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One proposed role for biomolecular condensates that contain RNA is translation regulation. In several specific contexts, translation has been shown to be modulated by the presence of a phase-separating protein and under conditions which promote phase separation, and likely many more await discovery. A powerful tool for determining the rules for condensate-dependent translation is the use of engineered RNA sequences, which can serve as reporters for translation efficiency. This Perspective will discuss design features to consider in engineering RNA reporters to determine the role of phase separation in translational regulation. Specifically, we will cover (i) how to engineer RNA sequence to recapitulate native protein/RNA interactions, (ii) the advantages and disadvantages for commonly used reporter RNA sequences, and (iii) important control experiments to distinguish between binding- and condensation-dependent translational repression. The goal of this review is to promote the design and application of faithful translation reporters to demonstrate a physiological role of biomolecular condensates in translation.

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP binding preferences. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors of RBPs and can provide new insights about the regulatory functions of RBPs.

Small extracellular vesicles (sEVs), or exosomes, play important roles in physiological and pathological cellular communication. sEVs contain both short and long non-coding RNAs that regulate gene expression and epigenetic processes. Studying the intricacies of sEV function and RNA-based communication requires tools capable of labeling sEV RNA. Here we developed a novel genetically encodable reporter system for tracking sEV RNAs comprising an sEV-loading RNA sequence, termed the EXO-Code, fused to a fluorogenic RNA Mango aptamer for RNA imaging. This fusion construct allowed the visualization and tracking of RNA puncta and colocalization with markers of multivesicular bodies; imaging RNA puncta within sEVs; and quantification of sEVs. This technology represents a useful and versatile tool to interrogate the role of sEVs in cellular communication via RNA trafficking to sEVs, cellular sorting decisions, and sEV RNA cargo transfer to recipient cells.

Post-transcriptional modifications by RNA editing are essential for neurodevelopment, yet their developmental and regulatory features remain poorly resolved. We constructed a full temporal view of base-specific RNA editing in the developing human cortex, from early progenitors through fully mature cells found in the adult brain. Developmental regulation of RNA editing is characterized by an increase in editing rates for more than 10,000 selective editing sites, shifting between mid-fetal development and infancy, and a massive expansion of RNA hyper-editing sites that amass in the cortex through postnatal development into advanced age. These sites occur disproportionally in 3’UTRs of essential neurodevelopmental genes. These profiles are preserved in non-human primate and murine models, illustrating evolutionary conserved regulation of RNA editing in mammalian cortical development. RNA editing levels are commonly genetically regulated (editing quantitative trait loci, edQTLs) consistently across development or predominantly during prenatal or postnatal periods. Both consistent and temporal-predominant edQTLs co-localize with risk loci associated with neurological traits and disorders, including attention deficit hyperactivity disorder, schizophrenia, and sleep disorders. These findings expand the repertoire of highly regulated RNA editing sites in the brain and provide insights of how epitranscriptional sequence diversity by RNA editing contributes to neurodevelopment.

State-of-the-art methods for predicting novel trans RNA-RNA interactions use the so-called accessibility as key concept. It estimates whether a region in a given RNA sequence is accessible for forming trans interactions, using a thermodynamic model which quantifies its secondary structure features. RNA-RNA interactions are then predicted by finding the minimum free energy base-pairing between the two transcripts, taking into account the accessibility as energy penalty.We investigated the underlying assumptions of this approach using the two methods RNAplex and IntaRNA on two datasets, containing sRNA-mRNA and snoRNA-rRNA interactions, respectively.We find that (1) known trans RNA-RNA interactions frequently overlap regions containing RNA structure features, (2) the estimated accessibility reflects sRNA structures fairly well, but often disagrees with structure annotations of longer transcripts, (3) the prediction performance of RNA-RNA interaction prediction methods is independent of the quality of the estimated accessibility profiles, and (4) one important overall effect of accessibility profiles is to prevent the thermodynamic model from predicting too long interactions.Based on our findings, we conclude that the accessibility concept to the minimum free energy approach to predicting novel RNA-RNA interactions has conceptual limitations and discuss potential ways of improving the field in the future.

We report a non-clinical, mathematical method of studying genetic sequences based on the information theory. Our method involves calculating the information entropy spectrum of genomes by splitting them into “windows” containing a fixed number of nucleotides. The information entropy value of each window is computed using the m-block information entropy formula. We show that the information entropy spectrum of genomes contains sufficient information to allow detection of genetic mutations, as well as possibly predicting future ones. Our study indicates that the best m-block size is 2 and the optimal window size should contain more than 9, and less than 33 nucleotides. In order to implement the proposed technique, we created specialized software, which is freely available. Here we report the successful test of this method on the reference RNA sequence of the SARS-CoV-2 virus collected in Wuhan, Dec. 2019 (MN908947) and one of its randomly selected variants from Taiwan, Feb. 2020 (MT370518), displaying 7 mutations.

Guanine quadruplexes (G4) structures in viral genome have a key role in modulating viruses’ biological activity. While several DNA G4 structures have been experimentally resolved, RNA G4s are definitely less explored. We report the first calculated G4 structure of the RG-1 RNA sequence of SARS-CoV-2 genome, obtained by using a multiscale approach combining quantum and classical molecular modelling and corroborated by the excellent agreement between the corresponding calculated and experimental circular dichroism spectra. We prove the stability of RG-1 G4 arrangement as well as its interaction with G4 ligands potentially inhibiting viral protein translation.

RNA folds into secondary and tertiary structures that can mediate diverse cellular functions. Understanding how RNA sequence directs the formation of biologically active structures requires approaches that can comprehensively assess how changes in an RNA sequence affect its structure and function. Towards this goal, I have developed a general method for purifying E. coli RNA polymerase (RNAP) transcription elongation complexes (TECs) for use in systematic RNA assays. My approach depends on two constituent technologies: First, I have designed an E. coli σ 70 promoter that can be efficiently barcoded using a one-pot series of enzymatic reactions. Second, I have developed a strategy for purifying promoter-initiated E. coli RNAP TECs by selective photo-elution from streptavidin-coated magnetic beads. Together, these methods establish a platform for the development of TEC Display assays in which the functional properties of RNA sequence variants can be recorded by fractionating and quantitatively barcoding a TEC library.

N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m6A detection. The optimised miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m6A detection. The analyses are calibrated with Mettl3 knockout cells to learn the characteristics of m6A deposition, including m6A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification.HighlightsmiCLIP2 produces complex libraries to map m6A RNA modificationsMettl3 KO miCLIP2 allows to identify Mettl3-dependent RNA modification sitesMachine learning predicts genuine m6A sites from human and mouse miCLIP2 data without Mettl3 KOm6A modifications occur outside of DRACH motifs and associate with alternative splicing

Cis-regulatory motif finding is a crucial step in the detection of gene regulatory mechanisms using genomic data. Deep learning (DL) models have been utilized to denovoly identify motifs, and have been proven to outperform traditional methods. By 2020, twenty DL models have been developed to identify DNA and RNA motifs with diverse framework designs and implementation styles. Hence, it is beneficial to systematically compare their performances, which can facilitate researchers in selecting the appropriate tools for their motif analyses. Here, we carried out an in-depth assessment of the 20 models utilizing 1,043 genomic sequencing datasets, including 690 ENCODE ChIP-Seq, 126 cancer ChIP-Seq, 172 single-cell cleavages under targets and release using a nuclease, and 55 RNA CLIP-Seq. Four metrics were designed and investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability, and tool usability. The assessment results demonstrated the high complementarity of the existing models, and it was determined that the most suitable model should primarily depend on the data size and type as well as the model outputs. A webserver was developed to allow efficient access of the identified motifs and effective utilization of high-performing DL models.

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data—yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic–target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a virus’s genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPT’s designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPT’s designs exhibit significantly higher fluorescence and permit lower limits of detection, across a virus’s entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

SummaryRecently we developed scCapsNet, an interpretable deep learning cell type classifier for single cell RNA sequencing data, based on capsule network. However, the running process of scCapsNet is not fully automatic, in which a manual intervention is required for getting the final results. Here we present scCapsNet-mask, an updated version of scCapsNet that utilizes a mask to fully automate the running process of scCapsNet. scCapsNet-mask could constrain the internal parameter coupling coefficients and result in a one to one correspondence between the primary capsule and type capsule. Based on those bijective mapping between primary capsule and type capsule, the model could automatically extract the cell type related genes according to weight matrix connecting input and primary capsule, without a need for manual inspection of the relationship between primary capsules and type capsules. The scCapsNet-mask is evaluated on two single cell RNA sequence datasets. The results show that scCapsNet-mask not only retains the merits of the original scCapsNet with high classification accuracy and high interpretability, but also has the virtue of automatic processing.

Fundamental aspects of SARS-CoV-2 biology remain to be described, having the potential to provide insight to the response effort for this high-priority pathogen. Here we describe the first native RNA sequence of SARS-CoV-2, detailing the coronaviral transcriptome and epitranscriptome, and share these data publicly. A data-driven inference of viral genetic features and evolutionary rate is also made. The rapid sharing of sequence information throughout the SARS-CoV-2 pandemic represents an inflection point for public health and genomic epidemiology, providing early insights into the biology and evolution of this emerging pathogen.

Motivation Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner. Results We show that QAlign is able to improve alignment rates from around 80% up to 90% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6% to 75.4% and 82.6% to 90% in two real datasets. Availability and implementation https://github.com/joshidhaivat/QAlign.git. Supplementary information Supplementary data are available at Bioinformatics online.

The Rbfox family of splicing factors regulate alternative splicing during animal development and in disease, impacting thousands of exons in the maturing brain, heart, and muscle. Rbfox proteins have long been known to bind to the RNA sequence GCAUG with high affinity, but just half of Rbfox CLIP peaks contain a GCAUG motif. We incubated recombinant RBFOX2 with over 60,000 transcriptomic sequences to reveal significant binding to several moderate-affinity, non-GCAYG sites at a physiologically relevant range of RBFOX concentrations. We find that many of these “secondary motifs” bind Rbfox robustlyin vivoand that several together can exert regulation comparable to a GCAUG in a trichromatic splicing reporter assay. Furthermore, secondary motifs regulate RNA splicing in neuronal development and in neuronal subtypes where cellular Rbfox concentrations are highest, enabling a second wave of splicing changes as Rbfox levels increase.

MotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/scv_tirosh.Contactisaac.neuhaus@bms.com; kandasamy.ravi@bms.com

The problem of RNA design attempts to construct RNA sequences that perform a predefined biological function, identified by several additional constraints. One of the foremost objective of RNA design is that the designed RNA sequence should adopt a predefined target secondary structure preferentially to any alternative structure, according to a given metrics and folding model. It was observed in several works that some secondary structures are undesignable, i.e. no RNA sequence can fold into the target structure while satisfying some criterion measuring how preferential this folding is compared to alternative conformations.In this paper, we show that the proportion of designable secondary structures decreases exponentially with the size of the target secondary structure, for various popular combinations of energy models and design objectives. This exponential decay is, at least in part, due to the existence of undesignable motifs, which can be generically constructed, and jointly analyzed to yield asymptotic upper-bounds on the number of designable structures.

RNA molecules may experience independent selection pressures on their sequence and (secondary) structure. Structural features then may be preserved without maintaining their exact position along the sequence. In such cases, corresponding base pairs are no longer formed by homologous bases, leading to the incongruent evolutionary conservation of sequence and structure. In order to model this phenomenon, we introduce bi-alignments as a superposition of two alignments: one modeling sequence homology; the other, structural homology. We show that under natural assumptions on the scoring functions, bi-alignments form a special case of 4-way alignments, in which the incongruencies are measured as indels in the pairwise alignment of the two alignment copies. A preliminary survey of the Rfam database suggests that incongruent evolution of RNAs is not a very rare phenomenon.AvailabilityOur software is freely available at https://github.com/s-will/BiAlign

Zika virus (ZIKV) is considered to be an emerging viral outbreak due to its link to diseases like microcephaly, Guillain-Barre Syndrome in human. In this paper, we identify differentially expressed genes (DEGs) using RNA-seq data. In this study, we adopted the RNA-seq analysis pipeline to quantify RNA-seq data into read counts. Our analysis uncovers the significant DEGs which may be involved in the altered biological process somehow. Here, we report the list of significant DEGs, out of which three genes are found to be highly differentially expressed. In addition, our analysis also predicts other moderate DEGs, low DEGs whose differential expression was induced due to ZIKV infections.

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging.Here we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

Alignment of structural RNAs is an important problem with a wide range of applications. Since function is often determined by molecular structure, RNA alignment programs should take into account both sequence and base-pairing information for structural homology identi^Lcation. A number of successful alignment programs are heuristic versions of Sanko^K's optimal algorithm. Most of them require O(n4) run time. This paper describes C++ software, RNAmountAlign, for RNA sequence/structure alignment that runs in O(n3) time and O(n2) space; moreover, our software returns a p-value (transformable to expect value E) based on Karlin-Altschul statistics for local alignment, as well as parameter ^Ltting for local and global alignment. Using incremental mountain height, a representation of structural information computable in cubic time, RNAmountAlign implements quadratic time pairwise local, global and global/semiglobal (query search) alignment using a weighted combination of sequence and structural similarity. RNAmountAlign is capable of performing progressive multiple alignment as well. Benchmarking of RNAmountAlign against LocARNA, LARA, FOLDALIGN, DYNALIGN and STRAL shows that RNAmountAlign has reasonably good accuracy and much faster run time supporting all alignment types., 28 pages, 8 figures, 5 tables, 5 supplementary figures, 3 supplementary tables

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past forty years. While many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges. Here, we describe a new method to compute the entire free energy landscape of secondary structures of RNA resulting from a primary RNA sequence, by combining a polymer physics model for the entropy of pseudoknots with exhaustive enumeration of the set of possible structures. Our polymer physics model can address arbitrarily complex pseudoknots and has only two free loop entropy parameters that correspond to concrete physical quantities, over an order of magnitude fewer than even the sparsest state-of-the-art algorithms. Our model outperforms previously published methods in predicting pseudoknots, while performing on par with current methods in the prediction of non-pseudoknotted structures. For RNA sequences of ~ 45 nucleotides, or ~ 90 with minimal heuristics, the complet–e enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem.

Fatty acids (FAs) constitute a considerable fraction of all lipid molecules with a fundamental role in numerous physiological processes. In animals, the majority of complex lipid molecules are derived from the transformation of FAs through several biochemical pathways. Yet, for FAs to enroll in these pathways they require an activation step. FA activation is catalyzed by the rate limiting action of Acyl-CoA synthases. Several Acyl-CoA enzyme families have been previously described and classified according to the chain length of FAs they process. Here, we address the evolutionary history of the ACSBG gene family which activates, FAs with >16 carbons. Currently, two different ACSBG gene families, ACSBG1 and ACSBG2, are recognized in vertebrates. We provide evidence that a wider and unequal ACSBG gene repertoire is present in vertebrate lineages. We identify a novel ACSBG-like gene lineage which occurs specifically in amphibians, ray finned fishes, coelacanths and cartilaginous fishes named ACSBG3. Also, we show that the ACSBG2 gene lineage duplicated in the Theria ancestor. Our findings, thus offer a far richer understanding on FA activation in vertebrates and provide key insights into the relevance of comparative and functional analysis to perceive physiological differences, namely those related with lipid metabolic pathways. © 2018 Elsevier This work was supported by Project INNOVMAR—Innovation and Sustainability in the Management and Exploitation of Marine Resources (reference NORTE-01-0145-FEDER-000035 , within Research Line NOVELMAR/INSEAFOOD/ECOSERVICES), supported by North Portugal Regional Operational Programme ( NORTE 2020 ), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We acknowledge Fundação para a Ciência e a Tecnologia for the support to EF (SFRH/BD/79305/2011).

Predicting the secondary structure of an RNA sequence with speed and accuracy is useful in many applications such as drug design. The state-of-the-art predictors have a fundamental limitation: they have a run time that scales with the third power of the length of the input sequence, which is slow for longer RNAs and limits the use of secondary structure prediction in genome-wide applications. To address this bottleneck, we designed the first linear-time algorithm for RNA secondary structure prediction, which can be used with both thermodynamic and machine-learned scoring functions. Our algorithm, like previous work, is based on dynamic programming (DP), but with two crucial differences: (a) we incrementally process the sequence in a left-to-right rather than in a bottom-up fashion, and (b) because of this processing, we can further employ beam search pruning to ensure linear runtime in practice (with the cost of exact search). Even though our search is approximate, surprisingly, it results in even higher overall accuracy on a diverse database of se- quences with known structures. In particular, it leads to significantly more accurate predictions on the longest sequence families in that database (16S and 23S Ribosomal RNAs), as well as improved accu- racies for long-range base pairs (500+ nucleotides apart).

RNA promotes liquid-liquid phase separation (LLPS) to build membrane-less compartments in cells. How distinct molecular compositions are established and maintained in these liquid compartments is unknown. Here we report that secondary structure allows mRNAs to self-associate and determines if an mRNA is recruited to or excluded from liquid compartments. The polyQ-protein Whi3 induces conformational changes in RNA structure and generates distinct molecular fluctuations depending on the RNA sequence. These data support a model in which structure-based, RNA-RNA interactions promote assembly of distinct droplets and protein-driven, conformational dynamics of the RNA maintain this identity. Thus, the shape of RNA can promote the formation and coexistence of the diverse array of RNA-rich liquid compartments found in a single cell.One Sentence SummaryIdentity in cellular, phase-separated compartments arises from RNA-RNA complexes encoded by mRNA secondary structures.

Post-transcriptional mechanisms play a predominant role in the control of microRNA (miRNA) production. Recognition of the terminal loop of precursor miRNAs by RNA-binding proteins (RBPs) influences their processing; however, the mechanistic and structural basis for how levels of individual or subsets of miRNAs are regulated is mostly unexplored. We previously described a role for hnRNP A1, an RBP implicated in many aspects of RNA processing, as an auxiliary factor that promotes the Microprocessor-mediated processing of pri-mir-18a. Here, we reveal the mechanistic basis for this stimulatory role of hnRNP A1 by combining integrative structural biology with biochemical and functional assays. We demonstrate that hnRNP A1 forms a 1:1 complex with pri-mir-18a that involves binding of both RNA recognition motifs (RRMs) to cognate RNA sequence motifs in the conserved terminal loop of pri-mir-18a. Terminal loop binding induces an allosteric destabilization of base-pairing in the pri-mir-18a stem that promotes its down-stream processing. Our results highlight terminal loop RNA recognition by RNA-binding proteins as a general principle of miRNA biogenesis and regulation.

Measuring the essentiality of genes is critically important in biology and medicine. Some bioinformatic methods have been developed for this issue but none of them can be applied to long noncoding RNAs (lncRNAs), one big class of biological molecules. Here we developed a computational method, GIC (Gene Importance Calculator), which can predict the essentiality of both protein-coding genes and lncRNAs based on RNA sequence information. For identifying the essentiality of protein-coding genes, GIC is competitive with well-established computational scores. More important, GIC showed a high performance for predicting the essentiality of lncRNAs. In an independent mouse lncRNA dataset, GIC achieved an exciting performance (AUC=0.918). In contrast, the traditional computational methods are not applicable to lncRNAs. As a public web server, GIC is freely available at http://www.cuilab.cn/gic/.

RNA-binding proteins (RBPs) exert many roles in the post-transcriptional regulation of gene expression in eukaryotic cells. However, our understanding of how they recognize their target RNAs in vivo remains limited. In the January 1, 2012, issue of Genes & Development, Patel and colleagues (p. 43–53) provide detailed mechanistic insights into how one of the best-studied RBPs, zipcode-binding protein 1 (ZBP1), recognizes a bipartite RNA sequence element within the β-actin mRNA.

BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across multitudes of species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: 1) identifying truly noncoding genes from de novo reconstructed transcriptomes, and 2) prioritizing hundreds of resulting putative lncRNAs from each sample for downstream experimental interrogation. RESULTS: We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-Sequencing data and further prioritizes lncRNAs by characterizing selective constraint as a proxy for function. Our filtering pipeline is comparable to manual curation efforts and more sensitive than previously published approaches. Further, we develop, for the first time, a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for both sequence and transcript evolution. Our analysis reveals that selection acts in several distinct patterns, and uncovers two notable classes of lncRNAs: one showing strong purifying selection at RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. CONCLUSION: Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we believe should be prioritized for further interrogation. To aid in their analysis we provide the slncky Evolution Browser as a resource for experimentalists.

Because the abundance of functional molecules in RNA sequence space has many unexplored aspects, we compared the outcome of 11 independent selections, performed using the same affinity selection protocol and contiguous randomized regions of 16, 22, 26, 50, 70, and 90 nucleotides. All affinity selections targeted the simplest isoleucine aptamer, an asymmetric internal loop. This loop should be abundant in all selections, so that it can be compared across all experiments. In some cases, two primer sets intended to favor selection of different structures have also been compared. The simplest isoleucine aptamer dominates all selections except with the shortest tract, 16 contiguous randomized nucleotides. Here the isoleucine aptamer cannot be accommodated and no other motif can be selected. Our results suggest an optimum length for selection; surprisingly, both the shortest and the longest randomized tracts make it more difficult to recover the motif. Estimated apparent initial abundances suggest that the simplest isoleucine motif was 20- to 40-fold more frequent in selection with 50- or 70-nucleotide randomized regions than with any other length. Considering primer sets, a pre-formed stable stem within fixed flanking sequences had a five-to 10-fold negative effect on apparent motif abundance at all lengths. Differing random tract lengths also determined the probable motif permutation and the most abundant helix lengths. These data support a significant but lesser role for primer sequences in the outcome of selections.