbreviated as snRNAs, which are associated with Sm ribonucleoproteins to form functional RNA–protein complexes. Here, we describe a group of small RNA fragments derived from snRNAs, which are typically highly enriched in regions bound by Sm proteins. Based on this, we propose the existence of a potential novel small RNA family associated with Sm proteins.

Deep Sequencing Reveals Novel MicroRNAs and Regulation of MicroRNA Expression during Cell Senescence

In cell senescence, cultured cells cease proliferating and acquire aberrant gene expression patterns. MicroRNAs (miRNAs) modulate gene expression through translational repression or mRNA degradation and have been implicated in senescence. We used deep sequencing to carry out a comprehensive survey of miRNA expression and involvement in cell senescence. Informatic analysis of small RNA sequence datasets from young and senescent IMR90 human fibroblasts identifies many miRNAs that are regulated (either up or down) with cell senescence. Comparison with mRNA expression profiles reveals potential mRNA targets of these senescence-regulated miRNAs. The target mRNAs are enriched for genes involved in biological processes associated with cell senescence. This result greatly extends existing information on the role of miRNAs in cell senescence and is consistent with miRNAs having a causal role in the process.

Systematic Curation of miRBase Annotation Using Integrated Small RNA High-Throughput Sequencing Data for C. elegans and Drosophila

MicroRNAs (miRNAs) are a class of 20–23 nucleotide small RNAs that regulate gene expression post-transcriptionally in animals and plants. Annotation of miRNAs by the miRNA database (miRBase) has largely relied on computational approaches. As a result, many miRBase entries lack experimental validation, and discrepancies between miRBase annotation and actual miRNA sequences are often observed. In this study, we integrated the small RNA sequencing (smRNA-seq) datasets in Caenorhabditis elegans and Drosophila melanogaster and devised an analytical pipeline coupled with detailed manual inspection to curate miRNA annotation systematically in miRBase. Our analysis reveals 19 (17.0%) and 51 (31.3%) miRNAs entries with detectable smRNA-seq reads have mature sequence discrepancies in C. elegans and D. melanogaster, respectively. These discrepancies frequently occur either for conserved miRNA families whose mature sequences were predicted according to their homologous counterparts in other species or for miRNAs whose precursor miRNA (pre-miRNA) hairpins produce an abundance of multiple miRNA isoforms or variants. Our analysis shows that while Drosophila pre-miRNAs, on average, produce less than 60% accurate mature miRNA reads in addition to their 5′ and 3′ variant isoforms, the precision of miRNA processing in C. elegans is much higher, at over 90%. Based on the revised miRNA sequences, we analyzed expression patterns of the more conserved (MC) and less conserved (LC) miRNAs and found that, whereas MC miRNAs are often co-expressed at multiple developmental stages, LC miRNAs tend to be expressed specifically at fewer stages.

Re-Inspection of Small RNA Sequence Datasets Reveals Several Novel Human miRNA Genes

BackgroundmiRNAs are key players in gene expression regulation. To fully understand the complex nature of cellular differentiation or initiation and progression of disease, it is important to assess the expression patterns of as many miRNAs as possible. Thereby, identifying novel miRNAs is an essential prerequisite to make possible a comprehensive and coherent understanding of cellular biology.Methodology/Principal FindingsBased on two extensive, but previously published, small RNA sequence datasets from human embryonic stem cells and human embroid bodies, respectively [1], we identified 112 novel miRNA-like structures and were able to validate miRNA processing in 12 out of 17 investigated cases. Several miRNA candidates were furthermore substantiated by including additional available small RNA datasets, thereby demonstrating the power of combining datasets to identify miRNAs that otherwise may be assigned as experimental noise.Conclusions/SignificanceOur analysis highlights that existing datasets are not yet exhaustedly studied and continuous re-analysis of the available data is important to uncover all features of small RNA sequencing.