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MicroRNAs are important regulatory small RNAs in many eukaryotes. Due to their small size and simple structure, they are readily innovated de novo. Throughout the evolution of animals, the emergence of novel microRNA families traces key morphological innovations. Here, we use a computational approach based on homology search and parsimony-based presence/absence analysis to draw a comprehensive picture of microRNA evolution in 159 animal species. We confirm previous observations regarding bursts of innovations accompanying the three rounds of genome duplications in vertebrate evolution and in the early evolution of placental mammals. With a much better resolution for the invertebrate lineage compared to large-scale studies, we observe additional bursts of innovation, e.g., in Rhabditoidea. More importantly, we see clear evidence that loss of microRNA families is not an uncommon phenomenon. The Enoplea may serve as a second dramatic example beyond the tunicates. The large-scale analysis presented here also highlights several generic technical issues in the analysis of very large gene families that will require further research.

It is now over 10 years since the first avian genome [International Chicken Genome Sequencing Consortium, 2004] and the first complete avian karyotype [Masabanda et al., 2004] were both published; however, until 2014, avian cytogenetics has focused heavily on descriptive studies [e.g. Griffin et al., 2007, 2008; Skinner et al., 2009; Volker et al., 2010] with less attention to its functional relevance. Last year, however, saw 2 landmark efforts in the chromosomal studies of birds: a special issue of Chromosome Research in April and the announcement of recently completed sequences of multiple new avian genomes in Science and the BMC journals (taking the total number sequenced to over 50) in December. Studying the chromosomes of birds is, perhaps for the first time, telling us more about avian biology, function and evolution than it ever has... Conclusions. The most recent advances in avian cytogenetics have culminated in great promise not only for the study of bird karyotypes, but also for providing insight into the mechanisms of chromosome evolution in general. New avenues for investigation include gene regulation; for instance, it will become necessary to map accurately the physical location of polyadenylation and transcription start sites, important reference points that define promoters and post-transcriptional regulation. It will also become possible to sequence full-length transcripts, to allow accurate identification of alternate splicing events and their controlling elements. The ENCODE (Encyclopedia of DNA Elements) project has helped to define functional elements of the human genome, including those aforementioned as well as other chromatin signals, e.g. active chromatin, enhancers, insulators, methylation domains, etc. An effort of agENCODE is underway to include agriculturally important birds such as chicken, turkey, duck, quail, and perhaps ostrich. The study of cytogenetics will be essential here in helping to define higher-order structures in nuclear organization that show regulatory interactions within and between chromosomes. Finally, reconstruction of evolutionary events allows us to study genome organization and function not only in extant but, by extrapolation, in extinct species also. Reconstruction of avian-reptilian ancestral karyotypes will allow us to define chromosomal rearrangements in long-dead species that have captured the public imagination. Here be dragons!

Background snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and γ; and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms. Results Results of the training and test phases of boxes H/ACA and C/D snoRNAs, in both versions of snoReport, are discussed. Validation on real data was performed to evaluate the predictions of snoReport 2.0. Our program was applied to a set of previously annotated sequences, some of them experimentally confirmed, of humans, nematodes, drosophilids, platypus, chickens and leishmania. We significantly improved the predictions for vertebrates, since the training phase used information of these organisms, but H/ACA box snoRNAs identification was improved for the other ones. Conclusion We presented snoReport 2.0, to predict H/ACA box and C/D box snoRNAs, an efficient method to find true positives and avoid false positives in vertebrate organisms. H/ACA box snoRNA classifier showed an F-score of 93 % (an improvement of 10 % regarding the previous version), while C/D box snoRNA classifier, an F-Score of 94 % (improvement of 14 %). Besides, both classifiers exhibited performance measures above 90 %. These results show that snoReport 2.0 avoid false positives and false negatives, allowing to predict snoRNAs with high quality. In the validation phase, snoReport 2.0 predicted 67.43 % of vertebrate organisms for both classes. For Nematodes and Drosophilids, 69 % and 76.67 %, for H/ACA box snoRNAs were predicted, respectively, showing that snoReport 2.0 is good to identify snoRNAs in vertebrates and also H/ACA box snoRNAs in invertebrates organisms. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1345-6) contains supplementary material, which is available to authorized users.

Background Small nucleolar RNAs (snoRNAs) are one of the most ancient families amongst non-protein-coding RNAs. They are ubiquitous in Archaea and Eukarya but absent in bacteria. Their main function is to target chemical modifications of ribosomal RNAs. They fall into two classes, box C/D snoRNAs and box H/ACA snoRNAs, which are clearly distinguished by conserved sequence motifs and the type of chemical modification that they govern. Similarly to microRNAs, snoRNAs appear in distinct families of homologs that affect homologous targets. In animals, snoRNAs and their evolution have been studied in much detail. In plants, however, their evolution has attracted comparably little attention. Results In order to chart the phylogenetic distribution of individual snoRNA families in plants, we applied a sophisticated approach for identifying homologs of known plant snoRNAs across the plant kingdom. In response to the relatively fast evolution of snoRNAs, information on conserved sequence boxes, target sequences, and secondary structure is combined to identify additional snoRNAs. We identified 296 families of snoRNAs in 24 species and traced their evolution throughout the plant kingdom. Many of the plant snoRNA families comprise paralogs. We also found that targets are well-conserved for most snoRNA families. Conclusions The sequence conservation of snoRNAs is sufficient to establish homologies between phyla. The degree of this conservation tapers off, however, between land plants and algae. Plant snoRNAs are frequently organized in highly conserved spatial clusters. As a resource for further investigations we provide carefully curated and annotated alignments for each snoRNA family under investigation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3301-2) contains supplementary material, which is available to authorized users.

U6 small nuclear RNAs are part of the splicing machinery. They exhibit several unique features setting them appart from other snRNAs. Reports of introns in structured non-coding RNAs have been very rare. U6 genes, however, were found to be interrupted by an intron in several Schizosaccharomyces species and in 2 Basidiomycota. We conducted a homology search across 147 currently available fungal genome and identified the U6 genes in all but 2 of them. A detailed comparison of their sequences and predicted secondary structures showed that intron insertion events in the U6 snRNA were much more common in the fungal lineage than previously thought. Their positional distribution across the entire mature snRNA strongly suggests a large number of independent events. All the intron sequences reported here show canonical splice site and branch site motifs indicating that they require the splicesomal pathway for their removal.

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the post-transcriptional processing of other non-coding RNAs (mostly ribosomal RNAs), but have also been implicated in processes ranging from microRNA-dependent gene silencing to alternative splicing. In order to construct an up-to-date catalog of human snoRNAs we have combined data from various databases, de novo prediction and extensive literature review. In total, we list more than 750 curated genomic loci that give rise to snoRNA and snoRNA-like genes. Utilizing small RNA-seq data from the ENCODE project, our study characterizes the plasticity of snoRNA expression identifying both constitutively as well as cell type specific expressed snoRNAs. Especially, the comparison of malignant to non-malignant tissues and cell types shows a dramatic perturbation of the snoRNA expression profile. Finally, we developed a high-throughput variant of the reverse-transcriptase-based method for identifying 2′-O-methyl modifications in RNAs termed RimSeq. Using the data from this and other high-throughput protocols together with previously reported modification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA interaction network. Our current results assign a reliable modification site to 83% of the canonical snoRNAs, leaving only 76 snoRNA sequences as orphan.

The increase of bodyplan complexity in early bilaterian evolution is correlates with the advent and diversification of microRNAs. These small RNAs guide animal development by regulating temporal transitions in gene expression involved in cell fate choices and transitions between pluripotency and differentiation. One of the two known microRNAs whose origins date back before the bilaterian ancestor is mir-100. In Bilateria, it appears stably associated in polycistronic transcripts with let-7 and mir-125, two key regulators of development. In vertebrates, these three microRNA families have expanded to form a complex system of developmental regulators. In this contribution, we disentangle the evolutionary history of the let-7 locus, which was restructured independently in nematodes, platyhelminths, and deuterostomes. The foundation of a second let-7 locus in the common ancestor of vertebrates and urochordates predates the vertebrate-specific genome duplications, which then caused a rapid expansion of the let-7 family.

Small non-protein-coding RNAs (ncRNAs) have systematically been studied in various model organisms from Escherichia coli to Homo sapiens. Here, we analyse the small ncRNA transcriptome from the pathogenic filamentous fungus Aspergillus fumigatus. To that aim, we experimentally screened for ncRNAs, expressed under various growth conditions or during specific developmental stages, by generating a specialized cDNA library from size-selected small RNA species. Our screen revealed 30 novel ncRNA candidates from known ncRNA classes such as small nuclear RNAs (snRNAs) and C/D box-type small nucleolar RNAs (C/D box snoRNAs). Additionally, several candidates for H/ACA box snoRNAs could be predicted by a bioinformatical screen. We also identified 15 candidates for ncRNAs, which could not be assigned to any known ncRNA class. Some of these ncRNA species are developmentally regulated implying a possible novel function in A. fumigatus development. Surprisingly, in addition to full-length tRNAs, we also identified 5′- or 3′-halves of tRNAs, only, which are likely generated by tRNA cleavage within the anti-codon loop. We show that conidiation induces tRNA cleavage resulting in tRNA depletion within conidia. Since conidia represent the resting state of A. fumigatus we propose that conidial tRNA depletion might be a novel mechanism to down-regulate protein synthesis in a filamentous fungus.

Dynamic programming algorithms solve many standard problems of RNA bioinformatics in polynomial time. In this contribution we discuss a series of variations on these standard methods that implement refined biophysical models, such as a restriction of RNA folding to canonical structures, and an extension of structural alignments to an explicit scoring of stacking propensities. Furthermore, we demonstrate that a local structural alignment can be employed for ncRNA gene finding. In this context we discuss scanning variants for folding and alignment algorithms.

Here we present the results of a large-scale bioinformatics annotation of non-coding RNA loci in 48 avian genomes. Our approach uses probabilistic models of hand-curated families from the Rfam database to infer conserved RNA families within each avian genome. We supplement these annotations with predictions from the tRNA annotation tool, tRNAscan-SE and microRNAs from miRBase. We identify 34 lncRNA-associated loci that are conserved between birds and mammals and validate 12 of these in chicken. We report several intriguing cases where a reported mammalian lncRNA, but not its function, is conserved. We also demonstrate extensive conservation of classical ncRNAs (e.g., tRNAs) and more recently discovered ncRNAs (e.g., snoRNAs and miRNAs) in birds. Furthermore, we describe numerous "losses" of several RNA families, and attribute these to either genuine loss, divergence or missing data. In particular, we show that many of these losses are due to the challenges associated with assembling avian microchromosomes. These combined results illustrate the utility of applying homology-based methods for annotating novel vertebrate genomes.

Summary Coilin protein scaffolds Cajal bodies (CBs)—subnuclear compartments enriched in small nuclear RNAs (snRNAs)—and promotes efficient spliceosomal snRNP assembly. The molecular function of coilin, which is intrinsically disordered with no defined motifs, is poorly understood. We use UV crosslinking and immunoprecipitation (iCLIP) to determine whether mammalian coilin binds RNA in vivo and to identify targets. Robust detection of snRNA transcripts correlated with coilin ChIP-seq peaks on snRNA genes, indicating that coilin binding to nascent snRNAs is a site-specific CB nucleator. Surprisingly, several hundred small nucleolar RNAs (snoRNAs) were identified as coilin interactors, including numerous unannotated mouse and human snoRNAs. We show that all classes of snoRNAs concentrate in CBs. Moreover, snoRNAs lacking specific CB retention signals traffic through CBs en route to nucleoli, consistent with the role of CBs in small RNP assembly. Thus, coilin couples snRNA and snoRNA biogenesis, making CBs the cellular hub of small ncRNA metabolism.

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.

Ribosomal and small nuclear RNAs (snRNAs) comprise numerous modified nucleotides. The modification patterns are retained during evolution, making it even possible to project them from yeast onto human. The stringent conservation of modification sites and the slow evolution of rRNAs and snRNAs contradicts the rapid evolution of small nucleolar RNA (snoRNA) sequences. To explain this discrepancy, we investigated the coevolution of snoRNAs and their targeted sites throughout vertebrates. To measure and evaluate the conservation of RNA-RNA interactions, we defined the interaction conservation index (ICI). It combines the quality of individual interaction with the scope of its conservation in a set of species and serves as an efficient measure to evaluate the conservation of the interaction of snoRNA and target. We show that functions of homologous snoRNAs are evolutionarily stable, thus, members of the same snoRNA family guide equivalent modifications. The conservation of s noRNA sequences is high at target binding regions while the remaining sequence varies significantly. In addition to elucidating principles of correlated evolution, we were able, with the help of the ICI measure, to assign functions to previously orphan snoRNAs and to associate snoRNAs as partners to known chemical modifications unassigned to a given snoRNA. Furthermore, we used predictions of snoRNA functions in conjunction with sequence conservation to identify distant homologies. Because of the high overall entropy of snoRNA sequences, such relationships are hard to detect by means of sequence homology search methods alone.

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.

Motivation: Although small nucleolar RNAs form an important class of non-coding RNAs, no comprehensive annotation efforts have been undertaken, presumably because the task is complicated by both the large number of distinct small nucleolar RNA families and their relatively rapid pace of sequence evolution. Results: With snoStrip we present an automatic annotation pipeline developed specifically for comparative genomics of small nucleolar RNAs. It makes use of sequence conservation, canonical box motifs as well as secondary structure and predicts putative targets. Availability and implementation: The snoStrip web service and the download version is available at http://snostrip.bioinf.uni-leipzig.de/ Contact: sebastian@bioinf.uni-leipzig.de

Nat. Genet. 48, 427–437 (2016); published online 7 March 2016; corrected after print 25 April 2016 As we intended, other researchers have been able to use the draft spotted gar genome sequence available from the Broad Institute website since December 2011, the assembly LepOcu1 publicly available from NCBI since 13 January 2012 under accession code GCA000242695.

The avalanche of next generation sequencing data has led to a rapid increase of annotated microRNAs in the last few years. Many of them are specific to individual species or rather narrow clades. A closer inspection of the current version of miRBase shows that dozens of entries conflict with other ncRNAs, in particular snoRNAs.With few exceptions, these cases show little similarities to canonical microRNAs, however, and thus they should be considered as mis-annotations.

Andrea Tanzera,d Markus Riester,a Jana Hertel,a Clara Isabel Bermudez-Santana,a,b Jan Gorodkin,c Ivo L. Hofacker,d Peter F. Stadler,a,d,e, f aBioinformatics Group, Department of Computer Science; and Interdisciplinary Center for Bioinformatics, University of Leipzig, Hartelstrasse 16-18, D-01407, Leipzig, Germany bDepartment of Biology, Universidad Nacional de Colombia, Tr. 38 40-01, Bogota, D.C., Colombia cDivision of Genetics and Bioinformatics, IBHV, University of Copenhagen, Gronnegardsvej 3, DK-1870 Frederiksberg, Denmark d Institute for Theoretical Chemistry, University of Vienna, Wahringerstrase 17, A-1090 Wien, Austria eFraunhofer Institute for Cell Therapy und Immunology, Deutscher Platz 5e, D-04103 Leipzig, Germany f Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA

Ribosomal RNA (rRNA) genes are probably the most frequently used data source in phylogenetic reconstruction. Individual columns of rRNA alignments are not independent as a consequence of their highly conserved secondary structures. Unless explicitly taken into account, these correlation can distort the phylogenetic signal and/or lead to gross overestimates of tree stability. Maximum likelihood and Bayesian approaches are of course amenable to using RNA-specific substitution models that treat conserved base pairs appropriately, but require accurate secondary structure models as input. So far, however, no accurate and easy-to-use tool has been available for computing structure-aware alignments and consensus structures that can deal with the large rRNAs. The RNAsalsa approach is designed to fill this gap. Capitalizing on the improved accuracy of pairwise consensus structures and informed by a priori knowledge of group-specific structural constraints, the tool provides both alignments and consensus structures that are of sufficient accuracy for routine phylogenetic analysis based on RNA-specific substitution models. The power of the approach is demonstrated using two rRNA data sets: a mitochondrial rRNA set of 26 Mammalia, and a collection of 28S nuclear rRNAs representative of the five major echinoderm groups.

A detailed annotation of non-protein coding RNAs is typically missing in initial releases of newly sequenced genomes. Here we report on a comprehensive ncRNA annotation of the genome of Trichoplax adhaerens, the presumably most basal metazoan whose genome has been published to-date. Since blast identified only a small fraction of the best-conserved ncRNAs--in particular rRNAs, tRNAs and some snRNAs--we developed a semi-global dynamic programming tool, GotohScan, to increase the sensitivity of the homology search. It successfully identified the full complement of major and minor spliceosomal snRNAs, the genes for RNase P and MRP RNAs, the SRP RNA, as well as several small nucleolar RNAs. We did not find any microRNA candidates homologous to known eumetazoan sequences. Interestingly, most ncRNAs, including the pol-III transcripts, appear as single-copy genes or with very small copy numbers in the Trichoplax genome.

Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target. This sequence complementarity to known targets crucially contributes to sensitivity and specificity of snoRNA gene finding algorithms. The discovery of ‘orphan’ snoRNAs, which either have no known target, or which target ordinary protein-coding mRNAs, however, begs the question whether this class of ‘housekeeping’ non-coding RNAs is much more widespread and might have a diverse set of regulatory functions. In order to approach this question, we present here a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences. The snoReport approach deliberately avoids any usage of target information. We find that the combination of the conserved sequence boxes and secondary structure constraints as a pre-filter with SVM classifiers based on a small set of structural descriptors are sufficient for a reliable identification of snoRNAs. Tests of snoReport on data from several recent experimental surveys show that the approach is feasible; the application to a dataset from a large-scale comparative genomics survey for ncRNAs suggests that there are likely hundreds of previously undescribed ‘orphan’ snoRNAs still hidden in the human genome.

Summary: Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target. This sequence complementarity to known targets crucially contributes to sensitivity and specificity of snoRNA gene finding algorithms. The discovery of ‘orphan’ snoRNAs, which either have no known target, or which target ordinary protein-coding mRNAs, however, begs the question whether this class of ‘housekeeping’ non-coding RNAs is much more widespread and might have a diverse set of regulatory functions. In order to approach this question, we present here a combination of RNA secondary structure prediction and machine learning that is designed to recognize the two major classes of snoRNAs, box C/D and box H/ACA snoRNAs, among ncRNA candidate sequences. The snoReport approach deliberately avoids any usage of target information. We find that the combination of the conserved sequence boxes and secondary structure constraints as a pre-filter with SVM classifiers based on a small set of structural descriptors are sufficient for a reliable identification of snoRNAs. Tests of snoReport on data from several recent experimental surveys show that the approach is feasible; the application to a dataset from a large-scale comparative genomics survey for ncRNAs suggests that there are likely hundreds of previously undescribed ‘orphan’ snoRNAs still hidden in the human genome. Availability: The snoReport software is implemented in ANSI C. The source code is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/snoReport. Supplementary Material is available at http://www.bioinf.uni-leipzig.de/Publications/SUPPLEMENTS/07-015 Contact: jana@bioinf.uni-leipzig.de Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide multiple sequence alignments (MSAs) are a necessary prerequisite for an increasingly diverse collection of comparative genomic approaches. Here we present a versatile method that generates high-quality MSAs for non-protein-coding sequences. The NcDNAlign pipeline combines pairwise BLAST alignments to create initial MSAs, which are then locally improved and trimmed. The program is optimized for speed and hence is particulary well-suited to pilot studies. We demonstrate the practical use of NcDNAlign in three case studies: the search for ncRNAs in gammaproteobacteria and the analysis of conserved noncoding DNA in nematodes and teleost fish, in the latter case focusing on the fate of duplicated ultra-conserved regions. Compared to the currently widely used genome-wide alignment program TBA, our program results in a 20- to 30-fold reduction of CPU time necessary to generate gammaproteobacterial alignments. A showcase application of bacterial ncRNA prediction based on alignments of both algorithms results in similar sensitivity, false discovery rates, and up to 100 putatively novel ncRNA structures. Similar findings hold for our application of NcDNAlign to the identification of ultra-conserved regions in nematodes and teleosts. Both approaches yield conserved sequences of unknown function, result in novel evolutionary insights into conservation patterns among these genomes, and manifest the benefits of an efficient and reliable genome-wide alignment package. The software is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/NcDNAlign/.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view about chromatin structure has emerged, including its interrelationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded novel mechanistic and evolutionary insights about the functional landscape of the human genome. Together, these studies are defining a path forward to pursue a more-comprehensive characterisation of human genome function.

Functional RNA structures play an important role both in the context of noncoding RNA transcripts as well as regulatory elements in mRNAs. Here we present a computational study to detect functional RNA structures within the ENCODE regions of the human genome. Since structural RNAs in general lack characteristic signals in primary sequence, comparative approaches evaluating evolutionary conservation of structures are most promising. We have used three recently introduced programs based on either phylogenetic-stochastic context-free grammar (EvoFold) or energy directed folding (RNAz and AlifoldZ), yielding several thousand candidate structures (corresponding to approximately 2.7% of the ENCODE regions). EvoFold has its highest sensitivity in highly conserved and relatively AU-rich regions, while RNAz favors slightly GC-rich regions, resulting in a relatively small overlap between methods. Comparison with the GENCODE annotation points to functional RNAs in all genomic contexts, with a slightly increased density in 3'-UTRs. While we estimate a significant false discovery rate of approximately 50%-70% many of the predictions can be further substantiated by additional criteria: 248 loci are predicted by both RNAz and EvoFold, and an additional 239 RNAz or EvoFold predictions are supported by the (more stringent) AlifoldZ algorithm. Five hundred seventy RNAz structure predictions fall into regions that show signs of selection pressure also on the sequence level (i.e., conserved elements). More than 700 predictions overlap with noncoding transcripts detected by oligonucleotide tiling arrays. One hundred seventy-five selected candidates were tested by RT-PCR in six tissues, and expression could be verified in 43 cases (24.6%).

Significant fractions of eukaryotic genomes give rise to RNA, much of which is unannotated and has reduced protein-coding potential. The genomic origins and the associations of human nuclear and cytosolic polyadenylated RNAs longer than 200 nucleotides (nt) and whole-cell RNAs less than 200 nt were investigated in this genome-wide study. Subcellular addresses for nucleotides present in detected RNAs were assigned, and their potential processing into short RNAs was investigated. Taken together, these observations suggest a novel role for some unannotated RNAs as primary transcripts for the production of short RNAs. Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes. These data support a highly interleaved organization of the human transcriptome.

Background Recent experimental and computational studies have provided overwhelming evidence for a plethora of diverse transcripts that are unrelated to protein-coding genes. One subclass consists of those RNAs that require distinctive secondary structure motifs to exert their biological function and hence exhibit distinctive patterns of sequence conservation characteristic for positive selection on RNA secondary structure. The deep-sequencing of 12 drosophilid species coordinated by the NHGRI provides an ideal data set of comparative computational approaches to determine those genomic loci that code for evolutionarily conserved RNA motifs. This class of loci includes the majority of the known small ncRNAs as well as structured RNA motifs in mRNAs. We report here on a genome-wide survey using RNAz. Results We obtain 16 000 high quality predictions among which we recover the majority of the known ncRNAs. Taking a pessimistically estimated false discovery rate of 40% into account, this implies that at least some ten thousand loci in the Drosophila genome show the hallmarks of stabilizing selection action of RNA structure, and hence are most likely functional at the RNA level. A subset of RNAz predictions overlapping with TRF1 and BRF binding sites [Isogai et al., EMBO J. 26: 79–89 (2007)], which are plausible candidates of Pol III transcripts, have been studied in more detail. Among these sequences we identify several "clusters" of ncRNA candidates with striking structural similarities. Conclusion The statistical evaluation of the RNAz predictions in comparison with a similar analysis of vertebrate genomes [Washietl et al., Nat. Biotech. 23: 1383–1390 (2005)] shows that qualitatively similar fractions of structured RNAs are found in introns, UTRs, and intergenic regions. The intergenic RNA structures, however, are concentrated much more closely around known protein-coding loci, suggesting that flies have significantly smaller complement of independent structured ncRNAs compared to mammals.

Starting with the discovery of microRNAs and the advent of genome-wide transcriptomics, non-protein-coding transcripts have moved from a fringe topic to a central field research in molecular biology. In this contribution we review the state of the art of "computational RNomics", i.e., the bioinformatics approaches to genome-wide RNA annotation. Instead of rehashing results from recently published surveys in detail, we focus here on the open problem in the field, namely (functional) annotation of the plethora of putative RNAs. A series of exploratory studies are used to provide non-trivial examples for the discussion of some of the difficulties.

Summary: Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes. Availability: The program RNAmicro is available as source code and can be downloaded from Contact:Jana Hertel, Tel: ++49 341 97 16704, Fax: ++49 341 97 16709, {jana,studla}@bioinf.uni-leipzig.de

Background MicroRNAs have been identified as crucial regulators in both animals and plants. Here we report on a comprehensive comparative study of all known miRNA families in animals. We expand the MicroRNA Registry 6.0 by more than 1000 new homologs of miRNA precursors whose expression has been verified in at least one species. Using this uniform data basis we analyze their evolutionary history in terms of individual gene phylogenies and in terms of preservation of genomic nearness across species. This allows us to reliably identify microRNA clusters that are derived from a common transcript. Results We identify three episodes of microRNA innovation that correspond to major developmental innovations: A class of about 20 miRNAs is common to protostomes and deuterostomes and might be related to the advent of bilaterians. A second large wave of innovations maps to the branch leading to the vertebrates. The third significant outburst of miRNA innovation coincides with placental (eutherian) mammals. In addition, we observe the expected expansion of the microRNA inventory due to genome duplications in early vertebrates and in an ancestral teleost. The non-local duplications in the vertebrate ancestor are predated by local (tandem) duplications leading to the formation of about a dozen ancient microRNA clusters. Conclusion Our results suggest that microRNA innovation is an ongoing process. Major expansions of the metazoan miRNA repertoire coincide with the advent of bilaterians, vertebrates, and (placental) mammals.

Summary The phylogeny of insects, one of the most spectacular radiations of life on earth, has received considerable attention [1–3]. However, the evolutionary roots of one intriguing group of insects, the twisted-wing parasites (Strepsiptera), remain unclear despite centuries of study and debate [1, 2, 4–11]. Strepsiptera exhibit exceptional larval developmental features, consistent with a predicted step from direct (hemimetabolous) larval development to complete metamorphosis that could have set the stage for the spectacular radiation of metamorphic (holometabolous) insects [1, 12, 13]. Here we report the sequencing of a Strepsiptera genome and show that the analysis of sequence-based genomic data (comprising more than 18 million nucleotides from nearly 4,500 genes obtained from a total of 13 insect genomes), along with genomic metacharacters, clarifies the phylogenetic origin of Strepsiptera and sheds light on the evolution of holometabolous insect development. Our results provide overwhelming support for Strepsiptera as the closest living relatives of beetles (Coleoptera). They demonstrate that the larval developmental features of Strepsiptera, reminiscent of those of hemimetabolous insects, are the result of convergence. Our analyses solve the long-standing enigma of the evolutionary roots of Strepsiptera and reveal that the holometabolous mode of insect development is more malleable than previously thought.

Motivation: Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many ‘orphan’ snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms. Results: RNAsnoop implements a dynamic programming algorithm that computes thermodynamically optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based machine learning approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative information, it presents an efficient and reliable tool for the prediction of H/ACA snoRNA target sites. We apply RNAsnoop to identify the snoRNAs that are responsible for several of the remaining ‘orphan’ pseudouridine modifications in human rRNAs, and we assign a target to one of the five orphan H/ACA snoRNAs in Drosophila. Availability: The C source code of RNAsnoop is freely available at http://www.tbi.univie.ac.at/∼htafer/RNAsnoop Contact: htafer@tbi.univie.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.