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Background: Structured non-coding RNAs play many different roles in the cells, but the annotation of these RNAs is lacking even within the human genome. The currently available computational tools are either too computationally heavy for use in full genomic screens or rely on pre-aligned sequences., Methods: Here we present a fast and efficient method, DotcodeR, for detecting structurally similar RNAs in genomic sequences by comparing their corresponding coarse-grained secondary structure dot plots at string level. This allows us to perform an all-against-all scan of all window pairs from two genomes without alignment., Results: Our computational experiments with simulated data and real chromosomes demonstrate that the presented method has good sensitivity., Conclusions: DotcodeR can be useful as a pre-filter in a genomic comparative scan for structured RNAs.

Structured elements of RNA molecules are essential in, e.g., RNA stabilization, localization, and protein interaction, and their conservation across species suggests a common functional role. We computationally screened vertebrate genomes for conserved RNA structures (CRSs), leveraging structure-based, rather than sequence-based, alignments. After careful correction for sequence identity and GC content, we predict ∼516,000 human genomic regions containing CRSs. We find that a substantial fraction of human-mouse CRS regions (1) colocalize consistently with binding sites of the same RNA binding proteins (RBPs) or (2) are transcribed in corresponding tissues. Additionally, a CaptureSeq experiment revealed expression of many of our CRS regions in human fetal brain, including 662 novel ones. For selected human and mouse candidate pairs, qRT-PCR and in vitro RNA structure probing supported both shared expression and shared structure despite low abundance and low sequence identity. About 30,000 CRS regions are located near coding or long noncoding RNA genes or within enhancers. Structured (CRS overlapping) enhancer RNAs and extended 3' ends have significantly increased expression levels over their nonstructured counterparts. Our findings of transcribed uncharacterized regulatory regions that contain CRSs support their RNA-mediated functionality., (© 2017 Seemann et al.; Published by Cold Spring Harbor Laboratory Press.)

Simultaneous alignment and secondary structure prediction of RNA sequences is often referred to as "RNA structural alignment." A class of the methods for structural alignment is based on the principles proposed by Sankoff more than 25 years ago. The Sankoff algorithm simultaneously folds and aligns two or more sequences. The advantage of this algorithm over those that separate the folding and alignment steps is that it makes better predictions. The disadvantage is that it is slower and requires more computer memory to run. The amount of computational resources needed to run the Sankoff algorithm is so high that it took more than a decade before the first implementation of a Sankoff style algorithm was published. However, with the faster computers available today and the improved heuristics used in the implementations the Sankoff-based methods have become practical. This chapter describes the methods based on the Sankoff algorithm. All the practical implementations of the algorithm use heuristics to make them run in reasonable time and memory. These heuristics are also described in this chapter.

De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.

This unit describes how to use Foldalign and FoldalignM to make structural alignments of non-protein-coding-RNA (ncRNA). These tools can be used to find new ncRNAs, to find the structure of novel ncRNAs, and to improve alignments for known ncRNAs.

Background: Post-transcriptional control of gene expression is mostly conducted by specific elements in untranslated regions (UTRs) of mRNAs, in collaboration with specific binding proteins and RNAs. In several well characterized cases, these RNA elements are known to form stable secondary structures. RNA secondary structures also may have major functional implications for long noncoding RNAs (lncRNAs). Recent transcriptional data has indicated the importance of lncRNAs in brain development and function. However, no methodical efforts to investigate this have been undertaken. Here, we aim to systematically analyze the potential for RNA structure in brain-expressed transcripts., Results: By comprehensive spatial expression analysis of the adult mouse in situ hybridization data of the Allen Mouse Brain Atlas, we show that transcripts (coding as well as non-coding) associated with in silico predicted structured probes are highly and significantly enriched in almost all analyzed brain regions. Functional implications of these RNA structures and their role in the brain are discussed in detail along with specific examples. We observe that mRNAs with a structure prediction in their UTRs are enriched for binding, transport and localization gene ontology categories. In addition, after manual examination we observe agreement between RNA binding protein interaction sites near the 3' UTR structures and correlated expression patterns., Conclusions: Our results show a potential use for RNA structures in expressed coding as well as noncoding transcripts in the adult mouse brain, and describe the role of structured RNAs in the context of intracellular signaling pathways and regulatory networks. Based on this data we hypothesize that RNA structure is widely involved in transcriptional and translational regulatory mechanisms in the brain and ultimately plays a role in brain function.

Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

Growing recognition of the numerous, diverse and important roles played by non-coding RNA in all organisms motivates better elucidation of these cellular components. Comparative genomics is a powerful tool for this task and is arguably preferable to any high-throughput experimental technology currently available, because evolutionary conservation highlights functionally important regions. Conserved secondary structure, rather than primary sequence, is the hallmark of many functionally important RNAs, because compensatory substitutions in base-paired regions preserve structure. Unfortunately, such substitutions also obscure sequence identity and confound alignment algorithms, which complicates analysis greatly. This paper surveys recent computational advances in this difficult arena, which have enabled genome-scale prediction of cross-species conserved RNA elements. These predictions suggest that a wealth of these elements indeed exist.

User-driven in silico RNA homology search is still a nontrivial task. In part, this is the consequence of a limited precision of the computational tools in spite of recent exciting progress in this area, and to a certain extent, computational costs are still problematic in practice. An important, and as we argue here, dominating issue is the dependence on good curated (secondary) structural alignments of the RNAs. These are often hard to obtain, not so much because of an inherent limitation in the available data, but because they require substantial manual curation, an effort that is rarely acknowledged. Here, we qualitatively describe a realistic scenario for what a "regular user" (i.e., a nonexpert in a particular RNA family) can do in practice, and what kind of results are likely to be achieved. Despite the indisputable advances in computational RNA biology, the conclusion is discouraging: BLAST still works better or equally good as other methods unless extensive expert knowledge on the RNA family is included. However, when good curated data are available the recent development yields further improvements in finding remote homologs. Homology search beyond the reach of BLAST hence is not at all a routine task.

Recent computational scans for non-coding RNAs (ncRNAs) in multiple organisms have relied on existing multiple sequence alignments. However, as sequence similarity drops, a key signal of RNA structure--frequent compensating base changes--is increasingly likely to cause sequence-based alignment methods to misalign, or even refuse to align, homologous ncRNAs, consequently obscuring that structural signal. We have used CMfinder, a structure-oriented local alignment tool, to search the ENCODE regions of vertebrate multiple alignments. In agreement with other studies, we find a large number of potential RNA structures in the ENCODE regions. We report 6587 candidate regions with an estimated false-positive rate of 50%. More intriguingly, many of these candidates may be better represented by alignments taking the RNA secondary structure into account than those based on primary sequence alone, often quite dramatically. For example, approximately one-quarter of our predicted motifs show revisions in >50% of their aligned positions. Furthermore, our results are strongly complementary to those discovered by sequence-alignment-based approaches--84% of our candidates are not covered by Washietl et al., increasing the number of ncRNA candidates in the ENCODE region by 32%. In a group of 11 ncRNA candidates that were tested by RT-PCR, 10 were confirmed to be present as RNA transcripts in human tissue, and most show evidence of significant differential expression across tissues. Our results broadly suggest caution in any analysis relying on multiple sequence alignments in less well-conserved regions, clearly support growing appreciation for the biological significance of ncRNAs, and strongly support the argument for considering RNA structure directly in any searches for these elements.

Background: Non-coding RNAs (ncRNAs) are involved in a wide spectrum of regulatory functions. Within recent years, there have been increasing reports of observed polyadenylated ncRNAs and mRNA like ncRNAs in eukaryotes. To investigate this further, we examined the large data set in the Sino-Danish PigEST resource http://pigest.ku.dk which also contains expression information distributed on 97 non-normalized cDNA libraries., Results: We constructed a pipeline, EST2ncRNA, to search for known and novel ncRNAs. The pipeline utilises sequence similarity to ncRNA databases (blast), structure similarity to Rfam (RaveNnA) as well as multiple alignments to predict conserved novel putative RNA structures (RNAz). EST2ncRNA was fed with 48,000 contigs and 73,000 singletons available from the PigEST resource. Using the pipeline we identified known RNA structures in 137 contigs and single reads (conreads), and predicted high confidence RNA structures in non-protein coding regions of additional 1,262 conreads. Of these, structures in 270 conreads overlap with existing predictions in human. To sum up, the PigEST resource comprises trans-acting elements (ncRNAs) in 715 contigs and 340 singletons as well as cis-acting elements (inside UTRs) in 311 contigs and 51 singletons, of which 18 conreads contain both predictions of trans- and cis-acting elements. The predicted RNAz candidates were compared with the PigEST expression information and we identify 114 contigs with an RNAz prediction and expression in at least ten of the non-normalised cDNA libraries. We conclude that the contigs with RNAz and known predictions are in general expressed at a much lower level than protein coding transcripts. In addition, we also observe that our ncRNA candidates constitute about one to two percent of the genes expressed in the cDNA libraries. Intriguingly, the cDNA libraries from developmental (brain) tissues contain the highest amount of ncRNA candidates, about two percent. These observations are related to existing knowledge and hypotheses about the role of ncRNAs in higher organisms. Furthermore, about 80% porcine coding transcripts (of 18,600 identified) as well as less than one-third ORF-free transcripts are conserved at least in the closely related bovine genome. Approximately one percent of the coding and 10% of the remaining matches are unique between the PigEST data and cow genome. Based on the pig-cow alignments, we searched for similarities to 16 other organisms by UCSC available alignments, which resulted in a 87% coverage by the human genome for instance., Conclusion: Besides recovering several of the already annotated functional RNA structures, we predicted a large number of high confidence conserved secondary structures in polyadenylated porcine transcripts. Our observations of relatively low expression levels of predicted ncRNA candidates together with the observations of higher relative amount in cDNA libraries from developmental stages are in agreement with the current paradigm of ncRNA roles in higher organisms and supports the idea of polyadenylated ncRNAs.

Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type of approach. Determining these rates can be hard to do reliably without a large and accurate initial alignment, which ideally also has structural annotation. Hence, one must often apply rates extracted from other RNA families with trusted alignments and structures. Here, we investigate this problem by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions. In addition we obtained an alignment of the 5' HIV-1 region that is more consistent with the structure than that currently in the database. We added randomized noise to the original values of the rates to investigate the stability of predictions to rate matrix deviations. We find that changes within a fairly large range still produce reliable predictions and conclude that using rates from a limited set of RNA sequences is valid over a broader range of sequences.

The untranslated leader of the dimeric HIV-1 RNA genome is folded into a complex structure that plays multiple and essential roles in the viral replication cycle. Here, we have investigated secondary and tertiary structural elements within the 5' 744 nucleotides of the HIV-1 genome using a combination of bioinformatics, enzymatic probing, native gel electrophoresis, and UV-crosslinking experiments. We used a recently developed RNA folding algorithm (Pfold) to predict the common secondary structure of an alignment of 20 divergent HIV-1 sequences. Combining this analysis with biochemical data, we present a secondary structure model for the entire 744 nucleotide fragment, which incorporates previously recognized and novel structural elements. In particular, our data provided strong evidence for a long-distance interaction between the region encompassing the AUG Gag initiation codon and an upstream region and we demonstrate that this feature is highly conserved in distantly related human and animal retroviruses. To obtain information about tertiary interactions we applied an intramolecular UV-crosslinking strategy and identified a novel tertiary interaction within the PBS hairpin structure.

Post-transcriptional regulation of gene expression is often accomplished by proteins binding to specific sequence motifs in mRNA molecules, to affect their translation or stability. The motifs are often composed of a combination of sequence and structural constraints such that the overall structure is preserved even though much of the primary sequence is variable. While several methods exist to discover transcriptional regulatory sites in the DNA sequences of coregulated genes, the RNA motif discovery problem is much more difficult because of covariation in the positions. We describe the combined use of two approaches for RNA structure prediction, FOLDALIGN and COVE, that together can discover and model stem-loop RNA motifs in unaligned sequences, such as UTRs from post-transcriptionally coregulated genes. We evaluate the method on two datasets, one a section of rRNA genes with randomly truncated ends so that a global alignment is not possible, and the other a hyper-variable collection of IRE-like elements that were inserted into randomized UTR sequences. In both cases the combined method identified the motifs correctly, and in the rRNA example we show that it is capable of determining the structure, which includes bulge and internal loops as well as a variable length hairpin loop. Those automated results are quantitatively evaluated and found to agree closely with structures contained in curated databases, with correlation coefficients up to 0.9. A basic server, Stem-Loop Align SearcH (SLASH), which will perform stem-loop searches in unaligned RNA sequences, is available at http://www.bioinf.au.dk/slash/.