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In flies, some introns contain internal splice sites that cause ‘recursive splicing’, a multi-step removal of a single intron; this study demonstrates that the scope of this regulatory mechanism is much more extensive in flies than had been appreciated, and provides details about the recursive splicing process. Recursive splicing in insects and vertebrates The mechanisms by which the very longest genes in eukaryotic genomes are accurately processed are poorly understood. It was thought that intron removal generally involved a single excisive step. Later studies showed that, in flies, some introns contain internal splice sites that cause 'recursive splicing', in which single introns are removed 'bit-by-bit' in several sequential splicing reactions. Brenton Graveley and coworkers demonstrate that the scope of this regulatory mechanism is much more extensive in flies than had been appreciated. They identify nearly 200 zero-nucleotide exons in Drosophila that are the products of recursive splicing. Jernej Ule and colleagues identify recursive splicing sites in vertebrates, particularly within long genes encoding proteins that are involved in neuronal development. Analysis of the mechanism of their splicing reveals that such splicing sites can be used to dictate different mRNA isoforms. Recursive splicing is a process in which large introns are removed in multiple steps by re-splicing at ratchet points—5′ splice sites recreated after splicing 1 . Recursive splicing was first identified in the Drosophila Ultrabithorax ( Ubx ) gene 1 and only three additional Drosophila genes have since been experimentally shown to undergo recursive splicing 2 , 3 . Here we identify 197 zero nucleotide exon ratchet points in 130 introns of 115 Drosophila genes from total RNA sequencing data generated from developmental time points, dissected tissues and cultured cells. The sequential nature of recursive splicing was confirmed by identification of lariat introns generated by splicing to and from the ratchet points. We also show that recursive splicing is a constitutive process, that depletion of U2AF inhibits recursive splicing, and that the sequence and function of ratchet points are evolutionarily conserved in Drosophila . Finally, we identify four recursively spliced human genes, one of which is also recursively spliced in Drosophila . Together, these results indicate that recursive splicing is commonly used in Drosophila , occurs in humans, and provides insight into the mechanisms by which some large introns are removed.

Allele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants (GVs) mediating post-transcriptional regulation. Recently, genome-wide detection of in vivo binding of RNA-binding proteins is greatly facilitated by the enhanced crosslinking and immunoprecipitation (eCLIP) method. We developed a new computational approach, called BEAPR, to identify allele-specific binding (ASB) events in eCLIP-Seq data. BEAPR takes into account crosslinking-induced sequence propensity and variations between replicated experiments. Using simulated and actual data, we show that BEAPR largely outperforms often-used count analysis methods. Importantly, BEAPR overcomes the inherent overdispersion problem of these methods. Complemented by experimental validations, we demonstrate that the application of BEAPR to ENCODE eCLIP-Seq data of 154 proteins helps to predict functional GVs that alter splicing or mRNA abundance. Moreover, many GVs with ASB patterns have known disease relevance. Overall, BEAPR is an effective method that helps to address the outstanding challenge of functional interpretation of GVs. Differential binding of RNA-binding proteins mediated by genetic variants (GVs) can influence posttranscriptional regulation. Here, the authors develop BEAPR, a computational approach to identify allele-specific binding events in eCLIP-Seq data.

Adenosine-to-inosine (A-to-I) editing, mediated by the ADAR enzymes, diversifies the transcriptome by altering RNA sequences. Recent studies reported global changes in RNA editing in disease and development. Such widespread editing variations necessitate an improved understanding of the regulatory mechanisms of RNA editing. Here, we study the roles of >200 RNA-binding proteins (RBPs) in mediating RNA editing in two human cell lines. Using RNA-sequencing and global protein-RNA binding data, we identify a number of RBPs as key regulators of A-to-I editing. These RBPs, such as TDP-43, DROSHA, NF45/90 and Ro60, mediate editing through various mechanisms including regulation of ADAR1 expression, interaction with ADAR1, and binding to Alu elements. We highlight that editing regulation by Ro60 is consistent with the global up-regulation of RNA editing in systemic lupus erythematosus. Additionally, most key editing regulators act in a cell type-specific manner. Together, our work provides insights for the regulatory mechanisms of RNA editing. Giovanni Quinones-Valdez et al. examined the role of over 200 RNA-binding proteins in mediating A-to-I RNA editing. They identified several RNA-binding proteins that regulate ADAR1 expression, interaction, or binding with Alu elements in a cell type-specific manner.

Many proteins regulate the expression of genes by binding to specific regions encoded in the genome 1 . Here we introduce a new data set of RNA elements in the human genome that are recognized by RNA-binding proteins (RBPs), generated as part of the Encyclopedia of DNA Elements (ENCODE) project phase III. This class of regulatory elements functions only when transcribed into RNA, as they serve as the binding sites for RBPs that control post-transcriptional processes such as splicing, cleavage and polyadenylation, and the editing, localization, stability and translation of mRNAs. We describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. Integrative analyses using five assays identify RBP binding sites on RNA and chromatin in vivo, the in vitro binding preferences of RBPs, the function of RBP binding sites and the subcellular localization of RBPs, producing 1,223 replicated data sets for 356 RBPs. We describe the spectrum of RBP binding throughout the transcriptome and the connections between these interactions and various aspects of RNA biology, including RNA stability, splicing regulation and RNA localization. These data expand the catalogue of functional elements encoded in the human genome by the addition of a large set of elements that function at the RNA level by interacting with RBPs. A combination of five assays is used to produce a catalogue of RNA elements to which RNA-binding proteins bind in human cells.

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( http://bioconductor.org/packages/rnaseqcomp ). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.

The development and deployment of giant magnetoimpedance (GMI) sensors have been significantly hampered by their limited sensitivity to weak magnetic fields and pronounced thermal drift phenomena, both of which are intricately linked to the microstructural properties of the sensor core material, typically composed of metallic glass microwires (MGMWs). Herein, we successfully fabricated an ultra-stable Co-based MGMW with a high GMI effect through a novel multi-step stress-Joule coupled annealing (MS-JCA) technique. The Co-based MGMW showcases a significantly improved GMI effect with an unprecedented impedance change rate of 939%, coupled with an enhanced magnetic field sensitivity of 734%/Oe. In addition, the MS-JCA process ensures the GMI sensor retains exceptional stability during thermal drift measurements over a span of 20 h, characterized by a minimal signal fluctuation ratio of merely 0.59%. Notably, the ultra-stability of the GMI sensor arises from the ultra-stable energy state of the MGMWs following MS-JCA. Our findings offer a compelling strategy for significantly enhancing both the performance and stability of GMI sensors, thereby establishing a solid technical foundation for their broader application in weak magnetic detection.

The evolution of magnetic domains in metallic glass microwires (MGWs) plays a critical role in minimizing magnetic noise in giant magnetoimpedance (GMI) sensors. However, the underlying mechanisms of domain magnetization and reversal in MGWs remain poorly understood. Here, we report a discovered magnetic domain motion mechanism in MGWs: the single-mode uniform magnetization of circumferential domains. The increased atomic ordering promotes a more uniform alignment of magnetic moments, leading to the formation of circumferential magnetic domains. This suppresses the reversal of axial domains and lowers magnetic noise. These findings provide important insights for the design and development of low-noise GMI sensors.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03067-w

[...]we realized that we ran the eXpress submission differently from the other methods for this particular dataset. Table 1 Summarized metrics for analyzed pipelines based on an experimental dataset Method SD low SD medium SD high NE (K = 1) NN (K = 1) TxDiff low TxDiff medium TxDiff high deFC low deFC medium deFC high pAUC Cufflinks 0.62 (0.002) 0.26 (0.001) 0.12 (0.000) 0.08 0.70 0.31 (0.007) 0.08 (0.002) 0.03 (0.001) 2.65 (0.022) 2.25 (0.047) 1.01 (0.024) 0.77 eXpress 0.53 (0.002) 0.22 (0.001) 0.10 (0.000) 0.07 0.72 0.24 (0.006) 0.06 (0.002) 0.02 (0.001) 2.86 (0.022) 2.21 (0.048) 1.00 (0.019) 0.79 Flux Capacitor 0.62 (0.003) 0.57 (0.003) 0.18 (0.001) 0.10 0.73 0.42 (0.008) 0.15 (0.004) 0.07 (0.003) 2.62 (0.024) 2.40 (0.050) 1.01 (0.025) 0.75 kallisto 0.53 (0.002) 0.24 (0.001) 0.12 (0.000) 0.09 0.64 0.28 (0.007) 0.08 (0.002) 0.03 (0.0001 2.36 (0.024) 2.06 (0.045) 1.03 (0.024) 0.76 RSEM 0.54 (0.002) 0.22 (0.001) 0.11 (0.000) 0.06 0.73 0.39 (0.008) 0.07 (0.002) 0.02 (0.001) 2.72 (0.022) 2.22 (0.048) 1.03 (0.026) 0.78 Sailfish 0.46 (0.002) 0.25 (0.001) 0.13 (0.000) 0.08 0.60 0.27 (0.006) 0.08 (0.002) 0.04 (0.001) 2.30 (0.023) 2.08 (0.044) 0.97 (0.022) 0.77 Salmon 0.46 (0.002) 0.23 (0.001) 0.12 (0.000) 0.08 0.65 0.29 (0.007) 0.07 (0.002) 0.04 (0.001) 2.30 (0.024) 2.06 (0.045) 1.03 (0.022) 0.77 Metrics for single cell lines are averaged for both cell lines, except standard deviation is the square root of average squares. Columns 2–4 shows median standard deviation on three transcript abundance levels; column 5 shows proportions of discordant calls when K = 1; column 6 shows proportions of both non-expressed when K = 1; columns 7–9 show the mean proportion differences of transcripts in genes only having two annotated transcripts based on three transcript abundance levels; columns 10–12 show median log fold changes of true differentially expressed genes based on three abundance levels; column 13 shows standardized partial area under the curve for differential expression of genes. pAUC partial area under the receiver operating characteristic curve The comparative figures for GM12878 change (panel A Figures 3, 4, 5, 6 and Additional file 1: