miRNAkey: a software for microRNA deep sequencing analysis

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.

miND (miRNA NGS Discovery pipeline): a small RNA-seq analysis pipeline and report generator for microRNA biomarker discovery studies

In contrast to traditional methods like real-time polymerase chain reaction, next-generation sequencing (NGS), and especially small RNA-seq, enables the untargeted investigation of the whole small RNAome, including microRNAs (miRNAs) but also a multitude of other RNA species. With the promising application of small RNAs as biofluid-based biomarkers, small RNA-seq is the method of choice for an initial discovery study. However, the presentation of specific quality aspects of small RNA-seq data varies significantly between laboratories and is lacking a common (minimal) standard. The miRNA NGS Discovery pipeline (miND) aims to bridge the gap between wet lab scientist and bioinformatics with an easy to setup configuration sheet and an automatically generated comprehensive report that contains all essential qualitative and quantitative results that should be reported. Besides the standard steps like preprocessing, mapping, visualization, and quantification of reads, the pipeline also incorporates differential expression analysis when given the appropriate information regarding sample groups. Although miND has a focus on miRNAs, other RNA species like tRNAs, piRNA, snRNA, or snoRNA are included and mapping statistics are available for further analysis. miND has been developed and tested on a multitude of data sets with various RNA sources (tissue, plasma, extracellular vesicles, urine, etc.) and different species. miND is a Snakemake based pipeline and thus incorporates all advantages using a flexible workflow management system. Reference databases are downloaded, prepared and built with an included (but separate) workflow and thus can easily be updated to the most recent version but also stored for reproducibility. In conclusion, the miND pipeline aims to streamline the bioinformatics processing of small RNA-seq data by standardizing the processing from raw data to a final, comprehensive and reproducible report.

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.

Differential expression of miRNAs in skeletal muscles of Indian sheep with diverse carcass and muscle traits

The study presents the miRNA profiles of two Indian sheep populations with divergent carcass and muscle traits. The RNA sequencing of longissimus thoracis muscles from the two populations revealed a total of 400 known miRNAs. Myomirs or miRNAs specific to skeletal muscles identified in our data included oar-miR-1, oar-miR-133b, oar-miR-206 and oar-miR-486. Comparison of the two populations led to identification of 100 differentially expressed miRNAs (p < 0.05). A total of 45 miRNAs exhibited a log₂ fold change of ≥ ( ±) 3.0. Gene Ontology analysis revealed cell proliferation, epithelial to mesenchymal transition, apoptosis, immune response and cell differentiation as the most significant functions of the differentially expressed miRNAs. The differential expression of some miRNAs was validated by qRT-PCR analysis. Enriched pathways included metabolism of proteins and lipids, PI3K-Akt, EGFR and cellular response to stress. The microRNA-gene interaction network revealed miR-21, miR-155, miR-143, miR-221 and miR-23a as the nodal miRNAs, with multiple targets. MicroRNA-21 formed the focal point of the network with 42 interactions. The hub miRNAs identified in our study form putative regulatory candidates for future research on meat quality traits in Indian sheep. Our results provide insight into the biological pathways and regulatory molecules implicated in muscling traits of sheep.

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.

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.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

HIV-1 infection increases microRNAs that inhibit Dicer1, HRB and HIV-EP2, thereby reducing viral replication

HIV-1 is the causative agent of AIDS (Autoimmune Deficiency Syndrome). HIV-1 infection results in systemic CD4⁺ T cell depletion, thereby impairing cell-mediated immunity. MicroRNAs are short (~22 nucleotides long), endogenous single-stranded RNA molecules that regulate gene expression by binding to the 3' untranslated regions (3' UTR) of mRNA transcripts. The relation between HIV-1 infection and human miRNA expression profile has been previously investigated, and studies have shown that the virus can alter miRNA expression and vice versa. Here, we broaden the understanding of the HIV-1 infection process, and show that miRNA-186, 210 and 222 are up-regulated following HIV-1 infection of human Sup-T1 cells. As a result, the host miRNA target genes: Dicer1 (Double-Stranded RNA-Specific Endoribonuclease), HRB (HIV-1 Rev-binding protein) and HIV-EP2 (Human Immunodeficiency Virus Type I Enhancer Binding Protein 2), are down-regulated. Moreover, testing the miRNA-gene anti- correlation on the Jurkat and the HeLa-MAGI cell lines demonstrated the ability of the miRNAs to down-regulate viral expression as well. To conclude, we found that human miR-186, 210 and 222 directly regulate the human genes Dicer1, HRB and HIV-EP2, thus may be filling key roles during HIV-1 replication and miRNA biogenesis. This finding may contribute to the development of new therapeutic strategies.

Exploring miRNA-mRNA regulatory network in cardiac pathology in Na+/H+ exchanger isoform 1 transgenic mice

Numerous studies have demonstrated that Na⁺/H⁺ exchanger isoform 1 (NHE1) is elevated in myocardial diseases and its effect is detrimental. To better understand the involvement of NHE1, we have previously studied cardiac-specific NHE1 transgenic mice and shown that these mice develop cardiac hypertrophy, interstitial fibrosis, and cardiac dysfunction. The purpose of current study was to identify microRNAs and their mRNA targets involved in NHE1-mediated cardiac injury. An unbiased high-throughput sequencing study was performed on both microRNAs and mRNAs. RNA sequencing showed that differentially expressed genes were enriched in hypertrophic cardiomyopathy pathway by Kyoto Encyclopedia of Genes and Genomes annotation in NHE1 transgenic hearts. These genes were classified as contraction defects (e.g., Myl2, Myh6, Mybpc3, and Actb), impaired intracellular Ca²⁺ homeostasis (e.g., SERCA2a, Ryr2, Rcan1, and CaMKII delta), and signaling molecules for hypertrophic cardiomyopathy (e.g., Itga/b, IGF-1, Tgfb2/3, and Prkaa1/2). microRNA sequencing revealed that 15 microRNAs were differentially expressed (2-fold, P < 0.05). Six of them (miR-1, miR-208a-3p, miR-199a-5p, miR-21-5p, miR-146a-5p, and miR-30c-5p) were reported to be related to cardiac pathological functions. The integrative analysis of microRNA and RNA sequencing data identified several crucial microRNAs including miR-30c-5p, miR-199a-5p, miR-21-5p, and miR-34a-5p as well as 10 of their mRNA targets that may affect the heart via NFAT hypertrophy and cardiac hypertrophy signaling. Furthermore, important microRNAs and mRNA targets were validated by quantitative PCR. Our study comprehensively characterizes the expression patterns of microRNAs and mRNAs, establishes functional microRNA-mRNA pairs, elucidates the potential signaling pathways, and provides novel insights on the mechanisms underlying NHE1-medicated cardiac injury.

Comparative analysis of miRNA profile in human dendritic cells infected with respiratory syncytial virus and human metapneumovirus

OBJECTIVE

Human metapneumovirus (HMPV) and respiratory syncytial virus (RSV) are responsible for respiratory diseases, mostly in children. Despite the clinical and epidemiological similarities between these two pneumoviruses, they elicit different immune responses. This work aims to further our understanding of the differential immune response induced by these respiratory viruses by determining the changes of small non-coding RNAs (miRNAs), which regulate gene expression and are involved in numerous cellular processes including the immune system.

RESULTS

In the present study, we analyzed the expression of miRNA transcripts of human dendritic cells infected with RSV or HMPV by high throughput sequencing using Illumina sequencing technology. Further validation of miRNA expression by quantitative polymerase chain reaction indicated that HMPV infection up-regulated the expression of 2 miRNAs (hsa-miR-182-5p and hsa-miR-4634), while RSV infection induced significant expression of 3 miRNAs (hsa-miR-4448, hsa-miR-30a-5p and hsa-miR-4634). The predominant miRNA induced by both viruses was hsa-miR-4634.

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.

Differential Expression of MicroRNAs in Uterine Cervical Cancer and Its Implications in Carcinogenesis; An Integrative Approach

OBJECTIVES

Cervical cancer is the second most common cancer in women in developing countries, including India. Recently, microRNAs (miRNAs) are gaining importance in cancer biology because of their involvement in various cellular processes. The present study aimed to profile miRNA expression pattern in cervical cancer, identify their target genes, and understand their role in carcinogenesis.

METHODS

Human papillomavirus (HPV) infection statuses in samples were assessed by heminested polymerase chain reaction followed by direct DNA sequencing. Next-generation sequencing and miRNA microarray were used for miRNA profiling in cervical cancer cell lines and tissue samples, respectively. MicroRNA signature was validated by quantitative real-time PCR, and biological significance was elucidated using various in silico analyses.

RESULTS

Cervical cancer tissues samples were mostly infected by HPV type 16 (93%). MicroRNA profiling showed that the pattern of miRNA expression differed with respect to HPV positivity in cervical cancer cell lines. However, target and pathway analyses indicated identical involvement of these significantly deregulated miRNAs in HPV-positive cervical cancer cell lines irrespective of type of HPV infected. Microarray profiling identified a set of miRNAs that are differentially deregulated in cervical cancer tissue samples which were validated using quantitative real-time PCR. In silico analyses revealed that the signature miRNAs are mainly involved in PI3K-Akt and mTOR pathways.

CONCLUSIONS

The study identified that high-risk HPV induces similar carcinogenic mechanism irrespective of HPV type. The miRNA signature of cervical cancer and their target genes were also elucidated, thereby providing a better insight into the molecular mechanism underlying cervical cancer development.

Evaluation of Bioinformatics Approaches for Next-Generation Sequencing Analysis of microRNAs with a Toxicogenomics Study Design

MicroRNAs (miRNAs) are key post-transcriptional regulators that affect protein translation by targeting mRNAs. Their role in disease etiology and toxicity are well recognized. Given the rapid advancement of next-generation sequencing techniques, miRNA profiling has been increasingly conducted with RNA-seq, namely miRNA-seq. Analysis of miRNA-seq data requires several steps: (1) mapping the reads to miRBase, (2) considering mismatches during the hairpin alignment (windowing), and (3) counting the reads (quantification). The choice made in each step with respect to the parameter settings could affect miRNA quantification, differentially expressed miRNAs (DEMs) detection and novel miRNA identification. Furthermore, these parameters do not act in isolation and their joint effects impact miRNA-seq results and interpretation. In toxicogenomics, the variation associated with parameter setting should not overpower the treatment effect (such as the dose/time-dependent effect). In this study, four commonly used miRNA-seq analysis tools (i.e., miRDeep2, miRExpress, miRNAkey, sRNAbench) were comparatively evaluated with a standard toxicogenomics study design. We tested 30 different parameter settings on miRNA-seq data generated from thioacetamide-treated rat liver samples for three dose levels across four time points, followed by four normalization options. Because both miRExpress and miRNAkey yielded larger variation than that of the treatment effects across multiple parameter settings, our analyses mainly focused on the side-by-side comparison between miRDeep2 and sRNAbench. While the number of miRNAs detected by miRDeep2 was almost the subset of those detected by sRNAbench, the number of DEMs identified by both tools was comparable under the same parameter settings and normalization method. Change in the number of nucleotides out of the mature sequence in the hairpin alignment (window option) yielded the largest variation for miRNA quantification and DEMs detection. However, such a variation is relatively small compared to the treatment effect when the study focused on DEMs that are more critical to interpret the toxicological effect. While the normalization methods introduced a large variation in DEMs, toxic behavior of thioacetamide showed consistency in the trend of time-dose responses. Overall, miRDeep2 was found to be preferable over other choices when the window option allowed up to three nucleotides from both ends.

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.

miR-103 inhibits proliferation and sensitizes hemopoietic tumor cells for glucocorticoid-induced apoptosis

Glucocorticoid (GC) hormones are an important ingredient of leukemia therapy since they are potent inducers of lymphoid cell apoptosis. However, the development of GC resistance remains an obstacle in GC-based treatment. In the present investigation we found that miR-103 is upregulated in GC-sensitive leukemia cells treated by the hormone. Transfection of GC resistant cells with miR-103 sensitized them to GC induced apoptosis (GCIA), while miR-103 sponging of GC sensitive cells rendered them partially resistant. miR-103 reduced the expression of cyclin dependent kinase (CDK2) and its cyclin E1 target, thereby leading to inhibition of cellular proliferation. miR-103 is encoded within the fifth intron of PANK3 gene. We demonstrate that the GC receptor (GR) upregulates miR-103 by direct interaction with GC response element (GRE) in the PANK3 enhancer. Consequently, miR-103 targets the c-Myc activators c-Myb and DVL1, thereby reducing c-Myc expression. Since c-Myc is a transcription factor of the miR-17~92a poly-cistron, all six miRNAs of the latter are also downregulated. Of these, miR-18a and miR-20a are involved in GCIA, as they target GR and BIM, respectively. Consequently, GR and BIM expression are elevated, thus advancing GCIA. Altogether, this study highlights miR-103 as a useful prognostic biomarker and drug for leukemia management in the future.

Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges

One of the most significant biological discoveries of the last decade is represented by the reality that the vast majority of the transcribed genomic output comprises diverse classes of noncoding RNAs (ncRNAs) that may play key roles and/or be affected by many biochemical cellular processes (i.e., RNA editing), with implications in human health and disease. With 90% of the human genome being transcribed and novel classes of ncRNA emerging (tRNA-derived small RNAs and circular RNAs among others), the great majority of the human transcriptome suggests that many important ncRNA functions/processes are yet to be discovered. An approach to filling such vast void of knowledge has been recently provided by the increasing application of next-generation sequencing (NGS), offering the unprecedented opportunity to obtain a more accurate profiling with higher resolution, increased throughput, sequencing depth, and low experimental complexity, concurrently posing an increasing challenge in terms of efficiency, accuracy, and usability of data analysis software. This review provides an overview of ncRNAs, NGS technology, and the most recent/popular computational approaches and the challenges they attempt to solve, which are essential to a more sensitive and comprehensive ncRNA annotation capable of furthering our understanding of this still vastly uncharted genomic territory.

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.

isomiR-SEA: an RNA-Seq analysis tool for miRNAs/isomiRs expression level profiling and miRNA-mRNA interaction sites evaluation

Background

Massive parallel sequencing of transcriptomes, revealed the presence of many miRNAs and miRNAs variants named isomiRs with a potential role in several cellular processes through their interaction with a target mRNA. Many methods and tools have been recently devised to detect and quantify miRNAs from sequencing data. However, all of them are implemented on top of general purpose alignment methods, thus providing poorly accurate results and no information concerning isomiRs and conserved miRNA-mRNA interaction sites.

Results

To overcome these limitations we present a novel algorithm named isomiR-SEA, that is able to provide users with very accurate miRNAs expression levels and both isomiRs and miRNA-mRNA interaction sites precise classifications. Tags are mapped on the known miRNAs sequences thanks to a specialized alignment algorithm developed on top of biological evidence concerning miRNAs structure. Specifically, isomiR-SEA checks for miRNA seed presence in the input tags and evaluates, during all the alignment phases, the positions of the encountered mismatches, thus allowing to distinguish among the different isomiRs and conserved miRNA-mRNA interaction sites.

Conclusions

isomiR-SEA performances have been assessed on two public RNA-Seq datasets proving that the implemented algorithm is able to account for more reliable and accurate miRNAs expression levels with respect to those provided by two compared state of the art tools. Moreover, differently from the few methods currently available to perform isomiRs detection, the proposed algorithm implements the evaluation of isomiRs and conserved miRNA-mRNA interaction sites already in the first alignment phases, thus avoiding any additional filtering stages potentially responsible for the loss of useful information.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0958-0) contains supplementary material, which is available to authorized users.

MicroRNA from Moringa oleifera: Identification by High Throughput Sequencing and Their Potential Contribution to Plant Medicinal Value

Moringa oleifera is a widespread plant with substantial nutritional and medicinal value. We postulated that microRNAs (miRNAs), which are endogenous, noncoding small RNAs regulating gene expression at the post-transcriptional level, might contribute to the medicinal properties of plants of this species after ingestion into human body, regulating human gene expression. However, the knowledge is scarce about miRNA in Moringa. Furthermore, in order to test the hypothesis on the pharmacological potential properties of miRNA, we conducted a high-throughput sequencing analysis using the Illumina platform. A total of 31,290,964 raw reads were produced from a library of small RNA isolated from M. oleifera seeds. We identified 94 conserved and two novel miRNAs that were validated by qRT-PCR assays. Results from qRT-PCR trials conducted on the expression of 20 Moringa miRNA showed that are conserved across multiple plant species as determined by their detection in tissue of other common crop plants. In silico analyses predicted target genes for the conserved miRNA that in turn allowed to relate the miRNAs to the regulation of physiological processes. Some of the predicted plant miRNAs have functional homology to their mammalian counterparts and regulated human genes when they were transfected into cell lines. To our knowledge, this is the first report of discovering M. oleifera miRNAs based on high-throughput sequencing and bioinformatics analysis and we provided new insight into a potential cross-species control of human gene expression. The widespread cultivation and consumption of M. oleifera, for nutritional and medicinal purposes, brings humans into close contact with products and extracts of this plant species. The potential for miRNA transfer should be evaluated as one possible mechanism of action to account for beneficial properties of this valuable species.

iSRAP - a one-touch research tool for rapid profiling of small RNA-seq data

Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication. Currently, the advent of next-generation sequencing (NGS) technology for high-throughput profiling has further advanced the biological insights of non-coding RNA on a genome-wide scale and has become the preferred approach for the discovery and quantification of non-coding RNA species. Despite the routine practice of NGS, the processing of large data sets poses difficulty for analysis before conducting downstream experiments. Often, the current analysis tools are designed for specific RNA species, such as microRNA, and are limited in flexibility for modifying parameters for optimization. An analysis tool that allows for maximum control of different software is essential for drawing concrete conclusions for differentially expressed transcripts. Here, we developed a one-touch integrated small RNA analysis pipeline (iSRAP) research tool that is composed of widely used tools for rapid profiling of small RNAs. The performance test of iSRAP using publicly and in-house available data sets shows its ability of comprehensive profiling of small RNAs of various classes, and analysis of differentially expressed small RNAs. iSRAP offers comprehensive analysis of small RNA sequencing data that leverage informed decisions on the downstream analyses of small RNA studies, including extracellular vesicles such as exosomes.

Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques

The majority of the human transcriptome is defined as non-coding RNA (ncRNA), since only a small fraction of human DNA encodes for proteins, as reported by the ENCODE project. Several distinct classes of ncRNAs, such as transfer RNA, microRNA, and long non-coding RNA, have been classified, each with its own three-dimensional folding and specific function. As ncRNAs are highly abundant in living organisms and have been discovered to play important roles in many biological processes, there has been an ever increasing need to investigate the entire ncRNAome in further unbiased detail. Recently, the advent of next-generation sequencing (NGS) technologies has substantially increased the throughput of transcriptome studies, allowing an unprecedented investigation of ncRNAs, as regulatory pathways and novel functions involving ncRNAs are now also emerging. The huge amount of transcript data produced by NGS has progressively required the development and implementation of suitable bioinformatics workflows, complemented by knowledge-based approaches, to identify, classify, and evaluate the expression of hundreds of ncRNAs in normal and pathological conditions, such as cancer. In this mini-review, we present and discuss current bioinformatics advances in the development of such computational approaches to analyze and classify the ncRNA component of human transcriptome sequence data obtained from NGS technologies.

Optimization of miRNA-seq data preprocessing

The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our understanding of the impact of their dysregulation on a global scale. Currently, next-generation sequencing is the platform of choice for the discovery and quantification of miRNAs. Despite this, there is no clear consensus on how the data should be preprocessed before conducting downstream analyses. Often overlooked, data preprocessing is an essential step in data analysis: the presence of unreliable features and noise can affect the conclusions drawn from downstream analyses. Using a spike-in dilution study, we evaluated the effects of several general-purpose aligners (BWA, Bowtie, Bowtie 2 and Novoalign), and normalization methods (counts-per-million, total count scaling, upper quartile scaling, Trimmed Mean of M, DESeq, linear regression, cyclic loess and quantile) with respect to the final miRNA count data distribution, variance, bias and accuracy of differential expression analysis. We make practical recommendations on the optimal preprocessing methods for the extraction and interpretation of miRNA count data from small RNA-sequencing experiments.

MicroRNA-29c mediates initiation of gastric carcinogenesis by directly targeting ITGB1

OBJECTIVE

Gastric cancer (GC) remains difficult to cure due to heterogeneity in a clinical challenge and the molecular mechanisms underlying this disease are complex and not completely understood. Accumulating evidence suggests that microRNAs (miRNAs) play an important role in GC, but the role of specific miRNAs involved in this disease remains elusive. We performed next generation sequencing (NGS)-based whole-transcriptome profiling to discover GC-specific miRNAs, followed by functional validation of results.

DESIGN

NGS-based miRNA profiles were generated in matched pairs of GCs and adjacent normal mucosa (NM). Quantitative RT-PCR validation of miR-29c expression was performed in 274 gastric tissues, which included two cohorts of matched GC and NM specimens. Functional validation of miR-29c and its gene targets was undertaken in cell lines, as well as K19-C2mE and K19-Wnt1/C2mE transgenic mice.

RESULTS

NGS analysis revealed four GC-specific miRNAs. Among these, miR-29c expression was significantly decreased in GC versus NM tissues (p<0.001). Ectopic expression of miR-29c mimics in GC cell lines resulted in reduced proliferation, adhesion, invasion and migration. High miR-29c expression suppressed xenograft tumour growth in nude mice. Direct interaction between miR-29c and its newly discovered target, ITGB1, was identified in cell lines and transgenic mice. MiR-29c expression demonstrated a stepwise decrease in wild type hyperplasia-dysplasia cascade in transgenic mice models of GC.

CONCLUSIONS

MiR-29c acts as a tumour suppressor in GC by directly targeting ITGB1. Loss of miR-29c expression is an early event in the initiation of gastric carcinogenesis and may serve as a diagnostic and therapeutic biomarker for patients with GC.

MicroRNA-based biotechnology for plant improvement

MicroRNAs (miRNAs) are an extensive class of newly discovered endogenous small RNAs, which negatively regulate gene expression at the post-transcription levels. As the application of next-generation deep sequencing and advanced bioinformatics, the miRNA-related study has been expended to non-model plant species and the number of identified miRNAs has dramatically increased in the past years. miRNAs play a critical role in almost all biological and metabolic processes, and provide a unique strategy for plant improvement. Here, we first briefly review the discovery, history, and biogenesis of miRNAs, then focus more on the application of miRNAs on plant breeding and the future directions. Increased plant biomass through controlling plant development and phase change has been one achievement for miRNA-based biotechnology; plant tolerance to abiotic and biotic stress was also significantly enhanced by regulating the expression of an individual miRNA. Both endogenous and artificial miRNAs may serve as important tools for plant improvement.

Methods of quantifying microRNAs for hypoxia research: classic and next generation

SIGNIFICANCE

Recent evidence is uncovering the ever-increasing importance of microribonucleic acids (miRNAs) in the hypoxia response. In order to investigate the important roles that these small RNAs play, methods of quantification whether using classic single-gene methods or genome-wide technologies are necessary to obtain a global picture of the differential expression of miRNAs in hypoxia and their interplay with protein coding genes.

RECENT ADVANCES

Building on the groundwork of classic quantitative polymerase chain reaction (qPCR) and microarrays, the advent of next-generation sequencing technology has revolutionized how small RNAs can be detected and quantified on a genome-wide scale and without a priori knowledge of the small RNA sequence. This method delivers accurate and comprehensive data on the expression and sequence of all expressed small RNAs, and the data can be further combined with other sequencing modalities to better understand miRNAs via integrated genomic analyses.

CRITICAL ISSUES

Advancing technology has increased the need for better methods of sample and library preparation and for bioinformatics tools. Speed, cost, sample input, and analysis expertise remain the mainstay critical issues of small RNA sequencing.

FUTURE DIRECTIONS

Future hypoxia research will benefit from the application of genome-wide sequencing technologies. Analyses that combine genomic, transcriptomic, chromosome conformation, DNA/RNA-protein binding, and proteomics will help greatly advance hypoxia miRNA research.

Transcriptomics and proteomics in stem cell research

Stem cells are capable of self-renewal and differentiation, and the processes regulating these events are among the most comprehensively investigated topics in life sciences. In particular, the molecular mechanisms of the self-renewal, proliferation, and differentiation of stem cells have been extensively examined. Multi-omics integrative analysis, such as transcriptomics combined with proteomics, is one of the most promising approaches to the systemic investigation of stem cell biology. We reviewed the available information on stem cells by examining published results using transcriptomic and proteomic characterization of the different stem cell processes. Comprehensive understanding of these important processes can only be achieved using a systemic methodology, and employing such method will strengthen the study on stem cell biology and promote the clinical applications of stem cells.

Next-generation sequencing of small RNAs from inner ear sensory epithelium identifies microRNAs and defines regulatory pathways

BACKGROUND

The mammalian inner ear contains sensory organs, the organ of Corti in the cochlea and cristae and maculae in the vestibule, with each comprised of patterned sensory epithelia that are responsible for hearing and balance. The development, cell fate, patterning, and innervation of both the sensory and nonsensory regions of the inner ear are governed by tight regulation involving, among others, transcription factors and microRNAs (miRNAs). In humans, mutations in specific miRNA genes are associated with hearing loss. In mice, experimental reduction or mutations of miRNAs in the inner ear leads to severe developmental and structural abnormalities. A comprehensive identification of miRNAs in the sensory epithelia and their gene targets will enable pathways of auditory and vestibular function to be defined.

RESULTS

In this study, we used Next-Generation Sequencing (NGS) to identify the most prominent miRNAs in the inner ear and to define miRNA-target pairs that form pathways crucial for the function of the sensory epithelial cells. NGS of RNA from inner ear sensory epithelial cells led to the identification of 455 miRNAs in both cochlear and vestibular sensory epithelium, with 30 and 44 miRNAs found in only cochlea or vestibule, respectively. miR-6715-3p and miR-6715-5p were defined for the first time in the inner ear. Gene targets were identified for each of these miRNAs, including Arhgap12, a GTPase activating protein, for miR-6715-3p, implicating this miRNA in sensory hair cell bundle development, actin reorganization, cell adhesion and inner ear morphogenesis.

CONCLUSIONS

This study provides a comprehensive atlas of miRNAs in the inner ear sensory epithelia. The results provide further support of the essential regulatory role of miRNAs in inner ear sensory epithelia and in regulating pathways that define development and growth of these cells.

CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data

Background

miRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues.

Results

We developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline’s superior performances, flexibility, and practical use in research and biomarker discovery.

Conclusions

CAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-423) contains supplementary material, which is available to authorized users.

eRNA: a graphic user interface-based tool optimized for large data analysis from high-throughput RNA sequencing

BACKGROUND

RNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers.

RESULTS

We developed a standalone tool with graphic user interface (GUI)-based analytic modules, known as eRNA. The capacity of performing parallel processing and sample management facilitates large data analyses by maximizing hardware usage and freeing users from tediously handling sequencing data. The module miRNA identification" includes GUIs for raw data reading, adapter removal, sequence alignment, and read counting. The module "mRNA identification" includes GUIs for reference sequences, genome mapping, transcript assembling, and differential expression. The module "Target screening" provides expression profiling analyses and graphic visualization. The module "Self-testing" offers the directory setups, sample management, and a check for third-party package dependency. Integration of other GUIs including Bowtie, miRDeep2, and miRspring extend the program's functionality.

CONCLUSIONS

eRNA focuses on the common tools required for the mapping and quantification analysis of miRNA-seq and mRNA-seq data. The software package provides an additional choice for scientists who require a user-friendly computing environment and high-throughput capacity for large data analysis. eRNA is available for free download at https://sourceforge.net/projects/erna/?source=directory.

Sequencing small RNA: introduction and data analysis fundamentals

Small RNAs are important transcriptional regulators within cells. With the advent of powerful Next Generation Sequencing platforms, sequencing small RNAs seems to be an obvious choice to understand their expression and its downstream effect. Additionally, sequencing provides an opportunity to identify novel and polymorphic miRNA. However, the biggest challenge is the appropriate data analysis pipeline, which is still in phase of active development by various academic groups. This chapter describes basic and advanced steps for small RNA sequencing analysis including quality control, small RNA alignment and quantification, differential expression analysis, novel small RNA identification, target prediction, and downstream analysis. We also provide a list of various resources for small RNA analysis.

plantDARIO: web based quantitative and qualitative analysis of small RNA-seq data in plants

High-throughput sequencing techniques have made it possible to assay an organism's entire repertoire of small non-coding RNAs (ncRNAs) in an efficient and cost-effective manner. The moderate size of small RNA-seq datasets makes it feasible to provide free web services to the research community that provide many basic features of a small RNA-seq analysis, including quality control, read normalization, ncRNA quantification, and the prediction of putative novel ncRNAs. DARIO is one such system that so far has been focussed on animals. Here we introduce an extension of this system to plant short non-coding RNAs (sncRNAs). It includes major modifications to cope with plant-specific sncRNA processing. The current version of plantDARIO covers analyses of mapping files, small RNA-seq quality control, expression analyses of annotated sncRNAs, including the prediction of novel miRNAs and snoRNAs from unknown expressed loci and expression analyses of user-defined loci. At present Arabidopsis thaliana, Beta vulgaris, and Solanum lycopersicum are covered. The web tool links to a plant specific visualization browser to display the read distribution of the analyzed sample. The easy-to-use platform of plantDARIO quantifies RNA expression of annotated sncRNAs from different sncRNA databases together with new sncRNAs, annotated by our group. The plantDARIO website can be accessed at http://plantdario.bioinf.uni-leipzig.de/.

ISRNA: an integrative online toolkit for short reads from high-throughput sequencing data

Integrative Short Reads NAvigator (ISRNA) is an online toolkit for analyzing high-throughput small RNA sequencing data. Besides the high-speed genome mapping function, ISRNA provides statistics for genomic location, length distribution and nucleotide composition bias analysis of sequence reads. Number of reads mapped to known microRNAs and other classes of short non-coding RNAs, coverage of short reads on genes, expression abundance of sequence reads as well as some other analysis functions are also supported. The versatile search functions enable users to select sequence reads according to their sub-sequences, expression abundance, genomic location, relationship to genes, etc. A specialized genome browser is integrated to visualize the genomic distribution of short reads. ISRNA also supports management and comparison among multiple datasets.

AVAILABILITY

ISRNA is implemented in Java/C++/Perl/MySQL and can be freely accessed at http://omicslab.genetics.ac.cn/ISRNA/.

An integrated transcriptome and epigenome analysis identifies a novel candidate gene for pancreatic cancer

BACKGROUND

Pancreatic cancer is a highly lethal cancer with limited diagnostic and therapeutic modalities.

METHODS

To begin to explore the genomic landscape of pancreatic cancer, we used massively parallel sequencing to catalog and compare transcribed regions and potential regulatory elements in two human cell lines derived from normal and cancerous pancreas.

RESULTS

By RNA-sequencing, we identified 2,146 differentially expressed genes in these cell lines that were enriched in cancer related pathways and biological processes that include cell adhesion, growth factor and receptor activity, signaling, transcription and differentiation. Our high throughput Chromatin immunoprecipitation (ChIP) sequence analysis furthermore identified over 100,000 regions enriched in epigenetic marks, showing either positive (H3K4me1, H3K4me3, RNA Pol II) or negative (H3K27me3) correlation with gene expression. Notably, an overall enrichment of RNA Pol II binding and depletion of H3K27me3 binding were seen in the cancer derived cell line as compared to the normal derived cell line. By selecting genes for further assessment based on this difference, we confirmed enhanced expression of aldehyde dehydrogenase 1A3 (ALDH1A3) in two larger sets of pancreatic cancer cell lines and in tumor tissues as compared to normal derived tissues.

CONCLUSIONS

As aldehyde dehydrogenase (ALDH) activity is a key feature of cancer stem cells, our results indicate that a member of the ALDH superfamily, ALDH1A3, may be upregulated in pancreatic cancer, where it could mark pancreatic cancer stem cells.

Species-specific microRNA regulation influences phenotypic variability: perspectives on species-specific microRNA regulation

Phenotypic divergence among animal species may be due in part to species-specific (SS) regulation of gene expression by small, non-coding regulatory RNAs termed "microRNAs". This phenomenon can be modulated by several variables. First, microRNA genes vary by their level of conservation, many of them being SS, or unique to a particular evolutionary lineage. Second, microRNA expression levels vary spatially and temporally in different species. Lastly, while microRNAs bind the 3'UTR of target genes in order to silence their expression, the binding sites themselves are often non-conserved. The variability of the miRNA-target paradigm between different species is thus multifactorial, and this paradigm has only just started to gain attention from researchers in various fields. Here we present and discuss recent findings regarding the characteristics and implications of SS microRNA regulation.

miReader: Discovering Novel miRNAs in Species without Sequenced Genome

Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences. The approach used NGS read data to build highly accurate miRNA models, molded through a Multi-boosting algorithm with Best-First Tree as its base classifier. It was comprehensively tested over large amount of experimental data from wide range of species including human, plants, nematode, zebrafish and fruit fly, performing consistently with >90% accuracy. Using the same tool over Illumina read data for Miscanthus, a plant whose genome is not sequenced; the study reported 21 novel mature miRNA duplex candidates. Considering the fact that miRNA discovery requires handling of high throughput data, the entire approach has been implemented in a standalone parallel architecture. This work is expected to cause a positive impact over the area of miRNA discovery in majority of species, where genomic sequence availability would not be a compulsion any more.

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

Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in a cost-effective way. In this study, we developed mirTools 2.0, an updated version of mirTools 1.0, which includes the following new features. (1) From miRNA discovery in mirTools 1.0, mirTools 2.0 allows users to detect and profile various types of ncRNAs, such as miRNA, tRNA, snRNA, snoRNA, rRNA, and piRNA. (2) From miRNA profiling in mirTools 1.0, mirTools 2.0 allows users to identify miRNA-targeted genes and performs detailed functional annotation of miRNA targets, including Gene Ontology, KEGG pathway and protein-protein interaction. (3) From comparison of two samples for differentially expressed miRNAs in mirTools 1.0, mirTools 2.0 allows users to detect differentially expressed ncRNAs between two experimental groups or among multiple samples. (4) Other significant improvements include strategies used to detect novel miRNAs and piRNAs, more taxonomy categories to discover more known miRNAs and a stand-alone version of mirTools 2.0. In conclusion, we believe that mirTools 2.0 (122.228.158.106/mr2_dev and centre.bioinformatics.zj.cn/mr2_dev) will provide researchers with more detailed insight into small RNA transcriptomes.

Comparison of Analysis Tools for miRNA High Throughput Sequencing Using Nerve Crush as a Model

Recent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.

Rat mir-155 generated from the lncRNA Bic is 'hidden' in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

MicroRNAs (miRNAs) are a class of small noncoding RNAs acting as post-transcriptional gene expression regulators in many physiological and pathological conditions. During the last few years, many novel mammalian miRNAs have been predicted experimentally with bioinformatics approaches and validated by next-generation sequencing. Although these strategies have prompted the discovery of several miRNAs, the total number of these genes still seems larger. Here, by exploiting the species conservation of human, mouse, and rat hairpin miRNAs, we discovered a novel rat microRNA, mir-155. We found that mature miR-155 is overexpressed in rat spleen myeloid cells treated with LPS, similarly to humans and mice. Rat mir-155 is annotated only on the alternate genome, suggesting the presence of other "hidden" miRNAs on this assembly. Therefore, we comprehensively extended the homology search also to mice and humans, finally validating 34 novel mammalian miRNAs (two in humans, five in mice, and up to 27 in rats). Surprisingly, 15 of these novel miRNAs (one for mice and 14 for rats) were found only on the alternate and not on the reference genomic assembly. To date, our findings indicate that the choice of genomic assembly, when mapping small RNA reads, is an important option that should be carefully considered, at least for these animal models. Finally, the discovery of these novel mammalian miRNA genes may contribute to a better understanding of already acquired experimental data, thereby paving the way to still unexplored investigations and to unraveling the function of miRNAs in disease models.

A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing

Background

MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need.

Results

We herein propose the first CLIP and PAR-CLIP sequencing analysis platform specifically for miRNA target analysis, namely miRTarCLIP. From scratch, it automatically removes adaptor sequences from raw reads, filters low quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, identifies high confidence miRNA target sites, and provides annotations from external databases. With multi-threading techniques and our novel C to T reversion procedure, miRTarCLIP greatly reduces the running time comparing to conventional approaches. In addition, miRTarCLIP serves with a web-based interface to provide better user experiences in browsing and searching targets of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing datasets. miRTarCLIP not only shows comparable results to that of other existing tools in a much faster speed, but also reveals interesting features among these putative target sites. Specifically, we used miRTarCLIP to disclose that T to C conversion within position 1-7 and that within position 8-14 of miRNA target sites are significantly different (p value = 0.02), and even more significant when focusing on sites targeted by top 102 highly expressed miRNAs only (p value = 0.01). These results comply with previous findings and further suggest that combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA target prediction.

Conclusion

To sum up, we devised a systematic approach for mining miRNA-target sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow with a graphical web-based browser, which provides a user friendly interface and detailed annotations of MTIs. We also showed through real-life examples that miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.

Bioinformatics of Cancer ncRNA in High Throughput Sequencing: Present State and Challenges

The numerous genome sequencing projects produced unprecedented amount of data providing significant information to the discovery of novel non-coding RNA (ncRNA). Several ncRNAs have been described to control gene expression and display important role during cell differentiation and homeostasis. In the last decade, high throughput methods in conjunction with approaches in bioinformatics have been used to identify, classify, and evaluate the expression of hundreds of ncRNA in normal and pathological states, such as cancer. Patient outcomes have been already associated with differential expression of ncRNAs in normal and tumoral tissues, providing new insights in the development of innovative therapeutic strategies in oncology. In this review, we present and discuss bioinformatics advances in the development of computational approaches to analyze and discover ncRNA data in oncology using high throughput sequencing technologies.

miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data

miRDeep and its varieties are widely used to quantify known and novel micro RNA (miRNA) from small RNA sequencing (RNAseq). This article describes miRDeep*, our integrated miRNA identification tool, which is modeled off miRDeep, but the precision of detecting novel miRNAs is improved by introducing new strategies to identify precursor miRNAs. miRDeep* has a user-friendly graphic interface and accepts raw data in FastQ and Sequence Alignment Map (SAM) or the binary equivalent (BAM) format. Known and novel miRNA expression levels, as measured by the number of reads, are displayed in an interface, which shows each RNAseq read relative to the pre-miRNA hairpin. The secondary pre-miRNA structure and read locations for each predicted miRNA are shown and kept in a separate figure file. Moreover, the target genes of known and novel miRNAs are predicted using the TargetScan algorithm, and the targets are ranked according to the confidence score. miRDeep* is an integrated standalone application where sequence alignment, pre-miRNA secondary structure calculation and graphical display are purely Java coded. This application tool can be executed using a normal personal computer with 1.5 GB of memory. Further, we show that miRDeep* outperformed existing miRNA prediction tools using our LNCaP and other small RNAseq datasets. miRDeep* is freely available online at http://www.australianprostatecentre.org/research/software/mirdeep-star.

YM500: a small RNA sequencing (smRNA-seq) database for microRNA research

MicroRNAs (miRNAs) are small RNAs ∼22 nt in length that are involved in the regulation of a variety of physiological and pathological processes. Advances in high-throughput small RNA sequencing (smRNA-seq), one of the next-generation sequencing applications, have reshaped the miRNA research landscape. In this study, we established an integrative database, the YM500 (http://ngs.ym.edu.tw/ym500/), containing analysis pipelines and analysis results for 609 human and mice smRNA-seq results, including public data from the Gene Expression Omnibus (GEO) and some private sources. YM500 collects analysis results for miRNA quantification, for isomiR identification (incl. RNA editing), for arm switching discovery, and, more importantly, for novel miRNA predictions. Wetlab validation on >100 miRNAs confirmed high correlation between miRNA profiling and RT-qPCR results (R = 0.84). This database allows researchers to search these four different types of analysis results via our interactive web interface. YM500 allows researchers to define the criteria of isomiRs, and also integrates the information of dbSNP to help researchers distinguish isomiRs from SNPs. A user-friendly interface is provided to integrate miRNA-related information and existing evidence from hundreds of sequencing datasets. The identified novel miRNAs and isomiRs hold the potential for both basic research and biotech applications.

ncPRO-seq: a tool for annotation and profiling of ncRNAs in sRNA-seq data

SUMMARY

Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs. It also has a module to identify regions significantly enriched with short reads, which cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or small interfering RNA (siRNA)-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that sRNA raw data from the three current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including mammals and plants.

AVAILABILITY

Source code, annotation files, manual and online version are available at http://ncpro.curie.fr/.

CONTACT

bioinfo.ncproseq@curie.fr or cciaudo@ethz.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

miR-BAG: bagging based identification of microRNA precursors

Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets. Here, we have presented a novel reliable approach, miR-BAG, developed to identify miRNA candidate regions in genomes by scanning sequences as well as by using next generation sequencing (NGS) data. miR-BAG utilizes a bootstrap aggregation based machine learning approach, successfully creating an ensemble of complementary learners to attain high accuracy while balancing sensitivity and specificity. miR-BAG was developed for wide range of species and tested extensively for performance over a wide range of experimentally validated data. Consideration of position-specific variation of triplet structural profiles and mature miRNA anchored structural profiles had a positive impact on performance. miR-BAG’s performance was found consistent and the accuracy level was observed to be >90% for most of the species considered in the present study. In a detailed comparative analysis, miR-BAG performed better than six existing tools. Using miR-BAG NGS module, we identified a total of 22 novel miRNA candidate regions in cow genome in addition to a total of 42 cow specific miRNA regions. In practice, discovery of miRNA regions in a genome demands high-throughput data analysis, requiring large amount of processing. Considering this, miR-BAG has been developed in multi-threaded parallel architecture as a web server as well as a user friendly GUI standalone version.