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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Genome-wide searching with base-pairing kernel functions for noncoding RNAs: computational and expression analysis of snoRNA families in Caenorhabditis elegans.

Despite the accumulating research on noncoding RNAs (ncRNAs), it is likely that we are seeing only the tip of the iceberg regarding our understanding of the functions and the regulatory roles served by ncRNAs in cellular metabolism, pathogenesis and host-pathogen interactions. Therefore, more powerful computational and experimental tools for analyzing ncRNAs need to be developed. To this end, we propose novel kernel functions, called base-pairing profile local alignment (BPLA) kernels, for analyzing functional ncRNA sequences using support vector machines (SVMs). We extend the local alignment kernels for amino acid sequences in order to handle RNA sequences by using STRAL's; scoring function, which takes into account sequence similarities as well as upstream and downstream base-pairing probabilities, thus enabling us to model secondary structures of RNA sequences. As a test of the performance of BPLA kernels, we applied our kernels to the problem of discriminating members of an RNA family from nonmembers using SVMs. The results indicated that the discrimination ability of our kernels is stronger than that of other existing methods. Furthermore, we demonstrated the applicability of our kernels to the problem of genome-wide search of snoRNA families in the Caenorhabditis elegans genome, and confirmed that the expression is valid in 14 out of 48 of our predicted candidates by using qRT-PCR. Finally, highly expressed six candidates were identified as the original target regions by DNA sequencing.

SnoRNAs from the filamentous fungus Neurospora crassa: structural, functional and evolutionary insights.

BACKGROUND: SnoRNAs represent an excellent model for studying the structural and functional evolution of small non-coding RNAs involved in the post-transcriptional modification machinery for rRNAs and snRNAs in eukaryotic cells. Identification of snoRNAs from Neurospora crassa, an important model organism playing key roles in the development of modern genetics, biochemistry and molecular biology will provide insights into the evolution of snoRNA genes in the fungus kingdom. RESULTS: Fifty five box C/D snoRNAs were identified and predicted to guide 71 2'-O-methylated sites including four sites on snRNAs and three sites on tRNAs. Additionally, twenty box H/ACA snoRNAs, which potentially guide 17 pseudouridylations on rRNAs, were also identified. Although not exhaustive, the study provides the first comprehensive list of two major families of snoRNAs from the filamentous fungus N. crassa. The independently transcribed strategy dominates in the expression of box H/ACA snoRNA genes, whereas most of the box C/D snoRNA genes are intron-encoded. This shows that different genomic organizations and expression modes have been adopted by the two major classes of snoRNA genes in N. crassa . Remarkably, five gene clusters represent an outstanding organization of box C/D snoRNA genes, which are well conserved among yeasts and multicellular fungi, implying their functional importance for the fungus cells. Interestingly, alternative splicing events were found in the expression of two polycistronic snoRNA gene hosts that resemble the UHG-like genes in mammals. Phylogenetic analysis further revealed that the extensive separation and recombination of two functional elements of snoRNA genes has occurred during fungus evolution. CONCLUSION: This is the first genome-wide analysis of the filamentous fungus N. crassa snoRNAs that aids in understanding the differences between unicellular fungi and multicellular fungi. As compared with two yeasts, a more complex pattern of methylation guided by box C/D snoRNAs in multicellular fungus than in unicellular yeasts was revealed, indicating the high diversity of post-transcriptional modification guided by snoRNAs in the fungus kingdom.

Families of H/ACA ncRNA molecules in trypanosomatids.

H/ACA molecules are small nucleolar RNAs (snoRNAs) that guide the conversion of a uridine into a pseudouridine. This modification is crucial for ribosomal RNA (rRNA) function. In addition, H/ACA RNA also function in ribosomal RNA processing. Unlike the double hairpin structure of H/ACA molecules in other organisms, H/ACA molecules in Trypanosomes have a single hairpin structure. Here we describe the repertoire of the 46 published H/ACA molecules in Trypanosoma brucei, as well as identify the orthologous counterparts in other Trypanosomatids enlarging the H/ACA collection in this family to over 300.

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.

Evolution of small nucleolar RNAs in nematodes.

In contrast to mRNAs, which are templates for translating proteins, non-protein coding (npc) RNAs (also known as 'non-coding' RNA, ncRNA), exhibit various functions in different compartments and developmental stages of the cell. Small nucleolar RNAs (snoRNAs), one of the largest classes of npcRNAs, guide post-transcriptional modifications of other RNAs that are crucial for appropriate RNA folding as well as for RNA-RNA and RNA-protein interactions. Although snoRNA genes comprise a significant fraction of the eutherian genome, identifying and characterizing large numbers of them is not sufficiently accessible by classical computer searches alone. Furthermore, most previous investigations of snoRNAs yielded only limited indications of their evolution. Using data obtained by a combination of high-throughput cDNA library screening and computational search strategies based on a modified DNAMAN program, we characterized 151 npcRNAs, and in particular 121 snoRNAs, from Caenorhabditis elegans and extensively compared them with those in the related, Caenorhabditis briggsae. Detailed comparisons of paralog snoRNAs in the two nematodes revealed, in addition to trans-duplication, a novel, cis-duplication distribution strategy with insertions near to the original loci. Some snoRNAs coevolved with their modification target sites, demonstrating the close interaction of complementary regions. Some target sites modified by snoRNAs were changed, added or lost, documenting a high degree of evolutionary plasticity of npcRNAs.

[Small nucleolar RNAs].

Small nucleolar RNAs (snoRNAs) are an abundant class of non-protein-coding RNAs. In association with proteins they perform two most frequent nucleotide modifications in rRNAs and some other cellular RNAs: 2'-O-ribose methylation and pseudouridylation. SnoRNAs also participate in pre-rRNA cleavage and telomerase functions. Most snoRNAs fall into two families, box C/D and H/ACA, distinguished by the presence of conserved sequence boxes. Although C/D and H/ACA snoRNP proteins contain homologous regions, the assembly of these RNPs significantly differ. In addition, snoRNAs include the RNA component of RNAses P and MRP. The structure and function of small RNPs from Cajal bodies (small organelles associated with nucleoli) similar to snoRNP are also discussed.