Rfam Database Research Articles

Rfam 15: RNA families database in 2025.

The Rfam database, a widely used repository of non-coding RNA families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26 106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology (GO) and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronization with miRBase, resulting in 1603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation and the development of machine learning models. Rfam is freely available at https://rfam.org.

Rfam 15: RNA families database in 2025.

The Rfam database, a widely-used repository of non-coding RNA (ncRNA) families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26,106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronisation with miRBase, resulting in 1,603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation, and the development of machine learning models. Rfam is freely available at https://rfam.org.

In-silico identification of putatively functional intergenic small open reading frames in the cucumber genome and their predicted response to biotic and abiotic stresses.

The availability of high-throughput sequencing technologies increased our understanding of different genomes. However, the genomes of all living organisms still have many unidentified coding sequences. The increased number of missing small open reading frames (sORFs) is due to the length threshold used in most gene identification tools, which is true in the genic and, more importantly and surprisingly, in the intergenic regions. Scanning the cucumber genome intergenic regions revealed 420 723 sORF. We excluded 3850 sORF with similarities to annotated cucumber proteins. To propose the functionality of the remaining 416 873 sORF, we calculated their codon adaptation index (CAI). We found 398 937 novel sORF (nsORF) with CAI ≥ 0.7 that were further used for downstream analysis. Searching against the Rfam database revealed 109 nsORFs similar to multiple RNA families. Using SignalP-5.0 and NLS, identified 11 592 signal peptides. Five predicted proteins interacting with Meloidogyne incognita and Powdery mildew proteins were selected using published transcriptome data of host-pathogen interactions. Gene ontology enrichment interpreted the function of those proteins, illustrating that nsORFs' expression could contribute to the cucumber's response to biotic and abiotic stresses. This research highlights the importance of previously overlooked nsORFs in the cucumber genome and provides novel insights into their potential functions.

Median and small parsimony problems on RNA trees.

Noncoding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis. We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labeled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility. https://github.com/bmarchand/rna\_small\_parsimony.

Caulobacter crescentus RNase E condensation contributes to autoregulation and fitness.

RNase E is the most common RNA decay nuclease in bacteria, setting the global mRNA decay rate and scaffolding formation of the RNA degradosome complex and BR-bodies. To properly set the global mRNA decay rate, RNase E from Escherichia coli and neighboring γ-proteobacteria were found to autoregulate RNase E levels via the decay of its mRNA's 5' untranslated region (UTR). While the 5' UTR is absent from other groups of bacteria in the Rfam database, we identified that the α-proteobacterium Caulobacter crescentus RNase E contains a similar 5' UTR structure that promotes RNase E autoregulation. In both bacteria, the C-terminal intrinsically disordered region (IDR) of RNase E is required for proper autoregulation to occur, and this IDR is also necessary and sufficient for RNase E to phase-separate, generating BR-bodies. Using in vitro purified RNase E, we find that the IDR's ability to promote phase separation correlates with enhanced 5' UTR cleavage, suggesting that phase separation of RNase E with the 5' UTR enhances autoregulation. Finally, using growth competition experiments, we find that a strain capable of autoregulation rapidly outcompetes a strain with a 5' UTR mutation that cannot autoregulate, suggesting autoregulation promotes optimal cellular fitness.

Meta-omics approaches reveal unique small RNAs exhibited by the uncultured microorganisms dwelling deep-sea hydrothermal sediment in Guaymas Basin.

Small regulatory RNAs (sRNAs) are present in almost all investigated microbes, regarded as modulators and regulators of gene expression and also known to play their regulatory role in the environmentally significant process. It has been estimated that less than 1% of the microbes in nature are culturable in the laboratory, hindering our understanding of their physiology, and living strategies. However, recent big advancing of DNA sequencing and omics-related data analysis makes the understanding of the genetics, metabolic potentials, even ecological roles of uncultivated microbes possible. In this study, we used a metagenome and metatranscriptome-based integrated approach to identify small RNAs in the microbiome of Guaymas Basin sediments. Hundreds of environmental sRNAs comprising 228 groups were identified based on their homology, 82% of which displayed high similarity with previously known small RNAs in Rfam database, whereas, "18%" are putative novel sRNA motifs. A putative cis-acting sRNA potentially binding to methyl coenzyme M reductase, a key enzyme in methanogenesis or anaerobic oxidation of methane (AOM), was discovered in the genome of ANaerobic MEthane oxidizing archaea group 1 (ANME-1), which were the dominate microbe in the sample. These sRNAs were actively expressed in local Guaymas Basin hydrothermal environment, suggesting important roles of sRNAs in regulating microbial activity in natural environments.

Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences.

RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. Supplementary data are available at Bioinformatics online.

Swarm Intelligence-based Hierarchical Clustering for Identification of ncRNA using Covariance Search Model

Covariance Model (CM) has been quite effective in finding potential members of existing families of non-coding Ribonucleic Acid (ncRNA) identification and has provided ex-cellent accuracy in genome sequence database. However, it has significant drawbacks with family-specific search. An existing Hierarchical Agglomerative Clustering (HAC) technique merged overlapping sequences which is known as combined CM (CCM). However, the structural information will be discarded, and the sequence features of each family will be significantly diluted as the number of original structures increases. Additionally, it can only find members of the existing families and is not useful in finding potential members of novel ncRNA families. Furthermore, it is also important to construct generic sequence models which can be used to recognize new potential members of novel ncRNA families and define unknown ncRNA sequence as the potential members for known families. To achieve these objectives, this study proposes to implement Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to ensure the CCMs have the best quality for every level of dendrogram hierarchy. This study will also apply distance matrix as the criteria to measure the compatibility between two CMs. The proposed techniques will be using five gene families with fifty sequences from each family from Rfam database which will be divided into training and testing dataset to test CMs combination method. The proposed techniques will be compared to the existing HAC in terms of identification accuracy, sum of bit-scores, and processing time, where each of these performance measurements will be statistically validated.

Comparative genomics provides structural and functional insights into Bacteroides RNA biology.

Bacteria employ noncoding RNA molecules for a wide range of biological processes, including scaffolding large molecular complexes, catalyzing chemical reactions, defending against phages, and controlling gene expression. Secondary structures, binding partners, and molecular mechanisms have been determined for numerous small noncoding RNAs (sRNAs) in model aerobic bacteria. However, technical hurdles have largely prevented analogous analyses in the anaerobic gut microbiota. While experimental techniques are being developed to investigate the sRNAs of gut commensals, computational tools and comparative genomics can provide immediate functional insight. Here, using Bacteroides thetaiotaomicron as a representative microbiota member, we illustrate how comparative genomics improves our understanding of RNA biology in an understudied gut bacterium. We investigate putative RNA-binding proteins and predict a Bacteroides cold-shock protein homolog to have an RNA-related function. We apply an in silico protocol incorporating both sequence and structural analysis to determine the consensus structures and conservation of nine Bacteroides noncoding RNA families. Using structure probing, we validate and refine these predictions and deposit them in the Rfam database. Through synteny analyses, we illustrate how genomic coconservation can serve as a predictor of sRNA function. Altogether, this work showcases the power of RNA informatics for investigating the RNA biology of anaerobic microbiota members.

RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis.

The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap_standalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap. Supplementary data are available at Bioinformatics online.

RfaRm: An R client-side interface to facilitate the analysis of the Rfam database of RNA families.

rfaRm is an R package providing a client-side interface for the Rfam database of non-coding RNA and other structured RNA elements. The package facilitates the search of the Rfam database by keywords or sequences, as well as the retrieval of all available information about specific Rfam families, such as member sequences, multiple sequence alignments, secondary structures and covariance models. By providing such programmatic access to the Rfam database, rfaRm enables genomic workflows to incorporate information about non-coding RNA, whose potential cannot be fully exploited just through interactive access to the database. The features of rfaRm are demonstrated by using it to analyze the SARS-CoV-2 genome as an example case.

Comparative Metatranscriptomics of Periodontitis Supports a Common Polymicrobial Shift in Metabolic Function and Identifies Novel Putative Disease-Associated ncRNAs.

Periodontitis is an inflammatory disease that deteriorates bone supporting teeth afflicting ∼743 million people worldwide. Bacterial communities associated with disease have been classified into red, orange, purple, blue, green, and yellow complexes based on their roles in the periodontal pocket. Previous metagenomic and metatranscriptomics analyses suggest a common shift in metabolic signatures in disease vs. healthy communities with up-regulated processes including pyruvate fermentation, histidine degradation, amino acid metabolism, TonB-dependent receptors. In this work, we examine existing metatranscriptome datasets to identify the commonly differentially expressed transcripts and potential underlying RNA regulatory mechanisms behind the metabolic shifts. Raw RNA-seq reads from three studies (including 49 healthy and 48 periodontitis samples) were assembled into transcripts de novo. Analyses revealed 859 differentially expressed (DE) transcripts, 675 more- and 174 less-expressed. Only ∼20% of the DE transcripts originate from the pathogenic red/orange complexes, and ∼50% originate from organisms unaffiliated with a complex. Comparison of expression profiles revealed variations among disease samples; while specific metabolic processes are commonly up-regulated, the underlying organisms are diverse both within and across disease associated communities. Surveying DE transcripts for known ncRNAs from the Rfam database identified a large number of tRNAs and tmRNAs as well as riboswitches (FMN, glycine, lysine, and SAM) in more prevalent transcripts and the cobalamin riboswitch in both more and less prevalent transcripts. In silico discovery identified many putative ncRNAs in DE transcripts. We report 15 such putative ncRNAs having promising covariation in the predicted secondary structure and interesting genomic context. Seven of these are antisense of ribosomal proteins that are novel and may involve maintaining ribosomal protein stoichiometry during the disease associated metabolic shift. Our findings describe the role of organisms previously unaffiliated with disease and identify the commonality in progression of disease across three metatranscriptomic studies. We find that although the communities are diverse between individuals, the switch in metabolic signatures characteristic of disease is typically achieved through the contributions of several community members. Furthermore, we identify many ncRNAs (both known and putative) which may facilitate the metabolic shifts associated with periodontitis.

RNA 3D structure prediction guided by independent folding of homologous sequences

BackgroundThe understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task.ResultsUsing the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence.ConclusionThis work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

Identification and characterization of microRNAs in the gonads of Crassostrea hongkongensis using high-throughput sequencing

Crassostrea hongkongensis is one of the three most-commonly cultivated oyster species in China. Although microRNAs (miRNAs) expression in the gonads have been widely investigated, few studies of miRNAs in mollusk gonads are available, particularly in oyster. In the present study, we analyzed the miRNAs expressed in the ovaries and testes of C. hongkongensis. We obtained 14,166,409 and 15,133,900 raw reads from the ovaries and testes, respectively, yielding 13,634,997 (ovarian) and 14,494,149 (testicular) 18–35-nt sequences. We mapped these sequences to the C. hongkongensis genome reference sequence, and identified 8,771,717 (ovarian) and 9,926,014 (testicular) sequences corresponding to miRNAs in the Rfam database. After blasting the miRNA sequences against the miRBase database, we identified 50 known mature miRNAs and 53 novel miRNAs. Of these, 27 miRNAs were significantly upregulated in ovaries as compared to the testes, and 43 miRNAs were significantly upregulated in the testes as compared to the ovaries.To validate the differential expression results generated by Illumina sequencing, we used RT-real-time quantitative PCR (RT-qPCR) to characterize the expression patterns of the six most differently expressed miRNAs (lgi-miR-1990, lgi-miR-1986, lgi-miR-263b, lgi-miR-279, lgi-miR-1992, and novel_98) as well as two miRNAs associated with gonad development (lgi-miR-29 and lgi-miR-8). Most of the RT-qPCR miRNA expression patterns were similar to those recovered by high-throughput sequencing with the exceptions of novel_98 and lgi-miR-1992. Gene Ontology (GO) annotations indicated that the multi-organism cellular process GO category was enriched with the target genes of the differentially expressed miRNAs. Additionally, the target genes were enriched in several KEGG pathways, including the ECM-receptor interaction, galactose metabolism, phagosome, and notch signaling pathway. These pathways are involved in gonadal differentiation and the maintenance of gonad function. This identification and characterization of the miRNAs differentially expressed between the ovaries and testes of C. hongkongensis will increase our understanding of the role of miRNAs in gonad differentiation in the oyster.

Automatic curation of large comparative animal MicroRNA datasets.

MicroRNAs form an important class of RNA regulators that has been studied extensively. The miRBase and Rfam database provide rich, frequently updated information on both pre-miRNAs and their mature forms. These data sources, however, rely on individual data submission and thus are neither complete nor consistent in their coverage across different miRNA families. Quantitative studies of miRNA evolution therefore are difficult or impossible on this basis. We present here a workflow and a corresponding implementation, MIRfix, that automatically curates miRNA datasets by improving alignments of their precursors, the consistency of the annotation of mature miR and miR* sequence, and the phylogenetic coverage. MIRfix produces alignments that are comparable across families and sets the stage for improved homology search as well as quantitative analyses. MIRfix can be downloaded from https://github.com/Bierinformatik/MIRfix. Supplementary data are available at Bioinformatics online.

Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature.

The detection of MicroRNA (miRNA) mentions in scientific literature facilitates researchers with the ability to find relevant and appropriate literature based on queries formulated using miRNA information. Considering most published biological studies elaborated on signal transduction pathways or genetic regulatory information in the form of figure captions, the extraction of miRNA from both the main content and figure captions of a manuscript is useful in aggregate analysis and comparative analysis of the studies published. In this study, we present a statistical principle-based miRNA recognition and normalization method to identify miRNAs and link them to the identifiers in the Rfam database. As one of the core components in the text mining pipeline of the database miRTarBase, the proposed method combined the advantages of previous works relying on pattern, dictionary and supervised learning and provided an integrated solution for the problem of miRNA identification. Furthermore, the knowledge learned from the training data was organized in a human-interpretable manner to understand the reason why the system considers a span of text as a miRNA mention, and the represented knowledge can be further complemented by domain experts. We studied the ambiguity level of miRNA nomenclature to connect the miRNA mentions to the Rfam database and evaluated the performance of our approach on two datasets: the BioCreative VI Bio-ID corpus and the miRNA interaction corpus by extending the later corpus with additional Rfam normalization information. Our study highlights and also proposes a better understanding of the challenges associated with miRNA identification and normalization in scientific literature and the research gap that needs to be further explored in prospective studies.

Global discovery of small RNAs in the fish pathogen Edwardsiella piscicida: key regulator of adversity and pathogenicity

Recently, bacterial small RNA (sRNA) has been shown to be involved as a key regulator in stress responses. sRNAs of Edwardsiella piscicida, an important aquatic pathogen, are not well characterized to date. In this study, using RNA-seq technology, we globally found and identified sRNA candidates expressed from E. piscicida grown in normal LB medium, acid pressure, iron deficiency stress, and oxidation pressure. A total of 148 sRNAs were found, including 19 previously annotated sRNAs and 129 novel sRNA candidates by searching against the Rfam database. Compared in normal condition, the expression of 103 sRNAs (DEsRNA, differentially expressed sRNA) and 1615 mRNAs (DEmRNAs, differentially expressed mRNA) showed significant differences in three stress sample. Based on the prediction by IntaRNA and relational analysis between DEsRNAs and DEmRNAs, 103 DEsRNAs were predicted to regulate 769 target mRNAs. Pleiotropic function of target DEmRNAs indicated that sRNAs extensively participated in a variety of physiological processes, including response to adversity and pathogenicity, the latter was further confirmed by infection experiment. A large number transcription factors appeared in target genes of sRNAs, which suggested that sRNAs likely deeply interlaced within complex gene regulatory networks of E. piscicida. Moreover, 49 Hfq-associated sRNAs were also identified in this study. In summary, we globally discovered sRNAs for the first time in pathogenic bacteria of fish, and our findings indicated that sRNAs in E. piscicida have important roles in adaptation to environmental stress and pathogenicity. These results also provide clues for deciphering regulation mechanism of gene expression related to physiological response and pathogenicity.

Identification and profiling of microRNAs of Euphausia superba using Illumina deep sequencing

MicroRNAs (miRNAs) are an abundant class of conserved, non-coding small RNAs that play important role in gene regulation at post translational level. There have been no reports on the miRNAs of the Antarctic krill Euphausia superba despite the species’ crucial position in Antarctic food webs. Two small RNA libraries were constructed from eyestalk and muscle, subsequently, and deep sequencing analysis was performed to investigate and profile E. superba miRNAs. A total of 19 304 586 and 23 005 104 unique sequences were obtained from the eyestalk and muscle, respectively. After compared the small RNA sequences with the Rfam database, 12 342 039 and 7 907 477 reads in eyestalk and muscle were matched to the transcriptome sequence of E. superba. A total of 236 distinct miRNAs were identified after annotation to known animal miRNAs registered in miRBase 21. In both libraries, the most abundant known miRNA were miR-750 with 92 583 reads in muscle and miR-1304-3p with 56 386 reads in eyestalk while the average value was less than 106, revealing a wide range of different expression levels. In addition, miR-277a enriched in both libraries and may be involved in modulating Krebs cycle by targeting to Vimar. Differential expression analysis showed that 56 mature miRNAs were significantly up/down regulated according to expression fold change. It appeared that the expression of several abundant miRNAs maybe tissue-specific or tissue-bias. Notably, the expression pattern of miR-750 and miR-1 family, which was suggested as the crucial candidates, involved in muscle development. Taken together, this study provides the first miRNA profile of E. superba and some of these miRNAs were expected to play important role in immune response, reproduction, energy metabolism, and muscle development and so on. Thus, the results provides a reference for functional studies of miRNAs in E. superba.

In silico 'fishing' using known small regulatory RNA (sRNA) candidates as the decoy from Escherichia coli, Salmonella typhi and Salmonella typhimurium manifested 14 novel sRNA candidates in the orthologous region of Proteus mirabilis.

Proteus mirabilis, a gram-negative bacterium of the family Enterobacteriaceae, is a leading cause of urinary tract infection (UTI) with rapid development of multi-drug resistance. Identification of small regulatory RNAs (sRNAs), which belongs to a class of RNAs that do not translate into a protein, could permit the comprehension of the regulatory roles this molecules play in mediating pathogenesis and multi-drug resistance of the organism. In this study, comparative sRNA analysis across three different members of Enterobacteriaceae (Escherichia coli, Salmonella typhi and Salmonella typhimurium) was carried out to identify the sRNA homologs in P. mirabilis. A total of 232 sRNA genes that were reported in E. coli, S. typhi and S. typhimurium were subjected to comparative analysis against P. mirabilis HI4320 genome. We report the detection of 14 sRNA candidates, conserved in the orthologous regions of P. mirabilis, that are not included in Rfam database. Northern-blot analysis was carried out for selected three sRNA candidates from the current investigation and three known sRNA from Rfam of P. mirabilis. The expression pattern of the six sRNA candidates shows that they are growth stage-dependant. To the best of our knowledge, this is the first report on the identification of sRNA candidates in P. mirabilis.

RNApuzzler: efficient outerplanar drawing of RNA-secondary structures.

RNA secondary structure is a useful representation for studying the function of RNA, which captures most of the free energy of RNA folding. Using empirically determined energy parameters, secondary structures of nucleic acids can be efficiently computed by recursive algorithms. Several software packages supporting this task are readily available. As RNA secondary structures are outerplanar graphs, they can be drawn without intersection in the plane. Interpretation by the practitioner is eased when these drawings conform to a series of additional constraints beyond outerplanarity. These constraints are the reason why RNA drawing is difficult. Many RNA drawing algorithms therefore do not always produce intersection-free (outerplanar) drawings. To remedy this shortcoming we propose here the RNApuzzler algorithm which is guaranteed to produce intersection-free drawings. It is based on a drawing algorithm respecting constraints based on nucleotide distances (RNAturtle). We investigate relaxations of these constraints allowing for intersection-free drawings. Based on these relaxations, we implemented a fully automated, simple, and robust algorithm that produces aesthetic drawings adhering to previously established guidelines. We tested our algorithm using the RFAM database and found that we can compute intersection-free drawings of all RNAs therein efficiently. The software can be accessed freely at: https://github.com/dwiegreffe/RNApuzzler. Supplementary data are available at Bioinformatics online.