Rfam Database Research Articles

PRNA

Promoter-associated RNAs (pRNAs) are a family of ~90–100 nt-long divergent RNAs overlapping the promoter of the rRNA (rDNA) operon. pRNA transcripts interact with TIP5, a component of the chromatin remodeling complex NoRC, which recruits enzymes for heterochromatin formation and mediates silencing of rRNA genes. Here we present a comprehensive analysis of pRNA homologs, including different versions per species, as result of in silico studies in available metazoan genome assemblies. Comparative sequence analysis and secondary structure prediction ended up in two possible secondary structures, which let us assume a possible dual function of pRNAs for regulation of rRNA operons. Furthermore, we validated parts of our computational predictions experimentally by RT-PCR and sequencing. A representative seed alignment of the pRNA family, annotated with possible secondary structures was released to the Rfam database.

Determination of sRNA Expressions by RNA-seq in Yersinia pestis Grown In Vitro and during Infection

BackgroundSmall non-coding RNAs (sRNAs) facilitate host-microbe interactions. They have a central function in the post-transcriptional regulation during pathogenic lifestyles. Hfq, an RNA-binding protein that many sRNAs act in conjunction with, is required for Y. pestis pathogenesis. However, information on how Yersinia pestis modulates the expression of sRNAs during infection is largely unknown.Methodology and Principal FindingsWe used RNA-seq technology to identify the sRNA candidates expressed from Y. pestis grown in vitro and in the infected lungs of mice. A total of 104 sRNAs were found, including 26 previously annotated sRNAs, by searching against the Rfam database with 78 novel sRNA candidates. Approximately 89% (93/104) of these sRNAs from Y. pestis are shared with its ancestor Y. pseudotuberculosis. Ninety-seven percent of these sRNAs (101/104) are shared among more than 80 sequenced genomes of 135 Y. pestis strains. These 78 novel sRNAs include 62 intergenic and 16 antisense sRNAs. Fourteen sRNAs were selected for verification by independent Northern blot analysis. Results showed that nine selected sRNA transcripts were Hfq-dependent. Interestingly, three novel sRNAs were identified as new members of the transcription factor CRP regulon. Semi-quantitative analysis revealed that Y. pestis from the infected lungs induced the expressions of six sRNAs including RyhB1, RyhB2, CyaR/RyeE, 6S RNA, RybB and sR039 and repressed the expressions of four sRNAs, including CsrB, CsrC, 4.5S RNA and sR027.Conclusions and SignificanceThis study is the first attempt to subject RNA from Y. pestis-infected samples to direct high-throughput sequencing. Many novel sRNAs were identified and the expression patterns of relevant sRNAs in Y. pestis during in vitro growth and in vivo infection were revealed. The annotated sRNAs accounted for the most abundant sRNAs either expressed in bacteria grown in vitro or differentially expressed in the infected lungs. These findings suggested these sRNAs may have important functions in Y. pestis physiology or pathogenesis.

Comparative genomics of metabolic capacities of regulons controlled by cis-regulatory RNA motifs in bacteria

BackgroundIn silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels.An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis.ResultsA comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations.ConclusionsThe reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

Fast online and index-based algorithms for approximate search of RNA sequence-structure patterns

BackgroundIt is well known that the search for homologous RNAs is more effective if both sequence and structure information is incorporated into the search. However, current tools for searching with RNA sequence-structure patterns cannot fully handle mutations occurring on both these levels or are simply not fast enough for searching large sequence databases because of the high computational costs of the underlying sequence-structure alignment problem.ResultsWe present new fast index-based and online algorithms for approximate matching of RNA sequence-structure patterns supporting a full set of edit operations on single bases and base pairs. Our methods efficiently compute semi-global alignments of structural RNA patterns and substrings of the target sequence whose costs satisfy a user-defined sequence-structure edit distance threshold. For this purpose, we introduce a new computing scheme to optimally reuse the entries of the required dynamic programming matrices for all substrings and combine it with a technique for avoiding the alignment computation of non-matching substrings. Our new index-based methods exploit suffix arrays preprocessed from the target database and achieve running times that are sublinear in the size of the searched sequences. To support the description of RNA molecules that fold into complex secondary structures with multiple ordered sequence-structure patterns, we use fast algorithms for the local or global chaining of approximate sequence-structure pattern matches. The chaining step removes spurious matches from the set of intermediate results, in particular of patterns with little specificity. In benchmark experiments on the Rfam database, our improved online algorithm is faster than the best previous method by up to factor 45. Our best new index-based algorithm achieves a speedup of factor 560.ConclusionsThe presented methods achieve considerable speedups compared to the best previous method. This, together with the expected sublinear running time of the presented index-based algorithms, allows for the first time approximate matching of RNA sequence-structure patterns in large sequence databases. Beyond the algorithmic contributions, we provide with RaligNAtor a robust and well documented open-source software package implementing the algorithms presented in this manuscript. The RaligNAtor software is available at http://www.zbh.uni-hamburg.de/ralignator.

Transcriptomic analysis of a tertiary relict plant, extreme xerophyte Reaumuria soongorica to identify genes related to drought adaptation.

Background Reaumuria soongorica is an extreme xerophyte shrub widely distributed in the desert regions including sand dune, Gobi and marginal loess of central Asia which plays a crucial role to sustain and restore fragile desert ecosystems. However, due to the lacking of the genomic sequences, studies on R. soongorica had mainly limited in physiological responses to drought stress. Here, a deep transcriptomic sequencing of R. soongorica will facilitate molecular functional studies and pave the path to understand drought adaptation for a desert plant.Methodology/Principal FindingsA total of 53,193,660 clean paired-end reads was generated from the Illumina HiSeq™ 2000 platform. By assembly with Trinity, we got 173,700 contigs and 77,647 unigenes with mean length of 677 bp and N50 of 1109 bp. Over 55% (43,054) unigenes were successfully annotated based on sequence similarity against public databases as well as Rfam and Pfam database. Local BLAST and Kyoto Encyclopedia of Genes and Genomes (KEGG) maps were used to further exhausting seek for candidate genes related to drought adaptation and a set of 123 putative candidate genes were identified. Moreover, all the C4 photosynthesis genes existed and were active in R. soongorica, which has been regarded as a typical C3 plant.Conclusion/SignificanceThe assembled unigenes in present work provide abundant genomic information for the functional assignments in an extreme xerophyte R. soongorica, and will help us exploit the genetic basis of how desert plants adapt to drought environment in the near future.

CMCompare webserver: comparing RNA families via covariance models

A standard method for the identification of novel non-coding RNAs is homology search by covariance models. Covariance models are constructed for specific RNA families with common sequence and structure (e.g. transfer RNAs). Currently, there are models for 2208 families available from Rfam. Before being included into a database, a proposed family should be tested for specificity (finding only true homolog sequences), sensitivity (finding remote homologs) and uniqueness. The CMCompare webserver (CMCws) compares Infernal RNA family models to (i) identify models with poor specificity and (ii) explore the relationship between models. The CMCws provides options to compare new models against all existing models in the current Rfam database to avoid the construction of duplicate models for the same non-coding RNA family. In addition, the user can explore the relationship between two or more models, including whole sets of user-created family models. Visualization of family relationships provides help in evaluating candidates for clusters of biologically related families, called clans. The CMCws is freely available, without any login requirements, at http://rna.tbi.univie.ac.at/cmcws, and the underlying software is available under the GPL-3 license.

Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.

Identification and Characterization of MicroRNAs in Channel Catfish (Ictalurus punctatus) by Using Solexa Sequencing Technology

Channel catfish (Ictalurus spp.) is an economically important species in freshwater aquaculture around the world and occupies a prominent position in the aquaculture industry of the United States. MicroRNAs (miRNAs) play important roles in the regulation of almost every biological process in eukaryotes; however, there is little information available concerning miRNAs in channel catfish. In this study, a small-RNA cDNA library was constructed from 10 tissues of channel catfish, and Solexa sequencing technology was used to perform high-throughput sequencing of the library. A total of 14,919,026 raw reads, representing 161,288 unique sequences, were obtained from the small-cDNA library. After comparing the small RNA sequences with the RFam database, 4,542,396 reads that represent 25,538 unique sequences were mapped to the genome sequence of zebrafish to perform distribution analysis and to screen for candidate miRNA genes. Subsequent bioinformatic analysis identified 237 conserved miRNAs and 45 novel miRNAs in the channel catfish. Stem-loop RT-PCR was applied to validate and profile the expression of the novel miRNAs in 10 tissues. Some novel miRNAs, such as ipu-miR-129b, ipu-miR-7562 and ipu-miR-7553, were expressed in all tissues examined. However, some novel miRNAs appear to be tissue specific. Ipu-miR-7575 is predominantly expressed in stomach. Ipu-miR-7147 and ipu-miR-203c are highly expressed in heart, but are relatively weakly expressed in other tissues. Based on sequence complementarity between miRNAs and mRNA targets, potential target sequences for the 45 novel miRNAs were identified by searching for antisense hits in the reference RNA sequences of the channel catfish. These potential target sequences are involved in immune regulation, transcriptional regulation, metabolism and many other biological functions. The discovery of miRNAs in the channel catfish genome by this study contributes to a better understanding of the role miRNAs play in regulating diverse biological processes in fish and vertebrates.

31 Discovery of novel ncRNA by scanning multiple genome alignments

Recently, non-coding RNAs (ncRNAs) have been discovered with novel functions, and it has been appreciated that there is a pervasive transcription. Therefore, de novo computational ncRNA detection that is accurate and efficient is desirable. The purpose of this study is to develop a ncRNA detection method based on structural conservation. A new method called Multifind, based on Multilign (Xu & Mathews, 2011), was developed. It uses an algorithm that predicts common structures among multiple sequences and estimates the probability that input sequences are ncRNA using a classification support vector machine (SVM). Multilign uses Dynalign (Mathews & Turner, 2002), which folds and aligns two sequences simultaneously without requiring any sequence identity; its structure prediction quality will therefore not be affected by input sequence diversity. Benchmarks showed, Multifind performs better than RNAz on testing sequences extracted from Rfam database (Gardner et al., 2011), especially on sequences that are more diverse. For de novo ncRNA discovery in genomes, Multifind had an advantage in low similarity regions of genome alignments. Multifind takes about 48 hours to finish scanning the whole yeast genome alignment and RNAz takes about 4 hours, therefore, its computational requirements do not present a barrier for most of the users.

Selective binding of 2′-F-c-di-GMP to Ct-E88 and Cb-E43, new class I riboswitches from Clostridium tetani and Clostridium botulinum respectively

C-di-GMP is a second messenger in bacteria and partly regulates bacterial physiology by binding to class I and II riboswitches. Four class I c-di-GMP riboswitch aptamer candidates, Ct-E88, Cb-17B, Cb-E43 and Cd-630 RNAs, selected from a GEMM RNA sequence motif in the Rfam database, were expressed and experimentally verified to bind to c-di-GMP. The two newly characterized c-di-GMP riboswitches, Ct-E88 and Cb-E43, bound c-di-GMP with nanomolar Kd whereas the affinities of Cb-17B and Cd-630 for c-di-GMP were at least a 100-fold weaker. Interestingly, whereas the three riboswitches (Vc2, Et-E88 and Cb-E43) bound c-di-GMP with similar Kd values, 2'-modified analogs of c-di-GMP differentially bound to these three class I aptamers. For example, 2'-F-c-di-GMP bound Vc2 with a Kd value of 102 nM whereas the Kd value of 2'-F-c-di-GMP-Ct-E88 is 43 μM (422× higher than that for Vc2 RNA), revealing that there are differences in the binding sites of functional class I c-di-GMP riboswitches.

The hok mRNA family

The hok/sok toxin-antitoxin system of Escherichia coli plasmid R1 increases plasmid maintenance by killing plasmid-free daughter cells. The hok/sok locus specifies two RNAs: hok mRNA, which encodes a toxic transmembrane protein, and sok antisense RNA, which binds a complementary region in the hok mRNA and induces transcript degradation. During cell growth, the cis-encoded sok RNA inhibits expression of the Hok toxin. In plasmid-free segregants, the rapid decay of sok RNA relative to hok mRNA permits Hok translation, leading to cell death. This post-segregational killing mechanism relies upon the ability of the hok mRNA to adopt alternative structural configurations, which affect ease of translation and the susceptibility of the molecule to degradation. The full-length hok transcript is stable, highly structured and immune to ribosome and antisense RNA binding. Gradual 3′ end processing produces dramatic structural rearrangements in the mRNA, which render the molecule translationally active and expose the sok RNA binding site. During transcription, premature ribosome and sok binding are prevented through the formation of transient metastable hairpins in the 5′ end of the nascent transcript. Several hok mRNA paralogs have been identified in the genome of E. coli, and Hok protein orthologs found in the genomes of Enterobacteria. Using a combination of automated search and extensive manual editing, we compiled a multiple sequence alignment for the hok mRNA. All three experimentally validated hok mRNA structures are mapped onto this alignment, which has been submitted to the Rfam database for RNA families.

Rfam 11.0: 10 years of RNA families

The Rfam database (available via the website at http://rfam.sanger.ac.uk and through our mirror at http://rfam.janelia.org) is a collection of non-coding RNA families, primarily RNAs with a conserved RNA secondary structure, including both RNA genes and mRNA cis-regulatory elements. Each family is represented by a multiple sequence alignment, predicted secondary structure and covariance model. Here we discuss updates to the database in the latest release, Rfam 11.0, including the introduction of genome-based alignments for large families, the introduction of the Rfam Biomart as well as other user interface improvements. Rfam is available under the Creative Commons Zero license.

Identification of novel growth phase- and media-dependent small non-coding RNAs in Streptococcus pyogenes M49 using intergenic tiling arrays

BackgroundSmall non-coding RNAs (sRNAs) have attracted attention as a new class of gene regulators in both eukaryotes and bacteria. Genome-wide screening methods have been successfully applied in Gram-negative bacteria to identify sRNA regulators. Many sRNAs are well characterized, including their target mRNAs and mode of action. In comparison, little is known about sRNAs in Gram-positive pathogens. In this study, we identified novel sRNAs in the exclusively human pathogen Streptococcus pyogenes M49 (Group A Streptococcus, GAS M49), employing a whole genome intergenic tiling array approach. GAS is an important pathogen that causes diseases ranging from mild superficial infections of the skin and mucous membranes of the naso-pharynx, to severe toxic and invasive diseases.ResultsWe identified 55 putative sRNAs in GAS M49 that were expressed during growth. Of these, 42 were novel. Some of the newly-identified sRNAs belonged to one of the common non-coding RNA families described in the Rfam database. Comparison of the results of our screen with the outcome of two recently published bioinformatics tools showed a low level of overlap between putative sRNA genes. Previously, 40 potential sRNAs have been reported to be expressed in a GAS M1T1 serotype, as detected by a whole genome intergenic tiling array approach. Our screen detected 12 putative sRNA genes that were expressed in both strains. Twenty sRNA candidates appeared to be regulated in a medium-dependent fashion, while eight sRNA genes were regulated throughout growth in chemically defined medium. Expression of candidate genes was verified by reverse transcriptase-qPCR. For a subset of sRNAs, the transcriptional start was determined by 5′ rapid amplification of cDNA ends-PCR (RACE-PCR) analysis.ConclusionsIn accord with the results of previous studies, we found little overlap between different screening methods, which underlines the fact that a comprehensive analysis of sRNAs expressed by a given organism requires the complementary use of different methods and the investigation of several environmental conditions. Despite a high conservation of sRNA genes within streptococci, the expression of sRNAs appears to be strain specific.

Clustering Rfam 10.1: Clans, Families, and Classes

The Rfam database contains information about non-coding RNAs emphasizing their secondary structures and organizing them into families of homologous RNA genes or functional RNA elements. Recently, a higher order organization of Rfam in terms of the so-called clans was proposed along with its “decimal release”. In this proposition, some of the families have been assigned to clans based on experimental and computational data in order to find related families. In the present work we investigate an alternative classification for the RNA families based on tree edit distance. The resulting clustering recovers some of the Rfam clans. The majority of clans, however, are not recovered by the structural clustering. Instead, they get dispersed into larger clusters, which correspond roughly to Genes 2012, 3 379 well-described RNA classes such as snoRNAs, miRNAs, and CRISPRs. In conclusion, a structure-based clustering can contribute to the elucidation of the relationships among the Rfam families beyond the realm of clans and classes.

RNASTAR: An RNA STructural Alignment Repository that provides insight into the evolution of natural and artificial RNAs

Automated RNA alignment algorithms often fail to recapture the essential conserved sites that are critical for function. To assist in the refinement of these algorithms, we manually curated a set of 148 alignments with a total of 9600 unique sequences, in which each alignment was backed by at least one crystal or NMR structure. These alignments included both naturally and artificially selected molecules. We used principles of isostericity to improve the alignments from an average of 83%-94% isosteric base pairs. We expect that this alignment collection will assist in a wide range of benchmarking efforts and provide new insight into evolutionary principles governing change in RNA structural motifs. The improved alignments have been contributed to the Rfam database.

ITSoneDB: a specialized ITS1 database for amplicon-based metagenomic characterization of environmental fungal communities

Motivations. Metagenomics is experiencing an explosive improvement from the advent of high-throughput next-generation sequencing (NGS) technologies which allows an unprecedented large-scale identification of microorganisms living in almost every environment. In particular, the use of amplicon-based metagenomic approach to explore the diversity of fungal environmental communities is increasingly expanding. At the species level, a number of studies have used the non-conserved internal transcribed spacers (ITS) 1 and 2 of the ribosomal RNA genes cluster as genetic markers to explore the fungal taxonomic diversity. Particularly, ITS1 is gaining an increasing popularity as better discriminating species marker in Fungi because of its higher variability compared to ITS2. Starting from the total DNA extracted from any environmental sample, this locus can be easily amplified with taxonomically universal primers and sequenced by means of high-throughput next generation platforms. Reference databases and robust supporting taxonomies are crucial in assigning phylogenetic affiliation to the huge amount of produced sequences. Even if a large number of ITS1 sequences are collected in public databases, a specialized resource focused particularly on this region, where sequences identity, boundaries and taxonomic assignment are validated, is still needed at present. In this work we present ITSoneDB, a new comprehensive collection of ITS1 sequences belonging to Fungi Kingdom. Methods. ITSoneDB has been generated and populated using a multi-step Python workflow. In the first step the ribosomal RNA gene cluster sequences of Fungi including the target ITS1 region were retrieved from Genbank. Then, ITS1 start and end boundaries were extracted from the Features Tables annotations, if available. In order to infer, validate and, eventually, redesign the ITS1 location, Hidden Markov Model (HMM) profiles of flanking genes for 18S and 5.8S ribosomal RNA, generated from their reference alignments stored in RFAM database, were mapped on the entire collection of retrieved nucleotide sequences, by means of the hmmsearch tool from HMMER 3.0 package. Results. At present, ITSoneDB includes 405,433 taxonomically arranged sequence entries provided with ITS1 both start and end positions defined by GenBank annotations and/or HMM based method. ITSoneDB front-end is a JAVA platform-based website for data browsing and downloading. The database can be queried by species or taxon name, GenBank accession ID or by "expanding" the target rank on a detailed fungal taxonomical tree. The complete ITS1 sequences dataset collected in ITSoneDB is available in Fasta format and the users can extract and locally save all or selected queried ITS1 sequences for further analysis. Availability http://itsonedb.ba.itb.cnr.it/

Distinct families of cis-acting RNA replication elements epsilon from hepatitis B viruses

The hepadnavirus encapsidation signal, epsilon (ε), is an RNA structure located at the 5′ end of the viral pregenomic RNA. It is essential for viral replication and functions in polymerase protein binding and priming. This structure could also have potential regulatory roles in controlling the expression of viral replicative proteins. In addition to its structure, the primary sequence of this RNA element has crucial functional roles in the viral lifecycle. Although the ε elements in hepadnaviruses share common critical functions, there are some significant differences in mammalian and avian hepadnaviruses, which include both sequence and structural variations. Here we present several covariance models for ε elements from the Hepadnaviridae. The model building included experimentally determined data from previous studies using chemical probing and NMR analysis. These models have sufficient similarity to comprise a clan. The clan has in common a highly conserved overall structure consisting of a lower-stem, bulge, upper-stem and apical-loop. The models differ in functionally critical regions—notably the two types of avian ε elements have a tetra-loop (UGUU) including a non-canonical UU base pair, while the hepatitis B virus (HBV) epsilon has a tri-loop (UGU). The avian epsilon elements have a less stable dynamic structure in the upper stem. Comparisons between these models and all other Rfam models, and searches of genomes, showed these structures are specific to the Hepadnaviridae. Two family models and the clan are available from the Rfam database.

The R2 retrotransposon RNA families

Analysis of the R2 retrotransposons from multiple silkmoth and fruitfly species have revealed three segments that contain conserved RNA secondary structures. These conserved structures play important roles in the propagation of the R2 element, including R2 RNA processing and transposon integration into the host genome as well as a likely role in translation. Two of the structured regions comprise protein binding sites: one is located in the 3' UTR and the other is in the 5' UTR close to the putative start of the R2 open reading frame (ORF). The 3' structure was deduced from chemical mapping and sequence comparison. The 5' structure was determined using a combination of chemical mapping, oligonucleotide binding, NMR and sequence analysis and contains an unusual pseudoknot structure. The third structure occurs at the 5' end of the R2 RNA and is responsible for self-cleavage of the 5' end of the element from a 28S ribosomal RNA co-transcript. A structure for this fragment was proposed based on motif searching and sequence comparison. There is remarkable similarity in sequence and structure to the hepatitis delta virus (HDV) ribozyme. Seed alignments for the 5' structure and the R2 ribozyme, containing representative sequences and consensus structures, have been submitted to the Rfam database.

Two covariance models for iron-responsive elements

Iron-responsive elements (IREs) function in the 5’ or 3’ untranslated regions (UTRs) of mRNAs as post-transcriptional structured cis-acting RNA regulatory elements. One known functional mechanism is the binding of Iron Regulatory Proteins (IRPs) to 5’ UTR IREs, reducing translation rates at low iron levels. Another known mechanism is IRPs binding to 3’ UTR IREs in other mRNAs, increasing RNA stability. Experimentally proven elements are quite small, have some diversity of sequence and structure, and functional genes have similar pseudogenes in the genome. This paper presents two new IRE covariance models, comprising a new IRE clan in the RFAM database to encompass this variation without over-generalisation. Two IRE models rather than a single model is consistent with experimentally proven structures and predictions. All of the IREs with experimental support are modelled. These two new models show a marked increase in the sensitivity and specificity in detection of known iron-responsive elements and ability to predict novel IREs.

Structator: fast index-based search for RNA sequence-structure patterns

BackgroundThe secondary structure of RNA molecules is intimately related to their function and often more conserved than the sequence. Hence, the important task of searching databases for RNAs requires to match sequence-structure patterns. Unfortunately, current tools for this task have, in the best case, a running time that is only linear in the size of sequence databases. Furthermore, established index data structures for fast sequence matching, like suffix trees or arrays, cannot benefit from the complementarity constraints introduced by the secondary structure of RNAs.ResultsWe present a novel method and readily applicable software for time efficient matching of RNA sequence-structure patterns in sequence databases. Our approach is based on affix arrays, a recently introduced index data structure, preprocessed from the target database. Affix arrays support bidirectional pattern search, which is required for efficiently handling the structural constraints of the pattern. Structural patterns like stem-loops can be matched inside out, such that the loop region is matched first and then the pairing bases on the boundaries are matched consecutively. This allows to exploit base pairing information for search space reduction and leads to an expected running time that is sublinear in the size of the sequence database. The incorporation of a new chaining approach in the search of RNA sequence-structure patterns enables the description of molecules folding into complex secondary structures with multiple ordered patterns. The chaining approach removes spurious matches from the set of intermediate results, in particular of patterns with little specificity. In benchmark experiments on the Rfam database, our method runs up to two orders of magnitude faster than previous methods.ConclusionsThe presented method's sublinear expected running time makes it well suited for RNA sequence-structure pattern matching in large sequence databases. RNA molecules containing several stem-loop substructures can be described by multiple sequence-structure patterns and their matches are efficiently handled by a novel chaining method. Beyond our algorithmic contributions, we provide with Structator a complete and robust open-source software solution for index-based search of RNA sequence-structure patterns. The Structator software is available at http://www.zbh.uni-hamburg.de/Structator.