miRNATissueAtlas2: an update to the human miRNA tissue atlas.

Small non-coding RNAs (sncRNAs) are pervasive regulators of physiological and pathological processes. We previously developed the human miRNA Tissue Atlas, detailing the expression of miRNAs across organs in the human body. Here, we present an updated resource containing sequencing data of 188 tissue samples comprising 21 organ types retrieved from six humans. Sampling the organs from the same bodies minimizes intra-individual variability and facilitates the making of a precise high-resolution body map of the non-coding transcriptome. The data allow shedding light on the organ- and organ system-specificity of piwi-interacting RNAs (piRNAs), transfer RNAs (tRNAs), microRNAs (miRNAs) and other non-coding RNAs. As use case of our resource, we describe the identification of highly specific ncRNAs in different organs. The update also contains 58 samples from six tissues of the Tabula Muris collection, allowing to check if the tissue specificity is evolutionary conserved between Homo sapiens and Mus musculus. The updated resource of 87 252 non-coding RNAs from nine non-coding RNA classes for all organs and organ systems is available online without any restrictions (https://www.ccb.uni-saarland.de/tissueatlas2).

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer.

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.

Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA.

The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 111 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new snoRNA and tRNA-like genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distorts the perceived architecture of the human transcriptome.

Levels of sdRNAs in cytoplasm and their association with ribosomes are dependent upon stress conditions but independent from snoRNA expression.

In recent years, a number of small RNA molecules derived from snoRNAs have been observed. Findings concerning the functions of snoRNA-derived small RNAs (sdRNAs) in cells are limited primarily to their involvement in microRNA pathways. However, similar molecules have been observed in Saccharomyces cerevisiae, which is an organism lacking miRNA machinery. Here we examined the subcellular localization of sdRNAs in yeast. Our findings reveal that both sdRNAs and their precursors, snoRNAs, are present in the cytoplasm at levels dependent upon stress conditions. Moreover, both sdRNAs and snoRNAs may interact with translating ribosomes in a stress-dependent manner. Likely consequential to their ribosome association and protein synthesis suppression features, yeast sdRNAs may exert inhibitory activity on translation. Observed levels of sdRNAs and snoRNAs in the cytoplasm and their apparent presence in the ribosomal fractions suggest independent regulation of these molecules by yet unknown factors.

RNA-Seq employing a novel rRNA depletion strategy reveals a rich repertoire of snoRNAs in Euglena gracilis including box C/D and Ψ-guide RNAs targeting the modification of rRNA extremities.

Previous mRNA transcriptome studies of Euglena gracilis have shown that this organism possesses a large and diverse complement of protein coding genes; however, the study of non-coding RNA classes has been limited. The natural extensive fragmentation of the E. gracilis large subunit ribosomal RNA presents additional barriers to the identification of non-coding RNAs as size-selected small RNA libraries will be dominated by rRNA sequences. In this study we have developed a strategy to significantly reduce rRNA amplification prior to RNA-Seq analysis thereby producing a ncRNA library allowing for the identification of many new E. gracilis small RNAs. Library analysis reveals 113 unique new small nucleolar (sno) RNAs and a large collection of snoRNA isoforms, as well as the first significant collection of nuclear tRNAs in this organism. A 3' end AGAUGN consensus motif and conserved structural features can now be defined for E. gracilis pseudouridine guide RNAs. snoRNAs of both classes were identified that target modification of the 3' extremities of rRNAs utilizing predicted base-pairing interactions with internally transcribed spacers (ITS), providing insight into the timing of steps in rRNA maturation. Cumulatively, this represents the most comprehensive analysis of small ncRNAs in Euglena gracilis to date.

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Motivation: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites. Results: We propose a novel application of CNNs to classification of pairwise alignments of sequences for accurate clustering of sequences and show the benefits of the CNN method of inputting pairwise alignments for clustering of non-coding RNA (ncRNA) sequences and for motif discovery. Classification of a pairwise alignment of two sequences into positive and negative classes corresponds to the clustering of the input sequences. After we combined the distributed representation of RNA nucleotides with the secondary-structure information specific to ncRNAs and furthermore with mapping profiles of next-generation sequence reads, the training of CNNs for classification of alignments of RNA sequences yielded accurate clustering in terms of ncRNA families and outperformed the existing clustering methods for ncRNA sequences. Several interesting sequence motifs and secondary-structure motifs known for the snoRNA family and specific to microRNA and tRNA families were identified. Availability and implementation: The source code of our CNN software in the deep-learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/cnn/, and the dataset used for performance evaluation in this work is available at the same URL.

Methylation guide RNA evolution in archaea: structure, function and genomic organization of 110 C/D box sRNA families across six Pyrobaculum species.

Archaeal homologs of eukaryotic C/D box small nucleolar RNAs (C/D box sRNAs) guide precise 2'-O-methyl modification of ribosomal and transfer RNAs. Although C/D box sRNA genes constitute one of the largest RNA gene families in archaeal thermophiles, most genomes have incomplete sRNA gene annotation because reliable, fully automated detection methods are not available. We expanded and curated a comprehensive gene set across six species of the crenarchaeal genus Pyrobaculum, particularly rich in C/D box sRNA genes. Using high-throughput small RNA sequencing, specialized computational searches and comparative genomics, we analyzed 526 Pyrobaculum C/D box sRNAs, organizing them into 110 families based on synteny and conservation of guide sequences which determine methylation targets. We examined gene duplications and rearrangements, including one family that has expanded in a pattern similar to retrotransposed repetitive elements in eukaryotes. New training data and inclusion of kink-turn secondary structural features enabled creation of an improved search model. Our analyses provide the most comprehensive, dynamic view of C/D box sRNA evolutionary history within a genus, in terms of modification function, feature plasticity, and gene mobility.

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.

Automated detection of ncRNAs in the draft genome sequence of a colonial tunicate: the carpet sea squirt Didemnum vexillum.

BACKGROUND: The colonial ascidian Didemnum vexillum, sea carpet squirt, is not only a key marine organism to study morphological ancestral patterns of chordates evolution but it is also of great ecological importance due to its status as a major invasive species. Non-coding RNAs, in particular microRNAs (miRNAs), are important regulatory genes that impact development and environmental adaptation. Beyond miRNAs, not much in known about tunicate ncRNAs. RESULTS: We provide here a comprehensive homology-based annotation of non-coding RNAs in the recently sequenced genome of D. vexillum. To this end we employed a combination of several computational approaches, including blast searches with a wide range of parameters, and secondary structured centered survey with infernal. The resulting candidate set was curated extensively to produce a high-quality ncRNA annotation of the first draft of the D. vexillum genome. It comprises 57 miRNA families, 4 families of ribosomal RNAs, 22 isoacceptor classes of tRNAs (of which more than 72 % of loci are pseudogenes), 13 snRNAs, 12 snoRNAs, and 1 other RNA family. Additionally, 21 families of mitochondrial tRNAs and 2 of mitochondrial ribosomal RNAs and 1 long non-coding RNA. CONCLUSIONS: The comprehensive annotation of the D. vexillum non-coding RNAs provides a starting point towards a better understanding of the restructuring of the small RNA system in ascidians. Furthermore it provides a valuable research for efforts to establish detailed non-coding RNA annotations for other recently published and recently sequences in tunicate genomes.

Non-coding RNAs: Functions and applications in endocrine-related cancer.

A significant fraction of the human genome is transcribed as non-coding RNAs (ncRNAs). This non-coding transcriptome has challenged the notion of the central dogma and its involvement in transcriptional and post-transcriptional regulation of gene expression is well established. Interestingly, several ncRNAs are dysregulated in cancer and current non-coding transcriptome research aims to use our increasing knowledge of these ncRNAs for the development of cancer biomarkers and anti-cancer drugs. In endocrine-related cancers, for which survival rates can be relatively low, there is a need for such advancements. In this review, we aimed to summarize the roles and clinical implications of recently discovered ncRNAs, including long ncRNAs, PIWI-interacting RNAs, tRNA- and Y RNA-derived ncRNAs, and small nucleolar RNAs, in endocrine-related cancers affecting both sexes. We focus on recent studies highlighting discoveries in ncRNA biology and expression in cancer, and conclude with a discussion on the challenges and future directions, including clinical application. ncRNAs show great promise as diagnostic tools and therapeutic targets, but further work is necessary to realize the potential of these unconventional transcripts.

Identification of discrete classes of small nucleolar RNA featuring different ends and RNA binding protein dependency.

Small nucleolar RNAs (snoRNAs) are among the first discovered and most extensively studied group of small non-coding RNA. However, most studies focused on a small subset of snoRNAs that guide the modification of ribosomal RNA. In this study, we annotated the expression pattern of all box C/D snoRNAs in normal and cancer cell lines independent of their functions. The results indicate that C/D snoRNAs are expressed as two distinct forms differing in their ends with respect to boxes C and D and in their terminal stem length. Both forms are overexpressed in cancer cell lines but display a conserved end distribution. Surprisingly, the long forms are more dependent than the short forms on the expression of the core snoRNP protein NOP58, thought to be essential for C/D snoRNA production. In contrast, a subset of short forms are dependent on the splicing factor RBFOX2. Analysis of the potential secondary structure of both forms indicates that the k-turn motif required for binding of NOP58 is less stable in short forms which are thus less likely to mature into a canonical snoRNP. Taken together the data suggest that C/D snoRNAs are divided into at least two groups with distinct maturation and functional preferences.

snOPY: a small nucleolar RNA orthological gene database.

BACKGROUND: Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the modification of specific nucleotides in ribosomal RNAs (rRNAs) and small nuclear RNAs (snRNAs). Although most non-coding RNAs undergo post-transcriptional modifications prior to maturation, the functional significance of these modifications remains unknown. Here, we introduce the snoRNA orthological gene database (snOPY) as a tool for studying RNA modifications. FINDINGS: snOPY provides comprehensive information about snoRNAs, snoRNA gene loci, and target RNAs. It also contains data for orthologues from various species, which enables users to analyze the evolution of snoRNA genes. In total, 13,770 snoRNA genes, 10,345 snoRNA gene loci, and 133 target RNAs have been registered. Users can search and access the data efficiently using a simple web interface with a series of internal links. snOPY is freely available on the web at http://snoopy.med.miyazaki-u.ac.jp. CONCLUSIONS: snOPY is the database that provides information about the small nucleolar RNAs and their orthologues. It will help users to study RNA modifications and snoRNA gene evolution.

Generation of a mouse model with down-regulated U50 snoRNA (SNORD50) expression and its organ-specific phenotypic modulation.

Box C/D-type small nucleolar RNAs (snoRNAs) are functional RNAs responsible for mediating 2'-O-ribose methylation of ribosomal RNAs (rRNAs) within the nucleolus. In the past years, evidence for the involvement of human U50 snoRNA in tumorigenesis has been accumulating. We previously identified U50HG, a non-protein-coding gene that hosted a box C/D-type U50 snoRNA, in a chromosomal breakpoint in a human B-cell lymphoma. Mouse genome analysis revealed four mouse U50 (mU50) host-genes: three mU50HG-a gene variants that were clustered in the genome and an mU50HG-b gene that we supposed to be the U50HG ortholog. In this study, to investigate the physiological importance of mU50 snoRNA and its involvement in tumorigenesis, we eliminated mU50 snoRNA sequences from the mU50HG-b gene. The established mouse line (ΔmU50(HG-b)) showed a significant reduction of mU50 snoRNA expression without alteration of the host-gene length and exon-intron structure, and the corresponding target rRNA methylation in various organs was reduced. Lifelong phenotypic monitoring showed that the ΔmU50(HG-b) mice looked almost normal without accelerated tumorigenicity; however, a notable difference was the propensity for anomalies in the lymphoid organs. Transcriptome analysis showed that dozens of genes, including heat shock proteins, were differentially expressed in ΔmU50(HG-b) mouse lymphocytes. This unique model of a single snoRNA knockdown with intact host-gene expression revealed further new insights into the discrete transcriptional regulation of multiple mU50 host-genes and the complicated dynamics involved in organ-specific processing and maintenance of snoRNAs.

Computational prediction and validation of C/D, H/ACA and Eh_U3 snoRNAs of Entamoeba histolytica.

BACKGROUND: Small nucleolar RNAs are a highly conserved group of small RNAs found in eukaryotic cells. Genes encoding these RNAs are diversely located throughout the genome. They are functionally conserved, performing post transcriptional modification (methylation and pseudouridylation) of rRNA and other nuclear RNAs. They belong to two major categories: the C/D box and H/ACA box containing snoRNAs. U3 snoRNA is an exceptional member of C/D box snoRNAs and is involved in early processing of pre-rRNA. An antisense sequence is present in each snoRNA which guides the modification or processing of target RNA. However, some snoRNAs lack this sequence and often they are called orphan snoRNAs. RESULTS: We have searched snoRNAs of Entamoeba histolytica from the genome sequence using computational programmes (snoscan and snoSeeker) and we obtained 99 snoRNAs (C/D and H/ACA box snoRNAs) along with 5 copies of Eh_U3 snoRNAs. These are located diversely in the genome, mostly in intergenic regions, while some are found in ORFs of protein coding genes, intron and UTRs. The computationally predicted snoRNAs were validated by RT-PCR and northern blotting. The expected sizes were in agreement with the observed sizes for all C/D box snoRNAs tested, while for some of the H/ACA box there was indication of processing to generate shorter products. CONCLUSION: Our results showed the presence of snoRNAs in E. histolytica, an early branching eukaryote, and the structural features of E. histolytica snoRNAs were well conserved when compared with yeast and human snoRNAs. This study will help in understanding the evolution of these conserved RNAs in diverse phylogenetic groups.

Experimental identification and analysis of macronuclear non-coding RNAs from the ciliate Tetrahymena thermophila.

The ciliate Tetrahymena thermophila is an important eukaryotic model organism that has been used in pioneering studies of general phenomena, such as ribozymes, telomeres, chromatin structure and genome reorganization. Recent work has shown that Tetrahymena has many classes of small RNA molecules expressed during vegetative growth or sexual reorganization. In order to get an overview of medium-sized (40-500 nt) RNAs expressed from the Tetrahymena genome, we created a size-fractionated cDNA library from macronuclear RNA and analyzed 80 RNAs, most of which were previously unknown. The most abundant class was small nucleolar RNAs (snoRNAs), many of which are formed by an unusual maturation pathway. The modifications guided by the snoRNAs were analyzed bioinformatically and experimentally and many Tetrahymena-specific modifications were found, including several in an essential, but not conserved domain of ribosomal RNA. Of particular interest, we detected two methylations in the 5'-end of U6 small nuclear RNA (snRNA) that has an unusual structure in Tetrahymena. Further, we found a candidate for the first U8 outside metazoans, and an unusual U14 candidate. In addition, a number of candidates for new non-coding RNAs were characterized by expression analysis at different growth conditions.

Features of a unique intronless cluster of class I small heat shock protein genes in tandem with box C/D snoRNA genes on chromosome 6 in tomato (Solanum lycopersicum).

Physical clustering of genes has been shown in plants; however, little is known about gene clusters that have different functions, particularly those expressed in the tomato fruit. A class I 17.6 small heat shock protein (Sl17.6 shsp) gene was cloned and used as a probe to screen a tomato (Solanum lycopersicum) genomic library. An 8.3-kb genomic fragment was isolated and its DNA sequence determined. Analysis of the genomic fragment identified intronless open reading frames of three class I shsp genes (Sl17.6, Sl20.0, and Sl20.1), the Sl17.6 gene flanked by Sl20.1 and Sl20.0, with complete 5' and 3' UTRs. Upstream of the Sl20.0 shsp, and within the shsp gene cluster, resides a box C/D snoRNA cluster made of SlsnoR12.1 and SlU24a. Characteristic C and D, and C' and D', boxes are conserved in SlsnoR12.1 and SlU24a while the upstream flanking region of SlsnoR12.1 carries TATA box 1, homol-E and homol-D box-like cis sequences, TM6 promoter, and an uncharacterized tomato EST. Molecular phylogenetic analysis revealed that this particular arrangement of shsps is conserved in tomato genome but is distinct from other species. The intronless genomic sequence is decorated with cis elements previously shown to be responsive to cues from plant hormones, dehydration, cold, heat, and MYC/MYB and WRKY71 transcription factors. Chromosomal mapping localized the tomato genomic sequence on the short arm of chromosome 6 in the introgression line (IL) 6-3. Quantitative polymerase chain reaction analysis of gene cluster members revealed differential expression during ripening of tomato fruit, and relatively different abundances in other plant parts.

Animal snoRNAs and scaRNAs with exceptional structures.

The overwhelming majority of small nucleolar RNAs (snoRNAs) fall into two clearly defined classes characterized by distinctive secondary structures and sequence motifs. A small group of diverse ncRNAs, however, shares the hallmarks of one or both classes of snoRNAs but differs substantially from the norm in some respects. Here, we compile the available information on these exceptional cases, conduct a thorough homology search throughout the available metazoan genomes, provide improved and expanded alignments, and investigate the evolutionary histories of these ncRNA families as well as their mutual relationships.

SnoPatrol: how many snoRNA genes are there?

Small nucleolar RNAs (snoRNAs) are among the most evolutionarily ancient classes of small RNA. Two experimental screens published in BMC Genomics expand the eukaryotic snoRNA catalog, but many more snoRNAs remain to be found.

Identification and classification of small RNAs in transcriptome sequence data.

Current methods for high throughput sequencing (HTS) for the first time offer the opportunity to investigate the entire transcriptome in an essentially unbiased way. In many species, small non-coding RNAs with specific secondary structures constitute a significant part of the transcriptome. Some of these RNA classes, in particular microRNAs and snoRNAs, undergo maturation processes that lead to the production of shorter RNAs. After mapping the sequences to the reference genome specific patterns of short reads can be observed. These read patterns seem to reflect the processing and thus are specific for the RNA transcripts of which they are derived from. We explore here the potential of short read sequence data in the classification and identification of non-coding RNAs.

Computational prediction of Caenorhabditis box H/ACA snoRNAs using genomic properties of their host genes.

Identification of small nucleolar RNAs (snoRNAs) in genomic sequences has been challenging due to the relative paucity of sequence features. Many current prediction algorithms rely on detection of snoRNA motifs complementary to target sites in snRNAs and rRNAs. However, recent discovery of snoRNAs without apparent targets requires development of alternative prediction methods. We present an approach that combines rule-based filters and a Bayesian Classifier to identify a class of snoRNAs (H/ACA) without requiring target sequence information. It takes advantage of unique attributes of their genomic organization and improved species-specific motif characterization to predict snoRNAs that may otherwise be difficult to discover. Searches in the genomes of Caenorhabditis elegans and the closely related Caenorhabditis briggsae suggest that our method performs well compared to recent benchmark algorithms. Our results illustrate the benefits of training gene discovery engines on features restricted to particular phylogenetic groups and the utility of incorporating diverse data types in gene prediction.