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Hundreds of RNAs are enriched in the projections of neuronal cells. For the vast majority of them, though, the sequence elements that regulate their localization are unknown. To identify RNA elements capable of directing transcripts to neurites, we deployed a massively parallel reporter assay that tested the localization regulatory ability of thousands of sequence fragments drawn from endogenous mouse 3′ UTRs. We identified peaks of regulatory activity within several 3′ UTRs and found that sequences derived from these peaks were both necessary and sufficient for RNA localization to neurites in mouse and human neuronal cells. The localization elements were enriched in adenosine and guanosine residues. They were at least tens to hundreds of nucleotides long as shortening of two identified elements led to significantly reduced activity. Using RNA affinity purification and mass spectrometry, we found that the RNA-binding protein Unk was associated with the localization elements. Depletion of Unk in cells reduced the ability of the elements to drive RNAs to neurites, indicating a functional requirement for Unk in their trafficking. These results provide a framework for the unbiased, high-throughput identification of RNA elements and mechanisms that govern transcript localization in neurons.

Autogenous interactions between mRNAs and the proteins they encode are implicated in cellular feedback-loop regulation, but their extent and mechanistic foundation are unclear. It was recently hypothesized that such interactions may be common, reflecting the role of intrinsic nucleobase–amino acid affinities in shaping the genetic code's structure. Here we analyze a comprehensive set of CLIP-seq experiments involving multiple protocols and report on widespread autogenous interactions across different organisms. Specifically, 230 of 341 (67%) studied RNA-binding proteins (RBPs) interact with their own mRNAs, with a heavy enrichment among high-confidence hits and a preference for coding sequence binding. We account for different confounding variables, including physical (overexpression and proximity during translation), methodological (difference in CLIP protocols, peak callers and cell types) and statistical (treatment of null backgrounds). In particular, we demonstrate a high statistical significance of autogenous interactions by sampling null distributions of fixed-margin interaction matrices. Furthermore, we study the dependence of autogenous binding on the presence of RNA-binding motifs and structured domains in RBPs. Finally, we show that intrinsic nucleobase–amino acid affinities favor co-aligned binding between mRNA coding regions and the proteins they encode. Our results suggest a central role for autogenous interactions in RBP regulation and support the possibility of a fundamental connection between coding and binding.

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

It is currently unknown how many RNA transcripts are able to induce degradation of microRNAs (miRNA) via the mechanism known as target-directed miRNA degradation (TDMD). We developed TDMDfinder, a computational pipeline that identifies ‘high confidence’ TDMD interactions in the Human and Mouse transcriptomes by combining sequence alignment and feature selection approaches. Our predictions suggested that TDMD is widespread, with potentially every miRNA controlled by endogenous targets. We experimentally tested 37 TDMDfinder predictions, of which 17 showed TDMD effects as measured by RT-qPCR and small RNA sequencing, linking the miR-17, miR-19, miR-30, miR-221, miR-26 and miR-23 families to novel endogenous TDMDs. In some cases, TDMD was found to affect different members of the same miRNA family selectively. Features like complementarity to the miRNA 3′ region, bulge size and hybridization energy appeared to be the main factors determining sensitivity. Computational analyses performed using the multiomic TCGA platform substantiated the involvement of many TDMD transcripts in human cancer and highlighted 36 highly significant interactions, suggesting TDMD as a new potential oncogenic mechanism. In conclusion, TDMDfinder provides the first inventory of bona fide human and mouse TDMDs. Available as a free webtool, TDMDfinder allows users to search for any TDMD interaction of interest by customizing its selection criteria.

The Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) stores raw sequencing data and alignment information to enhance reproducibility and facilitate new discoveries through data analysis. Here we note changes in storage designed to increase access and highlight analyses that augment metadata with taxonomic insight to help users select data. In addition, we present three unanticipated applications of taxonomic analysis.

Alternative polyadenylation (APA) has been widely recognized as a crucial step during the post-transcriptional regulation of eukaryotic genes. Recent studies have demonstrated that APA exerts key regulatory roles in many biological processes and often occurs in a tissue- and cell-type-specific manner. However, to our knowledge, there is no database incorporating information about APA at the cell-type level. Single-cell RNA-seq is a rapidly evolving and powerful tool that enable APA analysis at the cell-type level. Here, we present a comprehensive resource, scAPAatlas (http://www.bioailab.com:3838/scAPAatlas), for exploring APA across different cell types, and interpreting potential biological functions. Based on the curated scRNA-seq data from 24 human and 25 mouse normal tissues, we systematically identified cell-type-specific APA events for different cell types and examined the correlations between APA and gene expression level. We also estimated the crosstalk between cell-type-specific APA events and microRNAs or RNA-binding proteins. A user-friendly web interface has been constructed to support browsing, searching and visualizing multi-layer information of cell-type-specific APA events. Overall, scAPAatlas, incorporating a rich resource for exploration of APA at the cell-type level, will greatly help researchers chart cell type with APA and elucidate the biological functions of APA.

Liquid–liquid phase separation (LLPS) is critical for assembling membraneless organelles (MLOs) such as nucleoli, P-bodies, and stress granules, which are involved in various physiological processes and pathological conditions. While the critical role of RNA in the formation and the maintenance of MLOs is increasingly appreciated, there is still a lack of specific resources for LLPS-related RNAs. Here, we presented RPS (http://rps.renlab.org), a comprehensive database of LLPS-related RNAs in 20 distinct biomolecular condensates from eukaryotes and viruses. Currently, RPS contains 21,613 LLPS-related RNAs with three different evidence types, including ‘Reviewed’, ‘High-throughput’ and ‘Predicted’. RPS provides extensive annotations of LLPS-associated RNA properties, including sequence features, RNA structures, RNA–protein/RNA–RNA interactions, and RNA modifications. Moreover, RPS also provides comprehensive disease annotations to help users to explore the relationship between LLPS and disease. The user-friendly web interface of RPS allows users to access the data efficiently. In summary, we believe that RPS will serve as a valuable platform to study the role of RNA in LLPS and further improve our understanding of the biological functions of LLPS.

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 relies on sRNAs to coordinate expression of metabolic and virulence factors to colonize the host. Here, we focus on the sRNA, named MavR (metabolism and virulence regulator), that is conserved among pathogenic Enterobacteriaceae. MavR is constitutively expressed under in vitro conditions that promote EHEC virulence gene expression. Using MS2-affinity purification coupled with RNA sequencing, the eutR transcript was identified as a putative target of MavR. EutR is a transcription factor that promotes expression of genes required for ethanolamine metabolism as well as virulence factors important for host colonization. MavR binds to the eutR coding sequence to protect the eutR transcript from RNase E-mediated degradation. Ultimately, MavR promotes EutR expression and in turn ethanolamine utilization and ethanolamine-dependent growth. RNAseq analyses revealed that MavR also affected expression of genes important for other metabolic pathways, motility, oxidative stress and attaching and effacing lesion formation, which contribute to EHEC colonization of the gastrointestinal tract. In support of the idea that MavR-dependent gene expression affects fitness during infection, deletion of mavR resulted in significant (∼10- to 100-fold) attenuation in colonization of the mammalian intestine. Altogether, these studies reveal an important, extensive, and robust phenotype for a bacterial sRNA in host-pathogen interactions.

Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.

Quantitative comparisons of RNA levels from different samples can lead to new biological understanding if they are able to distinguish biological variation from variable sample preparation. These challenges are pronounced in comparisons that require complex biochemical manipulations (e.g. isolating polysomes to study translation). Here, we present Transcript Regulation Identified by Labeling with Nucleoside Analogues in Cell Culture (TILAC), an internally controlled approach for quantitative comparisons of RNA content. TILAC uses two metabolic labels, 4-thiouridine (s4U) and 6-thioguanosine (s6G), to differentially label RNAs in cells, allowing experimental and control samples to be pooled prior to downstream biochemical manipulations. TILAC leverages nucleoside recoding chemistry to generate characteristic sequencing signatures for each label and uses statistical modeling to compare the abundance of RNA transcripts between samples. We verified the performance of TILAC in transcriptome-scale experiments involving RNA polymerase II inhibition and heat shock. We then applied TILAC to quantify changes in mRNA association with actively translating ribosomes during sodium arsenite stress and discovered a set of transcripts that are translationally upregulated, including MCM2 and DDX5. TILAC is broadly applicable to uncover differences between samples leading to improved biological insights.

With the rapid growth of synthetic messenger RNA (mRNA)-based therapeutics and vaccines, the development of analytical tools for characterization of long, complex RNAs has become essential. Tandem liquid chromatography–mass spectrometry (LC–MS/MS) permits direct assessment of the mRNA primary sequence and modifications thereof without conversion to cDNA or amplification. It relies upon digestion of mRNA with site-specific endoribonucleases to generate pools of short oligonucleotides that are then amenable to MS-based sequence analysis. Here, we showed that the uridine-specific human endoribonuclease hRNase 4 improves mRNA sequence coverage, in comparison with the benchmark enzyme RNase T1, by producing a larger population of uniquely mappable cleavage products. We deployed hRNase 4 to characterize mRNAs fully substituted with 1-methylpseudouridine (m1Ψ) or 5-methoxyuridine (mo5U), as well as mRNAs selectively depleted of uridine–two key strategies to reduce synthetic mRNA immunogenicity. Lastly, we demonstrated that hRNase 4 enables direct assessment of the 5′ cap incorporation into in vitro transcribed mRNA. Collectively, this study highlights the power of hRNase 4 to interrogate mRNA sequence, identity, and modifications by LC–MS/MS.

Although reverse-transcriptase (RT) enzymes are critical reagents for research and biotechnology, their mechanical properties are not well understood. In particular, we know little about their relative speed and response to structural obstacles in the template. Commercial retroviral RTs stop at many positions along mixed sequence templates, resulting in truncated cDNA products that complicate downstream analysis. By contrast, group II intron-encoded RTs appear to copy long RNAs with high processivity and minimal stops. However, their speed, consistency and pausing behavior have not been explored. Here, we analyze RT velocity as the enzyme moves through heterogeneous sequences and structures that are embedded within a long noncoding RNA transcript. We observe that heterogeneities in the template are highly disruptive to primer extension by retroviral RTs. However, sequence composition and template structure have negligible effects on behavior of group II intron RTs, such as MarathonRT (MRT). Indeed, MRT copies long RNAs in a single pass, and displays synchronized primer extension at a constant speed of 25 nt/sec. In addition, it passes through stable RNA structural motifs without perturbation of velocity. Taken together, the results demonstrate that consistent, robust translocative behavior is a hallmark of group II intron-encoded RTs, some of which operate at high velocity.

RNA–RNA inter- and intramolecular interactions are fundamental for numerous biological processes. While there are reasonable approaches to map RNA secondary structures genome-wide, understanding how different RNAs interact to carry out their regulatory functions requires mapping of intermolecular base pairs. Recently, different strategies to detect RNA–RNA duplexes in living cells, so called direct duplex detection (DDD) methods, have been developed. Common to all is the Psoralen-mediated in vivo RNA crosslinking followed by RNA Proximity Ligation to join the two interacting RNA strands. Sequencing of the RNA via classical RNA-seq and subsequent specialised bioinformatic analyses the result in the prediction of inter- and intramolecular RNA–RNA interactions. Existing approaches adapt standard RNA-seq analysis pipelines, but often neglect inherent features of RNA–RNA interactions that are useful for filtering and statistical assessment. Here we present RNAnue, a general pipeline for the inference of RNA–RNA interactions from DDD experiments that takes into account hybridisation potential and statistical significance to improve prediction accuracy. We applied RNAnue to data from different DDD studies and compared our results to those of the original methods. This showed that RNAnue performs better in terms of quantity and quality of predictions.

RNA structures play a fundamental role in nearly every aspect of cellular physiology and pathology. Gaining insights into the functions of RNA molecules requires accurate predictions of RNA secondary structures. However, the existing thermodynamic folding models remain less accurate than desired, even when chemical probing data, such as selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reactivities, are used as restraints. Unlike most SHAPE-directed algorithms that only consider SHAPE restraints for base pairing, we extract two-dimensional structural features encoded in SHAPE data and establish robust relationships between characteristic SHAPE patterns and loop motifs of various types (hairpin, internal, and bulge) and lengths (2–11 nucleotides). Such characteristic SHAPE patterns are closely related to the sugar pucker conformations of loop residues. Based on these patterns, we propose a computational method, SHAPELoop, which refines the predicted results of the existing methods, thereby further improving their prediction accuracy. In addition, SHAPELoop can provide information about local or global structural rearrangements (including pseudoknots) and help researchers to easily test their hypothesized secondary structures.

Biomarker-driven trials hold promise for therapeutic development in chronic diseases, such as muscular dystrophy. Myotonic dystrophy type 1 (DM1) involves RNA toxicity, where transcripts containing expanded CUG-repeats (CUGexp) accumulate in nuclear foci and sequester splicing factors in the Muscleblind-like (Mbnl) family. Oligonucleotide therapies to mitigate RNA toxicity have emerged but reliable measures of target engagement are needed. Here we examined muscle transcriptomes in mouse models of DM1 and found that CUGexp expression or Mbnl gene deletion cause similar dysregulation of alternative splicing. We selected 35 dysregulated exons for further study by targeted RNA sequencing. Across a spectrum of mouse models, the individual splice events and a composite index derived from all events showed a graded response to decrements of Mbnl or increments of CUGexp. Antisense oligonucleotides caused prompt reduction of CUGexp RNA and parallel correction of the splicing index, followed by subsequent elimination of myotonia. These results suggest that targeted splice sequencing may provide a sensitive and reliable way to assess therapeutic impact in DM1.

Chromatin properties are regulated by complex networks of epigenome modifications. Currently, it is unclear how these modifications interact and if they control downstream effects such as gene expression. We employed promiscuous chromatin binding of a zinc finger fused catalytic domain of DNMT3A to introduce DNA methylation in HEK293 cells at many CpG islands (CGIs) and systematically investigated the dynamics of the introduced DNA methylation and the consequent changes of the epigenome network. We observed efficient methylation at thousands of CGIs, but it was unstable at about 90% of them, highlighting the power of genome-wide molecular processes that protect CGIs against DNA methylation. Partially stable methylation was observed at about 1000 CGIs, which showed enrichment in H3K27me3. Globally, the introduced DNA methylation strongly correlated with a decrease in gene expression indicating a direct effect. Similarly, global but transient reductions in H3K4me3 and H3K27ac were observed after DNA methylation but no changes were found for H3K9me3 and H3K36me3. Our data provide a global and time-resolved view on the network of epigenome modifications, their connections with DNA methylation and the responses triggered by artificial DNA methylation revealing a direct repressive effect of DNA methylation in CGIs on H3K4me3, histone acetylation, and gene expression.

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell–cell interactions and relevant ligand–receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

European Union [765492 to M.H.]; Spanish Government [AGL2017-88702-C2-2-R to M.H.]; Chair 'Doctors Galera-Requena in cancer stem cell research' (to J.A.M.); Tromsoforskningsstiftelse (TFS) [20 SG BF 'MIRevolution' to B.F.]; Stichting Cancer Center Amsterdam [CCA2021-9-77 to C.G]; TKI-Health Holland ['AQrate' project to C.G. and M.P.]. This publication is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 765492., The NCBI Sequence Read Archive currently hosts microRNA sequencing data for over 800 different species, evidencing the existence of a broad taxonomic distribution in the field of small RNA research. Simultaneously, the number of samples per miRNA-seq study continues to increase resulting in a vast amount of data that requires accurate, fast and user-friendly analysis methods. Since the previous release of sRNAtoolbox in 2019, 55 000 sRNAbench jobs have been submitted which has motivated many improvements in its usability and the scope of the underlying annotation database. With this update, users can upload an unlimited number of samples or import them from Google Drive, Dropbox or URLs. Micro- and small RNA profiling can now be carried out using high-confidence Metazoan and plant specific databases, MirGeneDB and PmiREN respectively, together with genome assemblies and libraries from 441 Ensembl species. The new results page includes straightforward sample annotation to allow downstream differential expression analysis with sRNAde. Unassigned reads can also be explored by means of a new tool that performsmapping to microbial references, which can reveal contamination events or biologically meaningful findings as we describe in the example. sRNAtoolbox is available at: https://arn.ugr.es/srnatoolbox/., European Commission 765492, Spanish Government, European Commission AGL2017-88702-C2-2-R, Chair 'Doctors Galera-Requena in cancer stem cell research', Stichting Cancer Center Amsterdam CCA2021-9-77, Tromsoforskningsstiftelse (TFS) ['MIRevolution'] 20 SG BF, TKI-Health Holland ['AQrate' project]

With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called ‘dropout’ events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories: model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects: (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest. It is now freely accessible via https://server.wei-group.net/scIMC/.

Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.

The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For example, there can be cell types not well distinguished by clustering because they largely share the global structure, such as the anterior primitive streak and mid primitive streak cells. If one searches differentially expressed genes (DEGs) solely based on clustering, marker genes for distinguishing these types will be missed. Moreover, clustering depends on many parameters and can often be subjective to manual decisions. To overcome these limitations, we propose MarcoPolo, a method that identifies informative DEGs independently of prior clustering. MarcoPolo sorts out genes by evaluating if the distributions are bimodal, if similar expression patterns are observed in other genes, and if the expressing cells are proximal in a low-dimensional space. Using real datasets with FACS-purified cell labels, we demonstrate that MarcoPolo recovers marker genes better than competing methods. Notably, MarcoPolo finds key genes that can distinguish cell types that are not distinguishable by the standard clustering. MarcoPolo is built in a convenient software package that provides analysis results in an HTML file.

Metabolic labeling of RNAs with noncanonical nucleosides that are chemically active, followed by chemoselective conjugation with imaging probes or enrichment tags, has emerged as a powerful method for studying RNA transcription and degradation in eukaryotes. However, metabolic RNA labeling is not applicable for prokaryotes, in which the complexity and distinctness of gene regulation largely remain to be explored. Here, we report 2′-deoxy-2′-azidoguanosine (AzG) as a noncanonical nucleoside compatible with metabolic labeling of bacterial RNAs. With AzG, we develop AIR-seq (azidonucleoside-incorporated RNA sequencing), which enables genome-wide analysis of transcription upon heat stress in Escherichia coli. Furthermore, AIR-seq coupled with pulse-chase labeling allows for global analysis of bacterial RNA degradation. Finally, we demonstrate that RNAs of mouse gut microbiotas can be metabolically labeled with AzG in living animals. The AzG-enabled metabolic RNA labeling should find broad applications in studying RNA biology in various bacterial species.

Splicing is an essential step of RNA processing for multi-exon genes, in which introns are removed from a precursor RNA, thereby producing mature RNAs containing splice junctions. Here, we develope the RJunBase (www.RJunBase.org), a web-accessible database of three types of RNA splice junctions (linear, back-splice, and fusion junctions) that are derived from RNA-seq data of non-cancerous and cancerous tissues. The RJunBase aims to integrate and characterize all RNA splice junctions of both healthy or pathological human cells and tissues. This new database facilitates the visualization of the gene-level splicing pattern and the junction-level expression profile, as well as the demonstration of unannotated and tumor-specific junctions. The first release of RJunBase contains 682 017 linear junctions, 225 949 back-splice junctions and 34 733 fusion junctions across 18 084 non-cancerous and 11 540 cancerous samples. RJunBase can aid researchers in discovering new splicing-associated targets and provide insights into the identification and assessment of potential neoepitopes for cancer treatment.

Ribosome profiling (also known as Ribo-seq) has become an important technique to investigate changes in translation across a wide variety of contexts. Ribo-seq data not only provides the abundance of ribosomes bound to transcripts, but also positional information across transcripts that could be indicative of differences in translation dynamics between conditions. While many computational tools exist for the analysis of Ribo-seq data, including those that assess differences in translational efficiency between conditions, no tool currently exists for rigorous test of the pattern differences in ribosome footprint. In this paper we propose a novel approach together with an R package, RiboDiPA, for Differential PPattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to the existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and two benchmark data sets, we illustrate that RiboDiPA can not only uncover meaningful global translational differences between conditions, but also the detailed differential ribosome binding patterns to a single codon resolution.

The Bioinformation and DDBJ Center (DDBJ Center, https://www.ddbj.nig.ac.jp) provides databases that capture, preserve and disseminate diverse biological data to support research in the life sciences. This center collects nucleotide sequences with annotations, raw sequencing data, and alignment information from high-throughput sequencing platforms, and study and sample information, in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute (EBI). This collaborative framework is known as the International Nucleotide Sequence Database Collaboration (INSDC). In collaboration with the National Bioscience Database Center (NBDC), the DDBJ Center also provides a controlled-access database, the Japanese Genotype–phenotype Archive (JGA), which archives and distributes human genotype and phenotype data, requiring authorized access. The NBDC formulates guidelines and policies for sharing human data and reviews data submission and use applications. To streamline all of the processes at NBDC and JGA, we have integrated the two systems by introducing a unified login platform with a group structure in September 2020. In addition to the public databases, the DDBJ Center provides a computer resource, the NIG supercomputer, for domestic researchers to analyze large-scale genomic data. This report describes updates to the services of the DDBJ Center, focusing on the NBDC and JGA system enhancements.

Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports >40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.

The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

Genomic enhancer elements regulate gene expression programs important for neuronal fate and function and are implicated in brain disease states. Enhancers undergo bidirectional transcription to generate non-coding enhancer RNAs (eRNAs). However, eRNA function remains controversial. Here, we combined Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-Seq) and RNA-Seq datasets from three distinct neuronal culture systems in two activity states, enabling genome-wide enhancer identification and prediction of putative enhancer–gene pairs based on correlation of transcriptional output. Notably, stimulus-dependent enhancer transcription preceded mRNA induction, and CRISPR-based activation of eRNA synthesis increased mRNA at paired genes, functionally validating enhancer–gene predictions. Focusing on enhancers surrounding the Fos gene, we report that targeted eRNA manipulation bidirectionally modulates Fos mRNA, and that Fos eRNAs directly interact with the histone acetyltransferase domain of the enhancer-linked transcriptional co-activator CREB-binding protein (CBP). Together, these results highlight the unique role of eRNAs in neuronal gene regulation and demonstrate that eRNAs can be used to identify putative target genes.

RNA molecules play key roles in all living cells. Knowledge of the structural characteristics of RNA molecules allows for a better understanding of the mechanisms of their action. RNA chemical probing allows us to study the susceptibility of nucleotides to chemical modification, and the information obtained can be used to guide secondary structure prediction. These experimental results can be analyzed using various computational tools, which, however, requires additional, tedious steps (e.g., further normalization of the reactivities and visualization of the results), for which there are no fully automated methods. Here, we introduce RNAProbe, a web server that facilitates normalization, analysis, and visualization of the low-pass SHAPE, DMS and CMCT probing results with the modification sites detected by capillary electrophoresis. RNAProbe automatically analyzes chemical probing output data and turns tedious manual work into a one-minute assignment. RNAProbe performs normalization based on a well-established protocol, utilizes recognized secondary structure prediction methods, and generates high-quality images with structure representations and reactivity heatmaps. It summarizes the results in the form of a spreadsheet, which can be used for comparative analyses between experiments. Results of predictions with normalized reactivities are also collected in text files, providing interoperability with bioinformatics workflows. RNAProbe is available at https://rnaprobe.genesilico.pl.

Cyclin-dependent kinase 12 (CDK12) phosphorylates the carboxyl-terminal domain (CTD) of RNA polymerase II (pol II) but its roles in transcription beyond the expression of DNA damage response genes remain unclear. Here, we have used TT-seq and mNET-seq to monitor the direct effects of rapid CDK12 inhibition on transcription activity and CTD phosphorylation in human cells. CDK12 inhibition causes a genome-wide defect in transcription elongation and a global reduction of CTD Ser2 and Ser5 phosphorylation. The elongation defect is explained by the loss of the elongation factors LEO1 and CDC73, part of PAF1 complex, and SPT6 from the newly-elongating pol II. Our results indicate that CDK12 is a general activator of pol II transcription elongation and indicate that it targets both Ser2 and Ser5 residues of the pol II CTD.

Natural antisense transcript-derived small interfering RNAs (nat-siRNAs) are a class of functional small RNA (sRNA) that have been found in both plant and animals kingdoms. In plants, these sRNAs have been shown to suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex (RISC) to their sequence-specific mRNA target(s). Current computational tools for classification of nat-siRNAs are limited in number and can be computationally infeasible to use. In addition, current methods do not provide any indication of the function of the predicted nat-siRNAs. Here, we present a new software pipeline, called NATpare, for prediction and functional analysis of nat-siRNAs using sRNA and degradome sequencing data. Based on our benchmarking in multiple plant species, NATpare substantially reduces the time required to perform prediction with minimal resource requirements allowing for comprehensive analysis of nat-siRNAs in larger and more complex organisms for the first time. We then exemplify the use of NATpare by identifying tissue and stress specific nat-siRNAs in multiple Arabidopsis thaliana datasets.

Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regulate the amount of protein translated from each mRNA, thus allowing a tight temporal regulation of translation or buffering of protein levels from bursty transcription. Besides, mRNA localization performs a variety of additional roles like long-distance signaling, facilitating assembly of protein complexes and coordination of developmental processes. Here, we describe a novel machine-learning based tool, mRNALoc, to predict five sub-cellular locations of eukaryotic mRNAs using cDNA/mRNA sequences. During five fold cross-validations, the maximum overall accuracy was 65.19, 75.36, 67.10, 99.70 and 73.59% for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. Assessment on independent datasets revealed the prediction accuracies of 58.10, 69.23, 64.55, 96.88 and 69.35% for extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The corresponding values of AUC were 0.76, 0.75, 0.70, 0.98 and 0.74 for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The mRNALoc standalone software and web-server are freely available for academic use under GNU GPL at http://proteininformatics.org/mkumar/mrnaloc.

Subcellular organization of RNAs and proteins is critical for cell function, but we still lack global maps and conceptual frameworks for how these molecules are localized in cells and tissues. Here, we introduce ATLAS-Seq, which generates transcriptomes and proteomes from detergent-free tissue lysates fractionated across a sucrose gradient. Proteomic analysis of fractions confirmed separation of subcellular compartments. Unexpectedly, RNAs tended to co-sediment with other RNAs in similar protein complexes, cellular compartments, or with similar biological functions. With the exception of those encoding secreted proteins, most RNAs sedimented differently than their encoded protein counterparts. To identify RNA binding proteins potentially driving these patterns, we correlated their sedimentation profiles to all RNAs, confirming known interactions and predicting new associations. Hundreds of alternative RNA isoforms exhibited distinct sedimentation patterns across the gradient, despite sharing most of their coding sequence. These observations suggest that transcriptomes can be organized into networks of co-segregating mRNAs encoding functionally related proteins and provide insights into the establishment and maintenance of subcellular organization.