DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments

Computational tools supporting known miRNA identification

The study of small RNAs is a field that is expanding quickly. Other functional short RNA molecules other than microRNAs, and gene expression regulators, have been found in animals and plants. MicroRNAs play a significant role in host-microbe interactions, and parasite microRNAs may affect the host's innate immunity. Furthermore, short RNAs are intriguing non-invasive biomarker possibilities because they can be found in physiological fluids. These trends suggest that for many researchers, quick and simple techniques for expression profiling and subsequent downstream analysis of miRNA-seq data are crucial. We selected sRNAtoolbox to make integrated sRNA research easier. Each tool can be used separately or to explore and analyze sRNAbench results in further depth. A special focus was placed on the tools' usability. We review available miRNA research tools to have an overview of the evaluation of the tools. Mainly we evaluate the tool sRNAtoolbox.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

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.

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.

Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Are spliced ncRNA host genes distinct classes of lncRNAs?

Many small nucleolar RNAs and many of the hairpin precursors of miRNAs are processed from long non-protein-coding host genes. In contrast to their highly conserved and heavily structured payload, the host genes feature poorly conserved sequences. Nevertheless, there is mounting evidence that the host genes have biological functions beyond their primary task of carrying a ncRNA as payload. So far, no connections between the function of the host genes and the function of their payloads have been reported. Here we investigate whether there is evidence for an association of host gene function or mechanisms with the type of payload. To assess this hypothesis we test whether the miRNA host genes (MIRHGs), snoRNA host genes (SNHGs), and other lncRNA host genes can be distinguished based on sequence and/or structure features unrelated to their payload. A positive answer would imply a functional and mechanistic correlation between host genes and their payload, provided the classification does not depend on the presence and type of the payload. A negative answer would indicate that to the extent that secondary functions are acquired, they are not strongly constrained by the prior, primary function of the payload. We find that the three classes can be distinguished reliably when the classifier is allowed to extract features from the payloads. They become virtually indistinguishable, however, as soon as only sequence and structure of parts of the host gene distal from the snoRNAs or miRNA payload is used for classification. This indicates that the functions of MIRHGs and SNHGs are largely independent of the functions of their payloads. Furthermore, there is no evidence that the MIRHGs and SNHGs form coherent classes of long non-coding RNAs distinguished by features other than their payloads.

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.

Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing (NGS) technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq (sRNA-Seq) data analysis proved to be challenging due to non-unique genomic origin, short length, and abundant post-transcriptional modifications of sRNA species. Here, we present Manatee, an algorithm for the quantification of sRNA classes and the detection of novel expressed non-coding loci. Manatee combines prior annotation of sRNAs with reliable alignment density information and extensive rescue of usually neglected multimapped reads to provide accurate transcriptome-wide sRNA expression quantification. Comparison of Manatee against state-of-the-art implementations using real and simulated data demonstrates its high accuracy across diverse sRNA classes. Manatee also goes beyond common pipelines by identifying and quantifying expression from unannotated loci and microRNA isoforms (isomiRs). It is user-friendly, can be easily incorporated in pipelines, and provides a simplified output suitable for direct usage in downstream analyses and functional studies.

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.

Clinical value of non-coding RNAs in cardiovascular, pulmonary, and muscle diseases

Although a majority of the mammalian genome is transcribed to RNA, mounting evidence indicates that only a minor proportion of these transcriptional products are actually translated into proteins. Since the discovery of the first non-coding RNA (ncRNA) in the 1980s, the field has gone on to recognize ncRNAs as important molecular regulators of RNA activity and protein function, knowledge of which has stimulated the expansion of a scientific field that quests to understand the role of ncRNAs in cellular physiology, tissue homeostasis, and human disease. Although our knowledge of these molecules has significantly improved over the years, we have limited understanding of their precise functions, protein interacting partners, and tissue-specific activities. Adding to this complexity, it remains unknown exactly how many ncRNAs there are in existence. The increased use of high-throughput transcriptomics techniques has rapidly expanded the list of ncRNAs, which now includes classical ncRNAs (e.g., ribosomal RNAs and transfer RNAs), microRNAs, and long ncRNAs. In addition, splicing by-products of protein-coding genes and ncRNAs, so-called circular RNAs, are now being investigated. Because there is substantial heterogeneity in the functions of ncRNAs, we have summarized the present state of knowledge regarding the functions of ncRNAs in heart, lungs, and skeletal muscle. This review highlights the pathophysiologic relevance of these ncRNAs in the context of human cardiovascular, pulmonary, and muscle diseases.

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.

A comprehensive review of web-based resources of non-coding RNAs for plant science research

Non-coding RNAs (ncRNAs) are transcribed from genome but not translated into proteins. Many ncRNAs are key regulators of plants growth and development, metabolism and stress tolerance. In order to make the web-based ncRNA resources for plant science research be more easily accessible and understandable, we made a comprehensive review for 83 web-based resources of three types, including genome databases containing ncRNA data, microRNA (miRNA) databases and long non-coding RNA (lncRNA) databases. To facilitate effective usage of these resources, we also suggested some preferred resources of miRNAs and lncRNAs for performing meaningful analysis.

Next generation sequencing for miRNA profile of spleen CD4+ T cells in the murine model of acute asthma

AIM

To explore the miRNAs profile of CD4⁺ T lymphocytes in asthma via next generation sequencing.

METHODS

In the murine model of acute asthma, spleen CD4⁺ T lymphocytes were sorted, in which small RNAs were extracted and sequenced. Novel miRNAs were measured with real time quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

A total of 127 miRNAs were found to exhibit at least twofold change. In the 262 predicted novel miRNAs, 14 novel miRNAs were measured in qRT-PCR in the sorted CD4⁺ T cells or in the differentiated Th1/Th2 cells and novel miR-11 (xxx-m0228-3p) was significantly decreased in the sorted CD4⁺ T cells from the murine model of asthma and in the Th2 cells.

CONCLUSION

Aberrant miRNAs profile in the CD4⁺ T lymphocytes from acute asthma was documented.

miRDis: a Web tool for endogenous and exogenous microRNA discovery based on deep-sequencing data analysis

Small RNA sequencing is the most widely used tool for microRNA (miRNA) discovery, and shows great potential for the efficient study of miRNA cross-species transport, i.e., by detecting the presence of exogenous miRNA sequences in the host species. Because of the increased appreciation of dietary miRNAs and their far-reaching implication in human health, research interests are currently growing with regard to exogenous miRNAs bioavailability, mechanisms of cross-species transport and miRNA function in cellular biological processes. In this article, we present microRNA Discovery (miRDis), a new small RNA sequencing data analysis pipeline for both endogenous and exogenous miRNA detection. Specifically, we developed and deployed a Web service that supports the annotation and expression profiling data of known host miRNAs and the detection of novel miRNAs, other noncoding RNAs, and the exogenous miRNAs from dietary species. As a proof-of-concept, we analyzed a set of human plasma sequencing data from a milk-feeding study where 225 human miRNAs were detected in the plasma samples and 44 show elevated expression after milk intake. By examining the bovine-specific sequences, data indicate that three bovine miRNAs (bta-miR-378, -181* and -150) are present in human plasma possibly because of the dietary uptake. Further evaluation based on different sets of public data demonstrates that miRDis outperforms other state-of-the-art tools in both detection and quantification of miRNA from either animal or plant sources. The miRDis Web server is available at: http://sbbi.unl.edu/miRDis/index.php.

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users' input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.

The discovery potential of RNA processing profiles

Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.

Approaches to Detect microRNA Expression in T Cell Subsets and T Cell Differentiation

MicroRNAs (miRNAs) are crucial components of the molecular networks regulating differentiation and responses of T lymphocytes in health and disease. It is therefore essential to rely on robust methods of qualitative and quantitative investigation of miRNA expression in T cell subsets, and during T cell activation and differentiation. Here, we focus on different methods for miRNA analysis, including Northern blots, quantitative RT-PCR, and next-generation sequencing, and we discuss advantages and disadvantages of each method. While we mainly focus on the study of miRNA expression in human T lymphocytes, these methods can also be applied to other species and/or cell types.

Noncoding RNAs in Heart Failure

Heart failure is a major contributor to the healthcare burden and mortality worldwide. Current treatment strategies are able to slow down the transition of healthy heart into the failing one; nevertheless better understanding of the complex genetic regulation of maladaptive remodeling in the failing heart is essential for new drug discovery. Noncoding RNAs are key epigenetic regulators of cardiac gene expression and thus significantly influence cardiac homeostasis and functions.In this chapter we will discuss characteristics of noncoding RNAs, especially miRNAs, long noncoding RNAs, and circular RNAs, and review recent evidences proving their profound involvement during different stages of heart failure progression. Several open questions still prevent the extensive use of noncoding RNA-modulating therapies in clinics; yet they are becoming an attractive target to define novel regulatory mechanisms in the heart. In-depth study of their interaction with gene networks will refine our current view of heart failure and revolutionize the drug development in coming years.

Nuclear export factor 3 regulates localization of small nucleolar RNAs

Small nucleolar RNAs (snoRNAs) guide chemical modifications of ribosomal and small nuclear RNAs, functions that are carried out in the nucleus. Although most snoRNAs reside in the nucleolus, a growing body of evidence indicates that snoRNAs are also present in the cytoplasm and that snoRNAs move between the nucleus and cytoplasm by a mechanism that is regulated by lipotoxic and oxidative stress. Here, in a genome-wide shRNA-based screen, we identified nuclear export factor 3 (NXF3) as a transporter that alters the nucleocytoplasmic distribution of box C/D snoRNAs from the ribosomal protein L13a (Rpl13a) locus. Using RNA-sequencing analysis, we show that NXF3 associates not only with Rpl13a snoRNAs, but also with a broad range of box C/D and box H/ACA snoRNAs. Under homeostatic conditions, gain- or loss-of-function of NXF3, but not related family member NXF1, decreases or increases cytosolic Rpl13a snoRNAs, respectively. Furthermore, treatment with the adenylyl cyclase activator forskolin diminishes cytosolic localization of the Rpl13a snoRNAs through a mechanism that is dependent on NXF3 but not NXF1. Our results provide evidence of a new role for NXF3 in regulating the distribution of snoRNAs between the nuclear and cytoplasmic compartments.

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.

Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench

Recently, high-throughput sequencing (HTS) has revealed compelling details about the small RNA (sRNA) population in eukaryotes. These 20 to 25 nt noncoding RNAs can influence gene expression by acting as guides for the sequence-specific regulatory mechanism known as RNA silencing. The increase in sequencing depth and number of samples per project enables a better understanding of the role sRNAs play by facilitating the study of expression patterns. However, the intricacy of the biological hypotheses coupled with a lack of appropriate tools often leads to inadequate mining of the available data and thus, an incomplete description of the biological mechanisms involved. To enable a comprehensive study of differential expression in sRNA data sets, we present a new interactive pipeline that guides researchers through the various stages of data preprocessing and analysis. This includes various tools, some of which we specifically developed for sRNA analysis, for quality checking and normalization of sRNA samples as well as tools for the detection of differentially expressed sRNAs and identification of the resulting expression patterns. The pipeline is available within the UEA sRNA Workbench, a user-friendly software package for the processing of sRNA data sets. We demonstrate the use of the pipeline on a H. sapiens data set; additional examples on a B. terrestris data set and on an A. thaliana data set are described in the Supplemental Information A comparison with existing approaches is also included, which exemplifies some of the issues that need to be addressed for sRNA analysis and how the new pipeline may be used to do this.

RNA-bioinformatics: Tools, services and databases for the analysis of RNA-based regulation

The importance of RNA-based regulation is becoming more and more evident. Genome-wide sequencing efforts have shown that the majority of the DNA in eukaryotic genomes is transcribed. Advanced high-throughput techniques like CLIP for the genome-wide detection of RNA-protein interactions have shown that post-transcriptional regulation by RNA-binding proteins matches the complexity of transcriptional regulation. The need for a specialized and integrated analysis of RNA-based data has led to the foundation of the RNA Bioinformatics Center (RBC) within the German Network of Bioinformatics Infrastructure (de.NBI). This paper describes the tools, services and databases provided by the RBC, and shows example applications. Furthermore, we have setup an RNA workbench within the Galaxy framework. For an easy dissemination, we offer a virtualized version of Galaxy (via Galaxy Docker) enabling other groups to use our RNA workbench in a very simple way.

Identification of urinary exosomal noncoding RNAs as novel biomarkers in chronic kidney disease

In chronic kidney disease (CKD), the decline in the glomerular filtration rate is associated with increased morbidity and mortality and thus poses a major challenge for healthcare systems. While the contribution of tissue-derived miRNAs and mRNAs to CKD progression has been extensively studied, little is known about the role of urinary exosomes and their association with CKD. Exosomes are small, membrane-derived endocytic vesicles that contribute to cell-to-cell communication and are present in various body fluids, such as blood or urine. Next-generation sequencing approaches have revealed that exosomes are enriched in noncoding RNAs and thus exhibit great potential for sensitive nucleic acid biomarkers in various human diseases. Therefore, in this study we aimed to identify urinary exosomal ncRNAs as novel biomarkers for diagnosis of CKD. Since up to now most approaches have focused on the class of miRNAs, we extended our analysis to several other noncoding RNA classes, such as tRNAs, tRNA fragments (tRFs), mitochondrial tRNAs, or lincRNAs. For their computational identification from RNA-seq data, we developed a novel computational pipeline, designated as ncRNASeqScan. By these analyses, in CKD patients we identified 30 differentially expressed ncRNAs, derived from urinary exosomes, as suitable biomarkers for early diagnosis. Thereby, miRNA-181a appeared as the most robust and stable potential biomarker, being significantly decreased by about 200-fold in exosomes of CKD patients compared to healthy controls. Using a cell culture system for CKD indicated that urinary exosomes might indeed originate from renal proximal tubular epithelial cells.

V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature

Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .

Nutrition, microRNAs, and Human Health

MicroRNAs (miRs) hybridize with complementary sequences in mRNA and silence genes by destabilizing mRNA or preventing translation of mRNA. Over 60% of human protein-coding genes are regulated by miRs, and 1881 high-confidence miRs are encoded in the human genome. Evidence suggests that miRs not only are synthesized endogenously, but also might be obtained from dietary sources, and that food compounds alter the expression of endogenous miR genes. The main food matrices for studies of biological activity of dietary miRs include plant foods and cow milk. Encapsulation of miRs in exosomes and exosome-like particles confers protection against RNA degradation and creates a pathway for intestinal and vascular endothelial transport by endocytosis, as well as delivery to peripheral tissues. Evidence suggests that the amount of miRs absorbed from nutritionally relevant quantities of foods is sufficient to elicit biological effects, and that endogenous synthesis of miRs is insufficient to compensate for dietary miR depletion and rescue wild-type phenotypes. In addition, nutrition alters the expression of endogenous miR genes, thereby compounding the effects of nutrition-miR interactions in gene regulation and disease diagnosis in liquid biopsies. For example, food components and dietary preferences may modulate serum miR profiles that may influence biological processes. The complex crosstalk between nutrition, miRs, and gene targets poses a challenge to gene network analysis and studies of human disease. Novel pipelines and databases have been developed recently, including a dietary miR database for archiving reported miRs in 15 dietary resources. miRs derived from diet and endogenous synthesis have been implicated in physiologic and pathologic conditions, including those linked with nutrition and metabolism. In fact, several miRs are actively regulated in response to overnutrition and tissue inflammation, and are involved in facilitating the development of chronic inflammation by modulating tissue-infiltrated immune cell function.

Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

Non-coding RNA profiling of the developing murine lens

Non-coding RNAs (ncRNAs) are emerging as an important player in the regulation of genome integrity and gene expression, and they have been implicated in the pathogenesis of many diseases. The aim of the present study is to identify the repertoire of ncRNAs expressed in the developing mouse lens. We previously reported the mouse lens transcriptome, including mRNA and microRNA (miRNA) profiling at two embryonic (E15 and E18) and four postnatal (P0, P3, P6, and P9) time points. We analyzed the data from small RNA-Seq and mRNA-Seq libraries to investigate the ncRNA profile. Our analysis revealed expression of 12 different classes of ncRNA in the murine lens at six developmental time points. Annotation of small RNA data showed expression of 1,756 antisense ncRNA (asncRNA) in the mouse lens transcriptome. Likewise, we identified 82 P-element-induced wimpy testis (PIWI)-interacting RNA (piRNA), 345 transfer RNA (tRNA), 12 small nuclear RNA (snRNA), 167 small nucleolar RNA (snoRNA), 19 small Cajal body-specific RNA (scaRNA), six ribosomal RNA (rRNA), 18 tRNA-like structures, one MALAT1-associated small cytoplasmic RNA (mascRNA), one Vault RNA (vtRNA), and one Y RNA expressed in the developing mouse lens. In parallel, bioinformatic investigation of mRNA-Seq data identified expression of 1,952 long intergenic ncRNA (lincRNA) in the developing mouse lens. In conclusion, we report a comprehensive ncRNA profile in the murine lens at six developmental time points. To the best of our knowledge, this is first report investigating different classes of ncRNAs in the developing mouse lens and will be monumental in elucidating processes essential for the development of the ocular lens and the maintenance of its transparency.

miFRame: analysis and visualization of miRNA sequencing data in neurological disorders

Background

While in the past decades nucleic acid analysis has been predominantly carried out using quantitative low- and high-throughput approaches such as qRT-PCR and microarray technology, next-generation sequencing (NGS) with its single base resolution is now frequently applied in DNA and RNA testing. Especially for small non-coding RNAs such as microRNAs there is a need for analysis and visualization tools that facilitate interpretation of the results also for clinicians.

Methods

We developed miFRame, which supports the analysis of human small RNA NGS data. Our tool carries out different data analyses for known as well as predicted novel mature microRNAs from known precursors and presents the results in a well interpretable manner. Analyses include among others expression analysis of precursors and mature miRNAs, detection of novel precursors and detection of potential iso-microRNAs. Aggregation of results from different users moreover allows for evaluation whether remarkable results, such as novel mature miRNAs, are indeed specific for the respective experimental set-up or are frequently detected across a broad range of experiments.

Results

We demonstrate the capabilities of miFRame, which is freely available at http://www.ccb.uni-saarland.de/miframe on two studies, circulating biomarker screening for Multiple Sclerosis (cohort includes clinically isolated syndrome, relapse remitting MS, matched controls) as well as Alzheimer Disease (cohort includes Alzheimer Disease, Mild Cognitive Impairment, matched controls). Here, our tool allowed for an improved biomarker discovery by identifying likely false positive marker candidates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0594-x) contains supplementary material, which is available to authorized users.

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.

sRNAtoolbox: an integrated collection of small RNA research tools

Small RNA research is a rapidly growing field. Apart from microRNAs, which are important regulators of gene expression, other types of functional small RNA molecules have been reported in animals and plants. MicroRNAs are important in host-microbe interactions and parasite microRNAs might modulate the innate immunity of the host. Furthermore, small RNAs can be detected in bodily fluids making them attractive non-invasive biomarker candidates. Given the general broad interest in small RNAs, and in particular microRNAs, a large number of bioinformatics aided analysis types are needed by the scientific community. To facilitate integrated sRNA research, we developed sRNAtoolbox, a set of independent but interconnected tools for expression profiling from high-throughput sequencing data, consensus differential expression, target gene prediction, visual exploration in a genome context as a function of read length, gene list analysis and blast search of unmapped reads. All tools can be used independently or for the exploration and downstream analysis of sRNAbench results. Workflows like the prediction of consensus target genes of parasite microRNAs in the host followed by the detection of enriched pathways can be easily established. The web-interface interconnecting all these tools is available at http://bioinfo5.ugr.es/srnatoolbox

Nonlinear analysis of tRNAs nucleotide sequences by random walks: randomness and order in the primitive informational polymers

In order to test the hypothesis that the nucleotide sequences of the primitive informational polymers might not be chosen randomly and in the attempt to compare among taxa, we propose a comparison of computer-generated random sequences with tRNAs nucleotide sequences present in the bacterial and archaeal genomes, being tRNAs molecules possible "fossils" of the time (billions years ago) in which life arose. Our approach is based on the analysis of sequences of tRNAs described as random walks and the distances from the origin evaluated by the use of nonlinear indexes (largest Lyapunov exponent, entropy, BDS statistic). Six different tRNAs of Bacteria and Archaea (ten Archaea and ten Bacteria, thermophilic and mesophilic ones; n = 120), and computer-generated random sequences (n = 50) were studied. Our data show that tRNAs present indices statistical lower than the ones of computer-generated random data (tRNAs own a more ordered sequence than random ones: Lyapunov, p < 0.01; entropy, p < 0.05; BDS, p < 0.01). The observed deviation from pure randomness should be arisen from some constraints like the secondary structure of this biologic macromolecule and/or from a "frozen" stochastic transition, or even from the possible peculiar origin of tRNA by replication of older proto-RNA. Comparing between taxa, in the species studied, Bacteria present BDS and Base ratio (G+C)/(A+T) indexes statistically lower than in Archaea, together which a 20% of entropy increase. The analysis of a greater number of tRNAs and species will permit to explain if this finding, showing a higher randomness in the bacterial tRNAs sequences, is linked to the different base ratio, to the different environments in which the microorganisms live or to an evolutionary effect.

BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles

Summary: Non-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. However the vast majority of ncRNAs still have no functional annotation. One prominent approach for putative function assignment is clustering of transcripts according to sequence and secondary structure. However sequence information is changed by post-transcriptional modifications, and secondary structure is only a proxy for the true 3D conformation of the RNA polymer. A different type of information that does not suffer from these issues and that can be used for the detection of RNA classes, is the pattern of processing and its traces in small RNA-seq reads data. Here we introduce BlockClust, an efficient approach to detect transcripts with similar processing patterns. We propose a novel way to encode expression profiles in compact discrete structures, which can then be processed using fast graph-kernel techniques. We perform both unsupervised clustering and develop family specific discriminative models; finally we show how the proposed approach is scalable, accurate and robust across different organisms, tissues and cell lines.

Availability: The whole BlockClust galaxy workflow including all tool dependencies is available at http://toolshed.g2.bx.psu.edu/view/rnateam/blockclust_workflow.

Contact:backofen@informatik.uni-freiburg.de; costa@informatik.uni-freiburg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Small non-coding RNA profiling and the role of piRNA pathway genes in the protection of chicken primordial germ cells

BACKGROUND

Genes, RNAs, and proteins play important roles during germline development. However, the functions of non-coding RNAs (ncRNAs) on germline development remain unclear in avian species. Recent high-throughput techniques have identified several classes of ncRNAs, including micro RNAs (miRNAs), small-interfering RNAs (siRNAs), and PIWI-interacting RNAs (piRNAs). These ncRNAs are functionally important in the genome, however, the identification and annotation of ncRNAs in a genome is challenging. The aim of this study was to identify different types of small ncRNAs particularly piRNAs, and the role of piRNA pathway genes in the protection of chicken primordial germ cells (PGCs).

RESULTS

At first, we performed next-generation sequencing to identify ncRNAs in chicken PGCs, and we performed ab initio predictive analysis to identify putative piRNAs in PGCs. Then, we examined the expression of three repetitive sequence-linked piRNAs and 14 genic-transcript-linked piRNAs along with their linked genes using real-time PCR. All piRNAs and their linked genes were highly expressed in PGCs. Subsequently, we knocked down two known piRNA pathway genes of chicken, PIWI-like protein 1 (CIWI) and 2 (CILI), in PGCs using siRNAs. After knockdown of CIWI and CILI, we examined their effects on the expression of six putative piRNA-linked genes and DNA double-strand breakage in PGCs. The knockdown of CIWI and CILI upregulated chicken repetitive 1 (CR1) element and RAP2B, a member of RAS oncogene family, and increased DNA double-strand breakage in PGCs.

CONCLUSIONS

Our results increase the understanding of PGC-expressed piRNAs and the role of piRNA pathway genes in the protection of germ cells.

Search for microRNAs expressed by intracellular bacterial pathogens in infected mammalian cells

MicroRNAs are expressed by all multicellular organisms and play a critical role as post-transcriptional regulators of gene expression. Moreover, different microRNA species are known to influence the progression of a range of different diseases, including cancer and microbial infections. A number of different human viruses also encode microRNAs that can attenuate cellular innate immune responses and promote viral replication, and a fungal pathogen that infects plants has recently been shown to express microRNAs in infected cells that repress host cell immune responses and promote fungal pathogenesis. Here, we have used deep sequencing of total expressed small RNAs, as well as small RNAs associated with the cellular RNA-induced silencing complex RISC, to search for microRNAs that are potentially expressed by intracellular bacterial pathogens and translocated into infected animal cells. In the case of Legionella and Chlamydia and the two mycobacterial species M. smegmatis and M. tuberculosis, we failed to detect any bacterial small RNAs that had the characteristics expected for authentic microRNAs, although large numbers of small RNAs of bacterial origin could be recovered. However, a third mycobacterial species, M. marinum, did express an ∼23-nt small RNA that was bound by RISC and derived from an RNA stem-loop with the characteristics expected for a pre-microRNA. While intracellular expression of this candidate bacterial microRNA was too low to effectively repress target mRNA species in infected cultured cells in vitro, artificial overexpression of this potential bacterial pre-microRNA did result in the efficient repression of a target mRNA. This bacterial small RNA therefore represents the first candidate microRNA of bacterial origin.

Biological and bioinformatical approaches to study crosstalk of long-non-coding RNAs and chromatin-modifying proteins

Long-non-coding RNA (lncRNA) regulates gene expression through transcriptional and epigenetic regulation as well as alternative splicing in the nucleus. In addition, regulation is achieved at the levels of mRNA translation, storage and degradation in the cytoplasm. During recent years, several studies have described the interaction of lncRNAs with enzymes that confer so-called epigenetic modifications, such as DNA methylation, histone modifications and chromatin structure or remodelling. LncRNA interaction with chromatin-modifying enzymes (CME) is an emerging field that confers another layer of complexity in transcriptional regulation. Given that CME-lncRNA interactions have been identified in many biological processes, ranging from development to disease, comprehensive understanding of underlying mechanisms is important to inspire basic and translational research in the future. In this review, we highlight recent findings to extend our understanding about the functional interdependencies between lncRNAs and CMEs that activate or repress gene expression. We focus on recent highlights of molecular and functional roles for CME-lncRNAs and provide an interdisciplinary overview of recent technical and methodological developments that have improved biological and bioinformatical approaches for detection and functional studies of CME-lncRNA interaction.

Analysis of microRNA expression using machine learning

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

Computational prediction of microRNA genes

The computational identification of novel microRNA (miRNA) genes is a challenging task in bioinformatics. Massive amounts of data describing unknown functional RNA transcripts have to be analyzed for putative miRNA candidates with automated computational pipelines. Beyond those miRNAs that meet the classical definition, high-throughput sequencing techniques have revealed additional miRNA-like molecules that are derived by alternative biogenesis pathways. Exhaustive bioinformatics analyses on such data involve statistical issues as well as precise sequence and structure inspection not only of the functional mature part but also of the whole precursor sequence of the putative miRNA. Apart from a considerable amount of species-specific miRNAs, the majority of all those genes are conserved at least among closely related organisms. Some miRNAs, however, can be traced back to very early points in the evolution of eukaryotic species. Thus, the investigation of the conservation of newly found miRNA candidates comprises an important step in the computational annotation of miRNAs.Topics covered in this chapter include a review on the obvious problem of miRNA annotation and family definition, recommended pipelines of computational miRNA annotation or detection, and an overview of current computer tools for the prediction of miRNAs and their limitations. The chapter closes discussing how those bioinformatic approaches address the problem of faithful miRNA prediction and correct annotation.

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/.

Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs

Recent advances in high-throughput sequencing allow researchers to examine the transcriptome in more detail than ever before. Using a method known as high-throughput small RNA-sequencing, we can now profile the expression of small regulatory RNAs such as microRNAs and small interfering RNAs (siRNAs) with a great deal of sensitivity. However, there are many other types of small RNAs (<50nt) present in the cell, including fragments derived from snoRNAs (small nucleolar RNAs), snRNAs (small nuclear RNAs), scRNAs (small cytoplasmic RNAs), tRNAs (transfer RNAs), and transposon-derived RNAs. Here, we present a user's guide for CoRAL (Classification of RNAs by Analysis of Length), a computational method for discriminating between different classes of RNA using high-throughput small RNA-sequencing data. Not only can CoRAL distinguish between RNA classes with high accuracy, but it also uses features that are relevant to small RNA biogenesis pathways. By doing so, CoRAL can give biologists a glimpse into the characteristics of different RNA processing pathways and how these might differ between tissue types, biological conditions, or even different species. CoRAL is available at http://wanglab.pcbi.upenn.edu/coral/.

Persistently adenovirus-infected lymphoid cells express microRNAs derived from the viral VAI and especially VAII RNA

Human adenovirus can establish latent infections in lymphoid tissues in vivo and persistent, infections in cultured lymphoid cell lines. During lytic infection, adenovirus expresses microRNAs (miRNAs) derived from the viral non-coding RNAs VAI and, especially, VAII. Here, we demonstrate that persistently adenovirus-infected human BJAB cells also produce adenovirus-derived miRNAs primarily derived from the viral VAII RNA, which contributes ~2.7% of all RNA-induced silencing complex (RISC)-associated RNAs. However, our data indicate that the 5' end of the predominant VAII-derived viral RNA, and hence its seed sequence, differs from what has been previously reported. Our data demonstrate that adenovirus expresses viral miRNAs in chronically infected lymphoid cells and raise the possibility that these may contribute to the maintenance of the latently adenovirus-infected lymphoid cells previously observed in mucosal-associated lymphoid tissues in vivo.

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.

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

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

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.