mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Elucidation of transfer RNAs as stress regulating agents and the experimental strategies to conceive the functional role of tRNA-derived fragments in plants

In plants, the transfer RNAs (tRNAs) exhibit their profound influence in orchestrating diverse physiological activities like cell growth, development, and response to several surrounding stimuli. The tRNAs, which were known to restrict their function solely in deciphering the codons, are now emerging as frontline defenders in stress biology. The plants that are constantly confronted with a huge panoply of stresses rely on tRNA-mediated stress regulation by altering the tRNA abundance, curbing the transport of tRNAs, fragmenting the mature tRNAs during stress. Among them, the studies on the generation of transfer RNA-derived fragments (tRFs) and their biological implication in stress response have attained huge interest. In plants, the tRFs hold stable expression patterns and regulate biological functions under diverse environmental conditions. In this review, we discuss the fate of plant tRNAs upon stress and thereafter how the tRFs are metamorphosed into sharp ammunition to wrestle with stress. We also address the various methods developed to date for uncovering the role of tRFs and their function in plants.

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

The hitchhikers' guide to RNA sequencing and functional analysis

DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.

piRNA-like small RNAs target transposable elements in a Clade IV parasitic nematode

The small RNA (sRNA) pathways identified in the model organism Caenorhabditis elegans are not widely conserved across nematodes. For example, the PIWI pathway and PIWI-interacting RNAs (piRNAs) are involved in regulating and silencing transposable elements (TE) in most animals but have been lost in nematodes outside of the C. elegans group (Clade V), and little is known about how nematodes regulate TEs in the absence of the PIWI pathway. Here, we investigated the role of sRNAs in the Clade IV parasitic nematode Strongyloides ratti by comparing two genetically identical adult stages (the parasitic female and free-living female). We identified putative small-interfering RNAs, microRNAs and tRNA-derived sRNA fragments that are differentially expressed between the two adult stages. Two classes of sRNAs were predicted to regulate TE activity including (i) a parasite-associated class of 21-22 nt long sRNAs with a 5' uridine (21-22Us) and a 5' monophosphate, and (ii) 27 nt long sRNAs with a 5' guanine/adenine (27GAs) and a 5' modification. The 21-22Us show striking resemblance to the 21U PIWI-interacting RNAs found in C. elegans, including an AT rich upstream sequence, overlapping loci and physical clustering in the genome. Overall, we have shown that an alternative class of sRNAs compensate for the loss of piRNAs and regulate TE activity in nematodes outside of Clade V.

Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome's Dark Matter

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs' synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Identification and expression analysis of ten novel small non-coding RNAs (sncRNAs) in cancer cells using a high-throughput sequencing approach

Non-coding RNAs are characterized as RNA molecules, which lack the capacity to encode protein structures and appear to include a level of internal signals. Moreover, they control various stages of gene expression, thus controlling the cell physiology and development. In this study, we implemented a high-throughput sequencing approach based on the primary semi-conductor technology and computational tools, in order to identity novel small non-coding RNAs. Fourteen human cancer cell lines were cultured, and RNA samples were enriched for small RNAs following semi-conductor next generation sequencing (NGS). Bioinformatics analysis of NGS data revealed the existence of several classes of ncRNAs using the miRDeep* and CPSS 2.0 software. To investigate the existence of the predicted non-coding RNA sequences in cDNA pools of cell lines, a developed qPCR-based assay was implemented. The structure of each novel small ncRNA was visualized, using the RNAfold algorithm. Our results support the existence of twenty (20) putative new small ncRNAs, ten (10) of which have had their expression experimentally validated and presented differential profiles in cancerous and normal cells. A deeper comprehension of the ncRNAs interactive network and its role in cancer can therefore be translated into a wide range of clinical applications. Despite this progress, further scientific research from different perspectives and in different fields is needed, so that the riddle of the human transcriptome can be solved.

DANSR: A Tool for the Detection of Annotated and Novel Small RNAs

Existing small noncoding RNA analysis tools are optimized for processing short sequencing reads (17-35 nucleotides) to monitor microRNA expression. However, these strategies under-represent many biologically relevant classes of small noncoding RNAs in the 36-200 nucleotides length range (tRNAs, snoRNAs, etc.). To address this, we developed DANSR, a tool for the detection of annotated and novel small RNAs using sequencing reads with variable lengths (ranging from 17-200 nt). While DANSR is broadly applicable to any small RNA dataset, we applied it to a cohort of matched normal, primary, and distant metastatic colorectal cancer specimens to demonstrate its ability to quantify annotated small RNAs, discover novel genes, and calculate differential expression. DANSR is available as an open source tool.

Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements

Simple Summary

Transposable elements (TEs) are DNA sequences that are, or were, able to move (transpose) within the genome of a single cell. They were first discovered by Barbara McClintock while working on maize, and they make up a large fraction of the genome. Transpositions can result in mutations and they can alter the genome size. Cells regulate the activity of TEs using a variety of mechanisms, such as chemical modifications of DNA and small RNAs. Machine learning (ML) is an interdisciplinary subject that studies computer algorithms that can improve through experience and by the use of data. ML has been successfully applied to a variety of problems in bioinformatics and has exhibited favorable precision and speed. Here, we provide a systematic and guided review on the ML and bioinformatic methods and tools that are used for the analysis of the regulation of TEs.

Abstract

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.

RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

Background

RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.

Results

Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.

Conclusions

RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04211-7.

MicroRNAs Regulating Autophagy in Neurodegeneration

Social and economic impacts of neurodegenerative diseases (NDs) become more prominent in our constantly aging population. Currently, due to the lack of knowledge about the aetiology of most NDs, only symptomatic treatment is available for patients. Hence, researchers and clinicians are in need of solid studies on pathological mechanisms of NDs. Autophagy promotes degradation of pathogenic proteins in NDs, while microRNAs post-transcriptionally regulate multiple signalling networks including autophagy. This chapter will critically discuss current research advancements in the area of microRNAs regulating autophagy in NDs. Moreover, we will introduce basic strategies and techniques used in microRNA research. Delineation of the mechanisms contributing to NDs will result in development of better approaches for their early diagnosis and effective treatment.

DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

isomiRs-Hidden Soldiers in the miRNA Regulatory Army, and How to Find Them?

Numerous studies on microRNAs (miRNA) in cancer and other diseases have been accompanied by diverse computational approaches and experimental methods to predict and validate miRNA biological and clinical significance as easily accessible disease biomarkers. In recent years, the application of the next-generation deep sequencing for the analysis and discovery of novel RNA biomarkers has clearly shown an expanding repertoire of diverse sequence variants of mature miRNAs, or isomiRs, resulting from alternative post-transcriptional processing events, and affected by (patho)physiological changes, population origin, individual's gender, and age. Here, we provide an in-depth overview of currently available bioinformatics approaches for the detection and visualization of both mature miRNA and cognate isomiR sequences. An attempt has been made to present in a systematic way the advantages and downsides of in silico approaches in terms of their sensitivity and accuracy performance, as well as used methods, workflows, and processing steps, and end output dataset overlapping issues. The focus is given to the challenges and pitfalls of isomiR expression analysis. Specifically, we address the availability of tools enabling research without extensive bioinformatics background to explore this fascinating corner of the small RNAome universe that may facilitate the discovery of new and more reliable disease biomarkers.

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.

Versatile interactions and bioinformatics analysis of noncoding RNAs

Advances in RNA sequencing technologies and computational methodologies have provided a huge impetus to noncoding RNA (ncRNA) study. Once regarded as inconsequential results of transcriptional promiscuity, ncRNAs were later found to exert great roles in various aspects of biological functions. They are emerging as key players in gene regulatory networks by interacting with other biomolecules (DNA, RNA or protein). Here, we provide an overview of ncRNA repertoire and highlight recent discoveries of their versatile interactions. To better investigate the ncRNA-mediated regulation, it is necessary to make full use of innovative sequencing techniques and computational tools. We further describe a comprehensive workflow for in silico ncRNA analysis, providing up-to-date platforms, databases and tools dedicated to ncRNA identification and functional annotation.

Small noncoding RNA discovery and profiling with sRNAtools based on high-throughput sequencing

Small noncoding RNAs (sRNA/sncRNAs) are generated from different genomic loci and play important roles in biological processes, such as cell proliferation and the regulation of gene expression. Next-generation sequencing (NGS) has provided an unprecedented opportunity to discover and quantify diverse kinds of sncRNA, such as tRFs (tRNA-derived small RNA fragments), phasiRNAs (phased, secondary, small-interfering RNAs), Piwi-interacting RNA (piRNAs) and plant-specific 24-nt short interfering RNAs (siRNAs). However, currently available web-based tools do not provide approaches to comprehensively analyze all of these diverse sncRNAs. This study presents a novel integrated platform, sRNAtools (https://bioinformatics.caf.ac.cn/sRNAtools), that can be used in conjunction with high-throughput sequencing to identify and functionally annotate sncRNAs, including profiling microRNAss, piRNAs, tRNAs, small nuclear RNAs, small nucleolar RNAs and rRNAs and discovering isomiRs, tRFs, phasiRNAs and plant-specific 24-nt siRNAs for up to 21 model organisms. Different modules, including single case, batch case, group case and target case, are developed to provide users with flexible ways of studying sncRNA. In addition, sRNAtools supports different ways of uploading small RNA sequencing data in a very interactive queue system, while local versions based on the program package/Docker/virtureBox are also available. We believe that sRNAtools will greatly benefit the scientific community as an integrated tool for studying sncRNAs.

Deciphering miRNAs' Action through miRNA Editing

MicroRNAs (miRNAs) are small non-coding RNAs with the capability of modulating gene expression at the post-transcriptional level either by inhibiting messenger RNA (mRNA) translation or by promoting mRNA degradation. The outcome of a myriad of physiological processes and pathologies, including cancer, cardiovascular and metabolic diseases, relies highly on miRNAs. However, deciphering the precise roles of specific miRNAs in these pathophysiological contexts is challenging due to the high levels of complexity of their actions. Indeed, regulation of mRNA expression by miRNAs is frequently cell/organ specific; highly dependent on the stress and metabolic status of the organism; and often poorly correlated with miRNA expression levels. Such biological features of miRNAs suggest that various regulatory mechanisms control not only their expression, but also their activity and/or bioavailability. Several mechanisms have been described to modulate miRNA action, including genetic polymorphisms, methylation of miRNA promoters, asymmetric miRNA strand selection, interactions with RNA-binding proteins (RBPs) or other coding/non-coding RNAs. Moreover, nucleotide modifications (A-to-I or C-to-U) within the miRNA sequences at different stages of their maturation are also critical for their functionality. This regulatory mechanism called "RNA editing" involves specific enzymes of the adenosine/cytidine deaminase family, which trigger single nucleotide changes in primary miRNAs. These nucleotide modifications greatly influence a miRNA's stability, maturation and activity by changing its specificity towards target mRNAs. Understanding how editing events impact miRNA's ability to regulate stress responses in cells and organs, or the development of specific pathologies, e.g., metabolic diseases or cancer, should not only deepen our knowledge of molecular mechanisms underlying complex diseases, but can also facilitate the design of new therapeutic approaches based on miRNA targeting. Herein, we will discuss the current knowledge on miRNA editing and how this mechanism regulates miRNA biogenesis and activity.

A benchmarking of pipelines for detecting ncRNAs from RNA-Seq data

Next-Generation Sequencing (NGS) is a high-throughput technology widely applied to genome sequencing and transcriptome profiling. RNA-Seq uses NGS to reveal RNA identities and quantities in a given sample. However, it produces a huge amount of raw data that need to be preprocessed with fast and effective computational methods. RNA-Seq can look at different populations of RNAs, including ncRNAs. Indeed, in the last few years, several ncRNAs pipelines have been developed for ncRNAs analysis from RNA-Seq experiments. In this paper, we analyze eight recent pipelines (iSmaRT, iSRAP, miARma-Seq, Oasis 2, SPORTS1.0, sRNAnalyzer, sRNApipe, sRNA workbench) which allows the analysis not only of single specific classes of ncRNAs but also of more than one ncRNA classes. Our systematic performance evaluation aims at guiding users to select the appropriate pipeline for processing each ncRNA class, focusing on three key points: (i) accuracy in ncRNAs identification, (ii) accuracy in read count estimation and (iii) deployment and ease of use.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Automated analysis of small RNA datasets with RAPID

Understanding the role of short-interfering RNA (siRNA) in diverse biological processes is of current interest and often approached through small RNA sequencing. However, analysis of these datasets is difficult due to the complexity of biological RNA processing pathways, which differ between species. Several properties like strand specificity, length distribution, and distribution of soft-clipped bases are few parameters known to guide researchers in understanding the role of siRNAs. We present RAPID, a generic eukaryotic siRNA analysis pipeline, which captures information inherent in the datasets and automatically produces numerous visualizations as user-friendly HTML reports, covering multiple categories required for siRNA analysis. RAPID also facilitates an automated comparison of multiple datasets, with one of the normalization techniques dedicated for siRNA knockdown analysis, and integrates differential expression analysis using DESeq2.

Human Novel MicroRNA Seq-915_x4024 in Keratinocytes Contributes to Skin Regeneration by Suppressing Scar Formation

Early in gestation, wounds in fetal skin heal by regeneration, in which microRNAs play key roles. Seq-915_x4024 is a novel microRNA candidate confirmed by deep sequencing and mirTools 2.0. It is highly expressed in fetal keratinocytes during early gestation. Using an in vitro wound-healing assay, Transwell cell migration assay, and MTS proliferation assay, we demonstrated that keratinocytes overexpressing seq-915_x4024 exhibited higher proliferative activity and the ability to promote fibroblast migration and fibroblast proliferation. These characteristics of keratinocytes are the same biological behaviors as those of fetal keratinocytes, which contribute to skin regeneration. In addition, seq-915_x4024 suppressed the expression of the pro-inflammatory markers TNF-α, IL-6, and IL-8 and the pro-inflammatory chemokines CXCL1 and CXCL5. We also demonstrated that seq-915_x4024 regulates TGF-β isoforms and the extracellular matrix. Moreover, using an in vivo wound-healing model, we demonstrated that overexpression of seq-915_x4024 in keratinocytes suppresses inflammatory cell infiltration and scar formation. Using bioinformatics analyses, luciferase reporter assays, and western blotting, we further demonstrated that Sar1A, Smad2, TNF-α, and IL-8 are direct targets of seq-915_x4024. Furthermore, the expression of phosphorylated Smad2 and Smad3 was reduced by seq-915_x4024. Seq-915_x4024 could be used as an anti-fibrotic factor for the treatment of wound healing.

An Overview of miRNA and miRNA Target Analysis Tools

microRNA molecules have been shown to play various significant roles in many physiological and pathophysiological processes in living organisms. The tremendous interest in these molecules has led to the significant development and constant release of a number of computational tools useful for basic as well as advanced miRNA-related analyses. These approaches have various constantly evolving utilities, such as detection, target prediction, functional annotation, and many others. In this chapter, we provide an overview of several computational tools useful for broadly defined plant miRNA analysis.

miRandb: A Metadatabase of Online Resources of miRNA and miRNA Targets

MicroRNA (miRNA) studies deliver numerous types of information including miRNA identification, sequence of miRNAs, target prediction, roles in diseases, and interactions in signaling pathways. Considering the different types of miRNA data, the number of miRNA databases has been increasing quickly. While resources have been planned to simplify miRNA analysis, scientists are facing the challenging task of choosing the most proper tool to retrieve related information. In this chapter, we introduce the use of miRandb, a resource that we have established to present an outline of different types of miRNA online resources and to simplify finding the right miRNA information that scientists need for their research. miRandb offers a user-friendly platform to find related information about any miRNA data among more than 188 present miRNA databases. miRandb has an easy procedure, and information can be retrieved by miRNA category resources. Each database comprises numerous kinds of information including database activity, description, main and unique features, organism, URL, publication, category, published year, citations per year, last update, and relative popularity. miRandb provides several opportunities and facilitates access to diverse classes of microRNA resources. miRandb is available at http://miRandb.ir .

miRNomes involved in imparting thermotolerance to crop plants

Thermal stress is one of the challenges to crop plants that negatively impacts crop yield. To overcome this ever-growing problem, utilization of regulatory mechanisms, especially microRNAs (miRNAs), that provide efficient and precise regulation in a targeted manner have been found to play determining roles. Besides their roles in plant growth and development, many recent studies have shown differential regulation of several miRNAs during abiotic stresses including heat stress (HS). Thus, understanding the underlying mechanism of miRNA-mediated gene expression during HS will enable researchers to exploit this regulatory mechanism to address HS responses. This review focuses on the miRNAs and regulatory networks that were involved in physiological, metabolic and morphological adaptations during HS in plant, specifically in crops. Illustrated examples including, the miR156-SPL, miR169-NF-YA5, miR395-APS/AST, miR396-WRKY, etc., have been discussed in specific relation to the crop plants. Further, we have also discussed the available plant miRNA databases and bioinformatics tools useful for miRNA identification and study of their regulatory role in response to HS. Finally, we have briefly discussed the future prospects about the miRNA-related mechanisms of HS for improving thermotolerance in crop plants.

The evolutionary dynamics of microRNAs in domestic mammals

MiRNAs are crucial regulators of gene expression found across both the plant and animal kingdoms. While the number of annotated miRNAs deposited in miRBase has greatly increased in recent years, few studies provided comparative analyses across sets of related species, or investigated the role of miRNAs in the evolution of gene regulation. We generated small RNA libraries across 5 mammalian species (cow, dog, horse, pig and rabbit) from 4 different tissues (brain, heart, kidney and testis). We identified 1676 miRBase and 413 novel miRNAs by manually curating the set of computational predictions obtained from miRCat and miRDeep2. Our dataset spanning five species has enabled us to investigate the molecular mechanisms and selective pressures driving the evolution of miRNAs in mammals. We highlight the important contributions of intronic sequences (366 orthogroups), duplication events (135 orthogroups) and repetitive elements (37 orthogroups) in the emergence of new miRNA loci. We use this framework to estimate the patterns of gains and losses across the phylogeny, and observe high levels of miRNA turnover. Additionally, the identification of lineage-specific losses enables the characterisation of the selective constraints acting on the associated target sites. Compared to the miRBase subset, novel miRNAs tend to be more tissue specific. 20 percent of novel orthogroups are restricted to the brain, and their target repertoires appear to be enriched for neuron activity and differentiation processes. These findings may reflect an important role for young miRNAs in the evolution of brain expression plasticity. Many seed sequences appear to be specific to either the cow or the dog. Analyses on the associated targets highlight the presence of several genes under artificial positive selection, suggesting an involvement of these miRNAs in the domestication process. Altogether, we provide an overview on the evolutionary mechanisms responsible for miRNA turnover in 5 domestic species, and their possible contribution to the evolution of gene regulation.

QsRNA-seq: a method for high-throughput profiling and quantifying small RNAs

The ability to profile and quantify small non-coding RNAs (sRNAs), specifically microRNAs (miRNAs), using high-throughput sequencing is challenging because of their small size. We developed QsRNA-seq, a method for preparation of sRNA libraries for high-throughput sequencing that overcomes this difficulty by enabling a gel-free separation of fragments shorter than 100 nt that differ only by 20 nt in length. The method allows the use of unique molecular identifiers for quantification and is more amenable to automation than gel-based methods. We show that QsRNA-seq gives very accurate, comprehensive, and reproducible results by looking at miRNAs in Caenorhabditis elegans embryos and larvae.

A compilation of Web-based research tools for miRNA analysis

Since the discovery of microRNAs (miRNAs), a class of noncoding RNAs that regulate the gene expression posttranscriptionally in sequence-specific manner, there has been a release of number of tools useful for both basic and advanced applications. This is because of the significance of miRNAs in many pathophysiological conditions including cancer. Numerous bioinformatics tools that have been developed for miRNA analysis have their utility for detection, expression, function, target prediction and many other related features. This review provides a comprehensive assessment of web-based tools for the miRNA analysis that does not require prior knowledge of any computing languages.

miR-MaGiC improves quantification accuracy for small RNA-seq

Objective

Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns.

Results

We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to “functional groups”. We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method’s ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3418-2) contains supplementary material, which is available to authorized users.

Investigating piwi-interacting RNA regulome in human neuroblastoma

Remarkable attempts have been exercised in recent years using high-throughput technologies to identify and decipher the functions of piRNAs in various abnormalities like cancer. However, piRNAs in the oncogenesis of neuroblastoma (NB) has not been reported yet even after their illustrated roles in neurological processes. Therefore, we investigated the piRNA transcriptome in IMR-32 and SH-SY-5Y NB cell lines by employing high-throughput next-generation sequencing after confirming the expression of three associated PIWILs both at mRNAs and protein level by qRT-PCR and immunofluroscence, respectively. We identified a common pool of 525 piRNAs of 26-32 nts long expressed in both the cell lines. The possible functions of these piRNAs were charted by predicting their targeting on retrotransposon-containing 1769 mRNAs differentially expressed in 39 NB cell lines followed by network and pathway analysis. The analysis revealed that majority of the target binding sites in NB fall within retrotransposons residing within the 3'UTR of target mRNA transcripts like miRNA-targets. Further, we validated the expression of key piRNAs and their target genes enriched in cancer-related networks, pathways and biological processes which are hypothesized to play crucial roles in neoplastic events of NB. We believe that the evidence of piRNAs in human NB and their possible contribution to its pathogenesis reported in this work will open up new exciting possibilities for piRNA-mediated therapeutics for this malignancy.

miR-126-5p by direct targeting of JNK-interacting protein-2 (JIP-2) plays a key role in Theileria-infected macrophage virulence

Theileria annulata is an apicomplexan parasite that infects and transforms bovine macrophages that disseminate throughout the animal causing a leukaemia-like disease called tropical theileriosis. Using deep RNAseq of T. annulata-infected B cells and macrophages we identify a set of microRNAs induced by infection, whose expression diminishes upon loss of the hyper-disseminating phenotype of virulent transformed macrophages. We describe how infection-induced upregulation of miR-126-5p ablates JIP-2 expression to release cytosolic JNK to translocate to the nucleus and trans-activate AP-1-driven transcription of mmp9 to promote tumour dissemination. In non-disseminating attenuated macrophages miR-126-5p levels drop, JIP-2 levels increase, JNK1 is retained in the cytosol leading to decreased c-Jun phosphorylation and dampened AP-1-driven mmp9 transcription. We show that variation in miR-126-5p levels depends on the tyrosine phosphorylation status of AGO2 that is regulated by Grb2-recruitment of PTP1B. In attenuated macrophages Grb2 levels drop resulting in less PTP1B recruitment, greater AGO2 phosphorylation, less miR-126-5p associated with AGO2 and a consequent rise in JIP-2 levels. Changes in miR-126-5p levels therefore, underpin both the virulent hyper-dissemination and the attenuated dissemination of T. annulata-infected macrophages.

Oasis 2: improved online analysis of small RNA-seq data

Background

Small RNA molecules play important roles in many biological processes and their dysregulation or dysfunction can cause disease. The current method of choice for genome-wide sRNA expression profiling is deep sequencing.

Results

Here we present Oasis 2, which is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module.

Conclusions

Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.

Availability and Implementation

Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at https://oasis.dzne.de

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2047-z) contains supplementary material, which is available to authorized users.

Genome-wide profiling of the PIWI-interacting RNA-mRNA regulatory networks in epithelial ovarian cancers

PIWI-interacting (piRNAs), ~23–36 nucleotide-long small non-coding RNAs (sncRNAs), earlier believed to be germline-specific, have now been identified in somatic cells, including cancer cells. These sncRNAs impact critical biological processes by fine-tuning gene expression at post-transcriptional and epigenetic levels. The expression of piRNAs in ovarian cancer, the most lethal gynecologic cancer is largely uncharted. In this study, we investigated the expression of PIWILs by qRT-PCR and western blotting and then identified piRNA transcriptomes in tissues of normal ovary and two most prevalent epithelial ovarian cancer subtypes, serous and endometrioid by small RNA sequencing. We detected 219, 256 and 234 piRNAs in normal ovary, endometrioid and serous ovarian cancer samples respectively. We observed piRNAs are encoded from various genomic regions, among which introns harbor the majority of them. Surprisingly, piRNAs originated from different genomic contexts showed the varied level of conservations across vertebrates. The functional analysis of predicted targets of differentially expressed piRNAs revealed these could modulate key processes and pathways involved in ovarian oncogenesis. Our study provides the first comprehensive piRNA landscape in these samples and a useful resource for further functional studies to decipher new mechanistic views of piRNA-mediated gene regulatory networks affecting ovarian oncogenesis. The RNA-seq data is submitted to GEO database (GSE83794).

Bioinformatic Analysis of MicroRNA Sequencing Data

The vital role of microRNAs (miRNAs) involved in gene expression regulation has been confirmed in many biological processes. With the growing power and reducing cost of next-generation sequencing, more and more researchers turn to apply this high-throughput method to solve their biological problems. For miRNAs with known sequences, their expression profiles can be generated from the sequencing data. It also allows us to identify some novel miRNAs and explore the sequence variations under different conditions. Currently, there are a handful of tools available to analyze the miRNA sequencing data with separated or combined features, such as reads preprocessing, mapping and differential expression analysis. However, to our knowledge, a hands-on guideline for miRNA sequencing data analysis covering all steps is not available. Here we will utilize a set of published tools to perform the miRNA analysis with detailed explanation. Particularly, the miRNA target prediction and annotation may provide useful information for further experimental verification.

Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests

The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5′ end and a more flexible 3′ end with the possibility of 3′ tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage).

LncRNA Structural Characteristics in Epigenetic Regulation

The rapid development of new generation sequencing technology has deepened the understanding of genomes and functional products. RNA-sequencing studies in mammals show that approximately 85% of the DNA sequences have RNA products, for which the length greater than 200 nucleotides (nt) is called long non-coding RNAs (lncRNA). LncRNAs now have been shown to play important epigenetic regulatory roles in key molecular processes, such as gene expression, genetic imprinting, histone modification, chromatin dynamics, and other activities by forming specific structures and interacting with all kinds of molecules. This paper mainly discusses the correlation between the structure and function of lncRNAs with the recent progress in epigenetic regulation, which is important to the understanding of the mechanism of lncRNAs in physiological and pathological processes.

CPSS 2.0: a computational platform update for the analysis of small RNA sequencing data

SUMMARY

Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research.

AVAILABILITY AND IMPLEMENTATION

CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/.

CONTACT

zyuanwei@ustc.edu.cn or qshi@ustc.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Circulating microRNAs and Bioinformatics Tools to Discover Novel Diagnostic Biomarkers of Pediatric Diseases

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the post-transcriptional level. Current studies have shown that miRNAs are also present in extracellular spaces, packaged into various membrane-bound vesicles, or associated with RNA-binding proteins. Circulating miRNAs are highly stable and can act as intercellular messengers to affect many physiological processes. MicroRNAs circulating in body fluids have generated strong interest in their potential use as clinical biomarkers. In fact, their remarkable stability and the relative ease of detection make circulating miRNAs ideal tools for rapid and non-invasive diagnosis. This review summarizes recent insights about the origin, functions and diagnostic potential of extracellular miRNAs by especially focusing on pediatric diseases in order to explore the feasibility of alternative sampling sources for the development of non-invasive pediatric diagnostics. We will also discuss specific bioinformatics tools and databases for circulating miRNAs focused on the identification and discovery of novel diagnostic biomarkers of pediatric diseases.

Association of microRNAs with Argonaute proteins in the malaria mosquito Anopheles gambiae after blood ingestion

Drastic changes in gene expression occur after adult female mosquitoes take a blood meal and use the nutrients for egg maturation. A growing body of evidence indicates that microRNAs (miRNAs) contribute to this tightly controlled tissue- and stage-specific gene expression. To investigate the role of miRNAs, we monitored miRNA expression in the mosquito Anopheles gambiae during the 72-h period immediately after blood feeding. We also measured the association of miRNAs with Argonaute 1 (Ago1) and Argonaute 2 (Ago2) to assess the functional status of individual miRNA species. Overall, 173 mature miRNAs were precipitated with Ago1 and Ago2, including 12 new miRNAs, the orthologs of which are found thus far only in other Anopheles species. Ago1 is the predominant carrier of miRNAs in Anopheles gambiae. The abundance and Ago loading of most of the mature miRNAs were relatively stable after blood ingestion. However, miRNAs of the miR-309/286/2944 cluster were considerably upregulated after blood feeding. Injection of the specific antagomir for miR-309 resulted in smaller developing oocytes and ultimately fewer eggs. In addition, the Ago association of some miRNAs was not proportional to their cellular abundance, suggesting that integration of miRNAs into the Ago complexes is regulated by additional mechanisms.

Small RNA sequencing revealed dysregulated piRNAs in Alzheimer's disease and their probable role in pathogenesis

PIWI-interacting RNAs (piRNAs), ∼23-36 nucleotide-long small non-coding RNAs, earlier believed to be germline-specific, have now been identified in somatic cells including neural cells. However, piRNAs have not yet been studied in the human brain (HB) and Alzheimer's disease (AD)-affected brain. In this study, by next-generation small RNA sequencing, 564 and 451 piRNAs were identified in the HB and AD-affected brain respectively. The majority of the neuronal piRNAs have intronic origin wherein primary piRNAs are mostly from the negative strand. piRNAs originating from the coding sequence of mRNAs and tRNAs are highly conserved compared to other genomic contexts. We found 1923 mRNAs significantly down-regulated in AD as the predicted targets of 125 up-regulated piRNAs. The filtering of targets based on our criteria coupled with pathway enrichment analysis of all the predicted targets resulted in five most significant AD-associated pathways enriched with four genes (CYCS, LIN7C, KPNA6, and RAB11A) found to be regulated by four piRNAs. The qRT-PCR study verified the reciprocal expression of piRNAs and their targets. This study provides the first evidence of piRNAs in the HB and AD which will provide the foundation for future studies to unravel the regulatory role of piRNAs in the human brain and associated diseases. The sequencing data have been submitted to the GEO database (Accession no. GSE85075).

Comparative microRNAome analysis of the testis and ovary of the Chinese giant salamander

MicroRNAs (miRNAs) are 18-24 nucleotides non-coding RNAs that regulate gene expression by post-transcriptional suppression of mRNA. The Chinese giant salamander (CGS, Andrias davidianus), which is an endangered species, has become one of the important models of animal evolution; however, no miRNA studies on this species have been conducted. In this study, two small RNA libraries of CGS ovary and testis were constructed using deep sequencing technology. A bioinformatics pipeline was developed to distinguish miRNA sequences from other classes of small RNAs represented in the sequencing data. We found that many miRNAs and other small RNAs such as piRNA and tsRNA were abundant in CGS tissue. A total of 757 and 756 unique miRNAs were annotated as miRNA candidates in the ovary and testis respectively. We identified 145 miRNAs in CGS ovary and 155 miRNAs in CGS testis that were homologous to those in Xenopus laevis ovary and testis respectively. Forty-five miRNAs were more highly expressed in ovary than in testis and 21 miRNAs were more highly expressed in testis than in ovary. The expression profiles of the selected miRNAs (miR-451, miR-10c, miR-101, miR-202, miR-7a and miR-499) had their own different roles in other eight tissues and different development stages of testis and ovary, suggesting that these miRNAs play vital regulatory roles in sexual differentiation, gametogenesis and development in CGS. To our knowledge, this is the first study to reveal miRNA profiles that are related to male and female CGS gonads and provide insights into sex differences in miRNA expression in CGS.

Upregulation of Circulating miR-195-3p in Heart Failure

OBJECTIVES

Many circulating microRNAs (miRs) have been shown to have potential biomarker effects in cardiovascular disease. We studied the dysregulation of circulating miR-195-3p in patients with heart failure (HF) to elucidate its value as a potential biomarker for HF.

METHODS

Eight ischemic HF (IHF) patients, 8 nonischemic HF patients (NIHF), and 8 healthy volunteers (matched by age and sex - normal controls [NCs]) were chosen for miR sequencing. The plasma RNA was extracted, and a small RNA library of HF was established and then sequenced using next-generation sequencing (NGS) technology. The miR-195-3p was selected for a second clinical study in 60 IHF, 48 NIHF patients, and 35 NCs for qRT-PCR validation.

RESULTS

The expression of circulating miR-195-3p in the IHF group was increased 69.5-fold compared with the NC group using NGS technique, and it was the most elevated in all upregulated miRs. MiR-195-3p was ranked in the top 1 of all upregulated miRs in contribution to HF based on a random forest model analysis. The upregulation of circulating miR-195-3p was also validated with the qRT-PCR method, and receiver operating characteristic curve analysis showed that the area under the curve (AUC) was 0.831.

CONCLUSIONS

The circulating miR-195-3p was upregulated in IHF and NIHF patients and could be a potential biomarker for HF.

miRNA assays in the clinical laboratory: workflow, detection technologies and automation aspects

microRNAs (miRNAs) are short non-coding RNA molecules that regulate gene expression in eukaryotes. Their differential abundance is indicative or even causative for a variety of pathological processes including cancer or cardiovascular disorders. Due to their important biological function, miRNAs represent a promising class of novel biomarkers that may be used to diagnose life-threatening diseases, and to monitor disease progression. Further, they may guide treatment selection or dosage of drugs. miRNAs from blood or derived fractions are particularly interesting candidates for routine laboratory applications, as they can be measured in most clinical laboratories already today. This assures a good accessibility of respective tests. Albeit their great potential, miRNA-based diagnostic tests have not made their way yet into the clinical routine, and hence no standardized workflows have been established to measure miRNAs for patients' benefit. In this review we summarize the detection technologies and workflow options that exist to measure miRNAs, and we describe the advantages and disadvantages of each of these options. Moreover, we also provide a perspective on data analysis aspects that are vital for translation of raw data into actionable diagnostic test results.

QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing

BACKGROUND

Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously.

RESULTS

QuickMIRSeq considers the unique nature of miRNAs and combines many important features into its implementation. First, it takes advantage of high redundancy of miRNA reads and introduces joint mapping of multiple samples to reduce computational time. Second, it incorporates the strand information in the alignment step for more accurate quantification. Third, reads potentially arising from background noise are filtered out to improve the reliability of miRNA detection. Fourth, sequences aligned to miRNAs with mismatches are remapped to a reference genome to further reduce false positives. Finally, QuickMIRSeq generates a rich set of QC metrics and publication-ready plots.

CONCLUSIONS

The rich visualization features implemented allow end users to interactively explore the results and gain more insights into miRNA-seq data analyses. The high degree of automation and interactivity in QuickMIRSeq leads to a substantial reduction in the time and effort required for miRNA-seq data analysis.

Evaluation of microRNA alignment techniques

Genomic alignment of small RNA (smRNA) sequences such as microRNAs poses considerable challenges due to their short length (∼21 nucleotides [nt]) as well as the large size and complexity of plant and animal genomes. While several tools have been developed for high-throughput mapping of longer mRNA-seq reads (>30 nt), there are few that are specifically designed for mapping of smRNA reads including microRNAs. The accuracy of these mappers has not been systematically determined in the case of smRNA-seq. In addition, it is unknown whether these aligners accurately map smRNA reads containing sequence errors and polymorphisms. By using simulated read sets, we determine the alignment sensitivity and accuracy of 16 short-read mappers and quantify their robustness to mismatches, indels, and nontemplated nucleotide additions. These were explored in the context of a plant genome (Oryza sativa, ∼500 Mbp) and a mammalian genome (Homo sapiens, ∼3.1 Gbp). Analysis of simulated and real smRNA-seq data demonstrates that mapper selection impacts differential expression results and interpretation. These results will inform on best practice for smRNA mapping and enable more accurate smRNA detection and quantification of expression and RNA editing.

miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis

Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading. To avoid these restraints, we have developed a suite that offers the identification of miRNA, mRNA and circRNAs that can be applied to any sequenced organism. Additionally, it enables differential expression, miRNA-mRNA target prediction and/or functional analysis. The miARma-Seq pipeline is presented as a stand-alone tool that is both easy to install and flexible in terms of its use, and that brings together well-established software in a single bundle. Our suite can analyze a large number of samples due to its multithread design. By testing miARma-Seq in validated datasets, we demonstrate here the benefits that can be gained from this tool by making it readily accessible to the research community.