A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

Hidden messages in the nef gene of human immunodeficiency virus type 1 suggest a novel RNA secondary structure

The coexistence of multiple codes in the genome of human immunodeficiency virus type 1 (HIV-1) was analyzed. We explored factors constraining the variability of the virus genome primarily in relation to conserved RNA secondary structures overlapping coding sequences, and used a simple combination of algorithms for RNA secondary structure prediction based on the nearest-neighbor thermodynamic rules and a statistical approach. In our previous study, we applied this combination to a non- redundant data set of env nucleotide sequences, confirmed the conservative secondary structure of the rev-responsive element (RRE) and found a new RNA structure in the first conserved (C1) region of the env gene. In this study, we analyzed the variability of putative RNA secondary structures inside the nef gene of HIV-1 by applying these algorithms to a non-redundant data set of 104 nef sequences retrieved from the Los Alamos HIV database, and predicted the existence of a novel functional RNA secondary structure in the β3/β4 regions of nef. The predicted RNA fold in the β3/β4 region of nef appears in two forms with different loop sizes. The loop of the first fold consists of seven nucleotides (positions 494–500), with consensus UCAAGCU appearing in 79% of sequences. The other has a five-base loop (positions 495–499) with consensus CAAGC. The difference in size between these two loops may reflect the difference between respective counterparts in the hairpin recognition. This may also have an adaptive biological significance.     

Full-length RNA structure prediction of the HIV-1 genome reveals a conserved core domain

A distance constrained secondary structural model of the ≈10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus.     

Genomic analysis of codon,sequence and structural conservation with selective biochemical-structure mapping reveals highly conserved and dynamic structures in rotavirus RNAs with potential cis-acting functions

Rotaviruses are a major cause of acute, often fatal, gastroenteritis in infants and young children world-wide. Virions contain an 11 segment double-stranded RNA genome. Little is known about the cis-acting sequences and structural elements of the viral RNAs. Using a database of 1621 full-length sequences of mammalian group A rotavirus RNA segments, we evaluated the codon, sequence and RNA structural conservation of the complete genome. Codon conservation regions were found in eight ORFs, suggesting the presence of functional RNA elements. Using ConStruct and RNAz programmes, we identified conserved secondary structures in the positive-sense RNAs including long-range interactions (LRIs) at the 5′ and 3′ terminal regions of all segments. In RNA9, two mutually exclusive structures were observed suggesting a switch mechanism between a conserved terminal LRI and an independent 3′ stem–loop structure. In RNA6, a conserved stem–loop was found in a region previously reported to have translation enhancement activity. Biochemical structural analysis of RNA11 confirmed the presence of terminal LRIs and two internal helices with high codon and sequence conservation. These extensive in silico and in vitro analyses provide evidence of the conservation, complexity, multi-functionality and dynamics of rotavirus RNA structures which likely influence RNA replication, translation and genome packaging.     

Genome-Wide Double-Stranded RNA Sequencing Reveals the Functional Significance of Base-Paired RNAs in Arabidopsis

The functional structure of all biologically active molecules is dependent on intra- and inter-molecular interactions. This is especially evident for RNA molecules whose functionality, maturation, and regulation require formation of correct secondary structure through encoded base-pairing interactions. Unfortunately, intra- and inter-molecular base-pairing information is lacking for most RNAs. Here, we marry classical nuclease-based structure mapping techniques with high-throughput sequencing technology to interrogate all base-paired RNA in Arabidopsis thaliana and identify ∼200 new small (sm)RNA–producing substrates of RNA–DEPENDENT RNA POLYMERASE6. Our comprehensive analysis of paired RNAs reveals conserved functionality within introns and both 5′ and 3′ untranslated regions (UTRs) of mRNAs, as well as a novel population of functional RNAs, many of which are the precursors of smRNAs. Finally, we identify intra-molecular base-pairing interactions to produce a genome-wide collection of RNA secondary structure models. Although our methodology reveals the pairing status of RNA molecules in the absence of cellular proteins, previous studies have demonstrated that structural information obtained for RNAs in solution accurately reflects their structure in ribonucleoprotein complexes. Furthermore, our identification of RNA–DEPENDENT RNA POLYMERASE6 substrates and conserved functional RNA domains within introns and both 5′ and 3′ untranslated regions (UTRs) of mRNAs using this approach strongly suggests that RNA molecules are correctly folded into their secondary structure in solution. Overall, our findings highlight the importance of base-paired RNAs in eukaryotes and present an approach that should be widely applicable for the analysis of this key structural feature of RNA.     

FLORA: A Novel Method to Predict Protein Function from Structure in Diverse Superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues.     

Novel cis-Acting Element within the Capsid-Coding Region Enhances Flavivirus Viral-RNA Replication by Regulating Genome Cyclization

cis-Acting elements in the viral genome RNA (vRNA) are essential for the translation, replication, and/or encapsidation of RNA viruses. In this study, a novel conserved cis-acting element was identified in the capsid-coding region of mosquito-borne flavivirus. The downstream of 5′ cyclization sequence (5′CS) pseudoknot (DCS-PK) element has a three-stem pseudoknot structure, as demonstrated by structure prediction and biochemical analysis. Using dengue virus as a model, we show that DCS-PK enhances vRNA replication and that its function depends on its secondary structure and specific primary sequence. Mutagenesis revealed that the highly conserved stem 1 and loop 2, which are involved in potential loop-helix interactions, are crucial for DCS-PK function. A predicted loop 1-stem 3 base triple interaction is important for the structural stability and function of DCS-PK. Moreover, the function of DCS-PK depends on its position relative to the 5′CS, and the presence of DCS-PK facilitates the formation of 5′-3′ RNA complexes. Taken together, our results reveal that the cis-acting element DCS-PK enhances vRNA replication by regulating genome cyclization, and DCS-PK might interplay with other cis-acting elements to form a functional vRNA cyclization domain, thus playing critical roles during the flavivirus life cycle and evolution.     

RNA-dependent RNA polymerase of Japanese encephalitis virus binds the initiator nucleotide GTP to form a mechanistically important pre-initiation state

Flaviviral RNA-dependent RNA polymerases (RdRps) initiate replication of the single-stranded RNA genome in the absence of a primer. The template sequence 5′-CU-3′ at the 3′-end of the flaviviral genome is highly conserved. Surprisingly, flaviviral RdRps require high concentrations of the second incoming nucleotide GTP to catalyze de novo template-dependent RNA synthesis. We show that GTP stimulates de novo RNA synthesis by RdRp from Japanese encephalitis virus (jRdRp) also. Crystal structures of jRdRp complexed with GTP and ATP provide a basis for specific recognition of GTP. Comparison of the jRdRp_GTP structure with other viral RdRp-GTP structures shows that GTP binds jRdRp in a novel conformation. Apo-jRdRp structure suggests that the conserved motif F of jRdRp occupies multiple conformations in absence of GTP. Motif F becomes ordered on GTP binding and occludes the nucleotide triphosphate entry tunnel. Mutational analysis of key residues that interact with GTP evinces that the jRdRp_GTP structure represents a novel pre-initiation state. Also, binding studies show that GTP binding reduces affinity of RdRp for RNA, but the presence of the catalytic Mn²⁺ ion abolishes this inhibition. Collectively, these observations suggest that the observed pre-initiation state may serve as a checkpoint to prevent erroneous template-independent RNA synthesis by jRdRp during initiation.     

Discovery and analysis of evolutionarily conserved intronic splicing regulatory elements

Knowledge of the functional cis-regulatory elements that regulate constitutive and alternative pre-mRNA splicing is fundamental for biology and medicine. Here we undertook a genome-wide comparative genomics approach using available mammalian genomes to identify conserved intronic splicing regulatory elements (ISREs). Our approach yielded 314 ISREs, and insertions of ~70 ISREs between competing splice sites demonstrated that 84% of ISREs altered 5′ and 94% altered 3′ splice site choice in human cells. Consistent with our experiments, comparisons of ISREs to known splicing regulatory elements revealed that 40%–45% of ISREs might have dual roles as exonic splicing silencers. Supporting a role for ISREs in alternative splicing, we found that 30%–50% of ISREs were enriched near alternatively spliced (AS) exons, and included almost all known binding sites of tissue-specific alternative splicing factors. Further, we observed that genes harboring ISRE-proximal exons have biases for tissue expression and molecular functions that are ISRE-specific. Finally, we discovered that for Nova1, neuronal PTB, hnRNP C, and FOX1, the most frequently occurring ISRE proximal to an alternative conserved exon in the splicing factor strongly resembled its own known RNA binding site, suggesting a novel application of ISRE density and the propensity for splicing factors to auto-regulate to associate RNA binding sites to splicing factors. Our results demonstrate that ISREs are crucial building blocks in understanding general and tissue-specific AS regulation and the biological pathways and functions regulated by these AS events.     

Improving the prediction of RNA secondary structure by detecting and assessing conserved stems

The prediction of RNA secondary structure can be facilitated by incorporating with comparative analysis of homologous sequences. However, most of existing comparative methods are vulnerable to alignment errors and thus are of low accuracy in practical application. Here we improve the prediction of RNA secondary structure by detecting and assessing conserved stems shared by all sequences in the alignment. Our method can be summarized by: 1) we detect possible stems in single RNA sequence using the so-called position matrix with which some possibly paired positions can be uncovered; 2) we detect conserved stems across multiple RNA sequences by multiplying the position matrices; 3) we assess the conserved stems using the Signal-to-Noise; 4) we compute the optimized secondary structure by incorporating the so-called reliable conserved stems with predictions by RNAalifold program. We tested our method on data sets of RNA alignments with known secondary structures. The accuracy, measured as sensitivity and specificity, of our method is greater than predictions by RNAalifold.     

Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus

The existence and functional importance of RNA secondary structure in the replication of positive-stranded RNA viruses is increasingly recognized. We applied several computational methods to detect RNA secondary structure in the coding region of hepatitis C virus (HCV), including thermodynamic prediction, calculation of free energy on folding, and a newly developed method to scan sequences for covariant sites and associated secondary structures using a parsimony-based algorithm. Each of the prediction methods provided evidence for complex RNA folding in the core- and NS5B-encoding regions of the genome. The positioning of covariant sites and associated predicted stem-loop structures coincided with thermodynamic predictions of RNA base pairing, and localized precisely in parts of the genome with marked suppression of variability at synonymous sites. Combined, there was evidence for a total of six evolutionarily conserved stem-loop structures in the NS5B-encoding region and two in the core gene. The virus most closely related to HCV, GB virus-B (GBV-B) also showed evidence for similar internal base pairing in its coding region, although predictions of secondary structures were limited by the absence of comparative sequence data for this virus. While the role(s) of stem-loops in the coding region of HCV and GBV-B are currently unknown, the structure predictions in this study could provide the starting point for functional investigations using recently developed self-replicating clones of HCV.     

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.     

Discovery of 17 conserved structural RNAs in fungi

Many non-coding RNAs with known functions are structurally conserved: their intramolecular secondary and tertiary interactions are maintained across evolutionary time. Consequently, the presence of conserved structure in multiple sequence alignments can be used to identify candidate functional non-coding RNAs. Here, we present a bioinformatics method that couples iterative homology search with covariation analysis to assess whether a genomic region has evidence of conserved RNA structure. We used this method to examine all unannotated regions of five well-studied fungal genomes (Saccharomyces cerevisiae, Candida albicans, Neurospora crassa, Aspergillus fumigatus, and Schizosaccharomyces pombe). We identified 17 novel structurally conserved non-coding RNA candidates, which include four H/ACA box small nucleolar RNAs, four intergenic RNAs and nine RNA structures located within the introns and untranslated regions (UTRs) of mRNAs. For the two structures in the 3′ UTRs of the metabolic genes GLY1 and MET13, we performed experiments that provide evidence against them being eukaryotic riboswitches.     

Secondary Structures for 5′ Regions of R2 Retrotransposon RNAs Reveal a Novel Conserved Pseudoknot and Regions that Evolve under Different Constraints

Sequences from the 5′ region of R2 retrotransposons of four species of silk moth are reported. In Bombyx mori, this region of the R2 messenger RNA contains a binding site for R2 protein and mediates interactions critical to R2 element insertion into the host genome. A model of secondary structure for a segment of this RNA is proposed on the basis of binding to oligonucleotide microarrays, chemical mapping, and comparative sequence analysis. Five conserved secondary structures are identified, including a novel pseudoknot. There is an apparent transition from an entirely RNA structure coding function in most of the 5′ segment to a protein coding function near the 3′ end. This suggests that local regions evolved under separate functional constraints (structural, coding, or both).     

Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond

Single-stranded regions in RNA secondary structure are important for RNA–RNA and RNA–protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals ‘well-determined’ single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit β-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA–RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.     

Consensus folding of aligned sequences as a new measure for the detection of functional RNAs by comparative genomics

Facing the ever-growing list of newly discovered classes of functional RNAs, it can be expected that further types of functional RNAs are still hidden in recently completed genomes. The computational identification of such RNA genes is, therefore, of major importance. While most known functional RNAs have characteristic secondary structures, their free energies are generally not statistically significant enough to distinguish RNA genes from the genomic background. Additional information is required. Considering the wide availability of new genomic data of closely related species, comparative studies seem to be the most promising approach. Here, we show that prediction of consensus structures of aligned sequences can be a significant measure to detect functional RNAs. We report a new method to test multiple sequence alignments for the existence of an unusually structured and conserved fold. We show for alignments of six types of well-known functional RNA that an energy score consisting of free energy and a covariation term significantly improves sensitivity compared to single sequence predictions. We further test our method on a number of non-coding RNAs from Caenorhabditis elegans/Caenorhabditis briggsae and seven Saccharomyces species. Most RNAs can be detected with high significance. We provide a Perl implementation that can be used readily to score single alignments and discuss how the methods described here can be extended to allow for efficient genome-wide screens.     

Detecting riboSNitches with RNA folding algorithms: a genome-wide benchmark

Ribonucleic acid (RNA) secondary structure prediction continues to be a significant challenge, in particular when attempting to model sequences with less rigidly defined structures, such as messenger and non-coding RNAs. Crucial to interpreting RNA structures as they pertain to individual phenotypes is the ability to detect RNAs with large structural disparities caused by a single nucleotide variant (SNV) or riboSNitches. A recently published human genome-wide parallel analysis of RNA structure (PARS) study identified a large number of riboSNitches as well as non-riboSNitches, providing an unprecedented set of RNA sequences against which to benchmark structure prediction algorithms. Here we evaluate 11 different RNA folding algorithms’ riboSNitch prediction performance on these data. We find that recent algorithms designed specifically to predict the effects of SNVs on RNA structure, in particular remuRNA, RNAsnp and SNPfold, perform best on the most rigorously validated subsets of the benchmark data. In addition, our benchmark indicates that general structure prediction algorithms (e.g. RNAfold and RNAstructure) have overall better performance if base pairing probabilities are considered rather than minimum free energy calculations. Although overall aggregate algorithmic performance on the full set of riboSNitches is relatively low, significant improvement is possible if the highest confidence predictions are evaluated independently.