Recognition Complex Research Articles

Task design and assignment of full-text generation on mass Chinese historical archives in digital humanities

PurposeThe purpose of this paper is to explore the task design and assignment of full-text generation on mass Chinese historical archives (CHAs) by crowdsourcing, with special attention paid to how to best divide full-text generation tasks into smaller ones assigned to crowdsourced volunteers and to improve the digitization of mass CHAs and the data-oriented processing of the digital humanities.Design/methodology/approachThis paper starts from the complexities of character recognition of mass CHAs, takes Sheng Xuanhuai archives crowdsourcing project of Shanghai Library as a case study, and makes use of the theories of archival science, including diplomatics of Chinese archival documents, and the historical approach of Chinese archival traditions as the theoretical basis and analysis methods. The results are generated through the comprehensive research.FindingsThis paper points out that volunteer tasks of full-text generation include transcription, punctuation, proofreading, metadata description, segmentation, and attribute annotation in digital humanities and provides a metadata element set for volunteers to use in creating or revising metadata descriptions and also provides an attribute tag set. The two sets can be used across the humanities to construct overall observations about texts and the archives of which they are a part. Along these lines, this paper presents significant insights for application in outlining the principles, methods, activities, and procedures of crowdsourced full-text generation for mass CHAs.Originality/valueThis study is the first to explore and identify the effective design and allocation of tasks for crowdsourced volunteers completing full-text generation on CHAs in digital humanities.

Numerical sensitive data recognition based on hybrid gene expression programming for active distribution networks

Complex and flexible access mode, and frequent data interaction bring about large security risks to data transmission for active distribution networks. How to ensure data security is critical to the safe and stable operation of active distribution networks. Traditional methods, like access control, data encryption, and text filtering based on intelligent algorithms, are difficult to ensure the security of dynamically increased and high-dimensional numerical data transmission in active distribution networks. In this paper, we first propose a rough feature selection algorithm based on the average importance measurement (RFS-AIM) to simplify the complexity of data recognition. Then, we propose a sensitive data recognition function mining algorithm based on RFS-AIM and improved gene expression programming (SDR-IGEP) where population update operation is constructed by chromosome similarity based on the Jaccard coefficient. The operation avoids local convergence of the gene express programming by increasing individual diversity in the new population. Finally, we present a new incremental mining algorithm for a sensitive data recognition function based on global function fitting (ISDR-GFF) by using a grain granulation model for incremental datasets. The experimental results on IEEE benchmark datasets and real datasets show that the algorithms proposed in this paper outperform the state-of-the-art algorithms in terms of the average running time, precision, recall, F1 index, accuracy, specificity and speedup on all experimental datasets.

Complete Profiling of Methyl-CpG-Binding Domains for Combinations of Cytosine Modifications at CpG Dinucleotides Reveals Differential Read-out in Normal and Rett-Associated States

5-Methylcytosine (mC) exists in CpG dinucleotides of mammalian DNA and plays key roles in chromatin regulation during development and disease. As a main regulatory pathway, fully methylated CpG are recognized by methyl-CpG-binding domain (MBD) proteins that act in concert with chromatin remodelers, histone deacetylases and methyltransferases to trigger transcriptional downregulation. In turn, MBD mutations can alter CpG binding, and in case of the MBD protein MeCP2 can cause the neurological disorder Rett syndrome (RTT). An additional layer of complexity in CpG recognition is added by ten-eleven-translocation (TET) dioxygenases that oxidize mC to 5-hydroxymethyl-, 5-formyl- and 5-carboxylcytosine, giving rise to fifteen possible combinations of cytosine modifications in the two CpG strands. We report a comprehensive, comparative interaction analysis of the human MBD proteins MeCP2, MBD1, MBD2, MBD3, and MBD4 with all CpG combinations and observe individual preferences of each MBD for distinct combinations. In addition, we profile four MeCP2 RTT mutants and reveal that although interactions to methylated CpGs are similarly affected by the mutations, interactions to oxidized mC combinations are differentially affected. These findings argue for a complex interplay between local TET activity/processivity and CpG recognition by MBDs, with potential consequences for the transcriptional landscape in normal and RTT states.

Deep neural learning techniques with long short-term memory for gesture recognition

Gesture recognition is a kind of biometric which has assumed great significance in the field of computer vision for communicating information through human activities. To recognize the various gestures and achieve efficient classification, an efficient computational machine learning technique is required. The Shift Invariant Convolutional Deep Structured Neural Learning with Long Short-Term Memory (SICDSNL–LSTM) and Bivariate Fully Recurrent Deep Neural Network with Long Short-Term Memory (BFRDNN–LSTM) have been introduced for classifying human activities with efficient accuracy and minimal time complexity. The SICDSNL–LSTM technique collects gesture data (a kind of biometric) from the dataset and gives it to the input layers of Shift Invariant Convolutional Deep Structured Neural Learning. The SICDSNL–LSTM technique uses two hidden layers for performing regression and classification. In the regression process, dice similarity is used for measuring the relationship between data and output classes. In the second process, the input data are classified into dissimilar classes for each time step using LSTM unit with soft-step activation function. The soft-step activation function uses ‘forget gate’ for removing the less significant data from the cell state. This is also used to make a decision to display the output at a particular time step and to remove other class results. Then, LSTM output is given to the output layers, and convolutional deep neural learning is performed to classify the gesture. Based on the classification results, human activity and gesture are recognized with high accuracy. The BFRDNN–LSTM technique also performs regression in the first hidden layers using bivariate correlation to find relationship between data and classes. The LSTM unit in BFRDNN–LSTM technique uses Gaussian activation function in the second hidden layers for categorizing incoming data into various classes at each time step. In this proposed BFRDNN–LSTM method, fully recurrent deep neural network utilizes gradient descent function to minimize the error rate at the output layers and to increase the accuracy of the gesture recognition. Both SICDSNL–LSTM and BFRDNN–LSTM techniques automatically learn the features and the data to minimize time complexity in gesture recognition. Experimental evaluation is carried out using factors such as gesture recognition accuracy, false-positive rate and time complexity with a number of data.

Posture Recognition Technology Based on Kinect

Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

An Essential and Cell-Cycle-Dependent ORC Dimerization Cycle Regulates Eukaryotic Chromosomal DNA Replication

Eukaryotic DNA replication licensing is a prerequisite for, and plays a role in, regulating genome duplication that occurs exactly once per cell cycle. ORC (origin recognition complex) binds to and marks replication origins throughout the cell cycle and loads other replication-initiation proteins onto replication origins to form pre-replicative complexes (pre-RCs), completing replication licensing. However, how an asymmetric single-heterohexameric ORC structure loads the symmetric MCM (minichromosome maintenance) double hexamers is controversial, and importantly, it remains unknown when and how ORC proteins associate with the newly replicated origins to protect them from invasion by histones. Here, we report an essential and cell-cycle-dependent ORC "dimerization cycle" that plays three fundamental roles in the regulation of DNA replication: providing a symmetric platform to load the symmetric pre-RCs, marking and protecting the nascent sister replication origins for the next licensing, and playing a crucial role to prevent origin re-licensing within the same cell cycle.

Rationalizing the Transport of Trojan Horse Compounds for Crossing the Outer Membrane of Gram- Bacteria

One of the challenges of modern medicine is to find efficient antibiotics to counteract infections. The main concern is represented by Gram negative species, where antibiotics have to cross the outer membrane for reaching their targets. Among the possible strategy for resolving the permeation of antibiotics is the use of TonB dependent transporters, such as those expressed to capture iron from the environment. Existing siderophore molecules enriched by anti-infective properties (Trojan-Horse candidates) can be transported efficiently inside the cell. The high-resolution structures obtained recently using X-ray crystallography have open the way to a more detailed knowledge of transport mechanism. Thanks to the new structures, we have applied molecular simulations in combination with NMR spectroscopy to investigate at molecular level the formation of the molecule-ion complex in solution, the binding of the complex to the transporter and finally its diffusion along the interior of the transporter. In general the ion-siderophore complex in solution is the one recognized by the transporter, in a precise recognition pocket. Upon the recognition, the binding can provide a unique allosteric signal appearing to activate other regions of the transporters in order to control the self-transport of the ligand. This effect upon ligand binding promotes a novel idea that the internal diffusion does not require a large conformational change of the transporter, as suggested earlier. We obtained detailed molecular data to understand how to rationalize siderophores able to use transporters to cross the outer membrane. The three fundamental steps are: formation of the complex in solution, binding and recognition on the transporter, and internal diffusion. Only the first two steps seem to play a key role in the transport and both depend on subtle interactions ion-siderophore-transporter, modulated by selecting precise chemical groups.

Evolution of the Eukaryotic Origin Recognition Complex

The Origin Recognition Complex (ORC) is a six-subunit complex that is highly conserved from yeasts to humans. However, the mechanism for finding its binding sites diverged from a base-specific mechanism in yeasts to a site-specific but base-agnostic mechanism in humans. Based on the atomic model of the yeast ORC bound to replication origin DNA, the 19-aa base-specificity determinant that interacts with the invariant Ts in the consensus sequence of the yeast origin DNA is missing in metazoans. We studied the physiological effect of removing this crucial element of ORC in yeast and found that the altered ORC∗ behaved more like a human ORC.

Tissue-Specific DNA Replication Defects in Drosophila melanogaster Caused by a Meier-Gorlin Syndrome Mutation in Orc4.

Meier-Gorlin syndrome is a rare recessive disorder characterized by a number of distinct tissue-specific developmental defects. Genes encoding members of the origin recognition complex (ORC) and additional proteins essential for DNA replication (CDC6, CDT1, GMNN, CDC45, MCM5, and DONSON) are mutated in individuals diagnosed with MGS. The essential role of ORC is to license origins during the G1 phase of the cell cycle, but ORC has also been implicated in several nonreplicative functions. Because of its essential role in DNA replication, ORC is required for every cell division during development. Thus, it is unclear how the Meier-Gorlin syndrome mutations in genes encoding ORC lead to the tissue-specific defects associated with the disease. To begin to address these issues, we used Cas9-mediated genome engineering to generate a Drosophila melanogaster model of individuals carrying a specific Meier-Gorlin syndrome mutation in ORC4 along with control strains. Together these strains provide the first metazoan model for an MGS mutation in which the mutation was engineered at the endogenous locus along with precisely defined control strains. Flies homozygous for the engineered MGS allele reach adulthood, but with several tissue-specific defects. Genetic analysis revealed that this Orc4 allele was a hypomorph. Mutant females were sterile, and phenotypic analyses suggested that defects in DNA replication was an underlying cause. By leveraging the well-studied Drosophila system, we provide evidence that a disease-causing mutation in Orc4 disrupts DNA replication, and we propose that in individuals with MGS defects arise preferentially in tissues with a high-replication demand.

Origin Recognition Complex (ORC) Evolution Is Influenced by Global Gene Duplication/Loss Patterns in Eukaryotic Genomes.

The conservation of orthologs of most subunits of the origin recognition complex (ORC) has served to propose that the whole complex is common to all eukaryotes. However, various uncertainties have arisen concerning ORC subunit composition in a variety of lineages. Also, it is unclear whether the ancestral diversification of ORC in eukaryotes was accompanied by the neofunctionalization of some subunits, for example, role of ORC1 in centriole homeostasis. We have addressed these questions by reconstructing the distribution and evolutionary history of ORC1-5/CDC6 in a taxon-rich eukaryotic data set. First, we identified ORC subunits previously undetected in divergent lineages, which allowed us to propose a series of parsimonious scenarios for the origin of this multiprotein complex. Contrary to previous expectations, we found a global tendency in eukaryotes to increase or decrease the number of subunits as a consequence of genome duplications or streamlining, respectively. Interestingly, parasites show significantly lower number of subunits than free-living eukaryotes, especially those with the lowest genome size and gene content metrics. We also investigated the evolutionary origin of the ORC1 role in centriole homeostasis mediated by the PACT region in human cells. In particular, we tested the consequences of reducing ORC1 levels in the centriole-containing green alga Chlamydomonas reinhardtii. We found that the proportion of centrioles to flagella and nuclei was not dramatically affected. This, together with the PACT region not being significantly more conserved in centriole-bearing eukaryotes, supports the notion that this neofunctionalization of ORC1 would be a recent acquisition rather than an ancestral eukaryotic feature.

Identification of Highest-Affinity Binding Sites of Yeast Transcription Factor Families.

Transcription factors (TFs) play a crucial role in controlling key cellular processes and responding to the environment. Yeast is a single-cell fungal organism that is a vital biological model organism for studying transcription and translation in basic biology. The transcriptional control process of yeast cells has been extensively calculated and studied using traditional methods and high-throughput technologies. However, the identities of transcription factors that regulate major functional categories of genes remain unknown. Due to the avalanche of biological data in the post-genomic era, it is an urgent need to develop automated computational methods to enable accurate identification of efficient transcription factor binding sites from the large number of candidates. In this paper, we analyzed high-resolution DNA-binding profiles and motifs for TFs, covering all possible contiguous 8-mers. First, we divided all 8-mer motifs into 16 various categories and selected all sorts of samples from each category by setting the threshold of E-score. Then, we employed five feature representation methods. Also, we adopted a total of four feature selection methods to filter out useless features. Finally, we used Extreme Gradient Boosting (XGBoost) as our base classifier and then utilized the one-vs-rest tactics to build 16 binary classifiers to solve this multiclassification problem. In the experiment, our method achieved the best performance with an overall accuracy of 79.72% and Mathew's correlation coefficient of 0.77. We found the similarity relationship among each category from different TF families and obtained sequence motif schematic diagrams via multiple sequence alignment. The complexity of DNA recognition may act as an important role in the evolution of gene regulation. Source codes are available at https://github.com/guofei-tju/tfbs.

Two novel ELM-based stacking deep models focused on image recognition

Extreme learning machine (ELM) and its variants have been widely used in the field of object recognition and other complex classification tasks. Traditional deep learning architectures like Convolutional Neural Network (CNN) are capable of extracting high-level features, which are the key for the models to make right decisions. However, traditional deep architectures are confronted with solving a tough, non-convex optimization problem, which is a time-consuming process. In this paper, we propose two hierarchical models, i.e., Random Recursive Constrained ELM (R2CELM) and Random Recursive Local- Receptive-Fields-Based ELM (R2ELM-LRF), which are constructed by stacking with CELM or ELM-LRF, respectively. Besides, inspired by the stacking generalization philosophy, random projection and kernelization are incorporated as their constitutive elements. R2CELM and R2ELM-LRF not only fully inherit the merits of ELM, but also take advantage of the superiority of CELM and ELM-LRF in the field of image recognition, respectively. The essence of CELM is to constrain the weight vectors from the input layer to the hidden layer to be consistent with the directions from one class to another class, while ELM-LRF is adept at exploiting the local structures in images through many local receptive fields. In the empirical results, R2CELM and R2ELM-LRF demonstrate their better performance in testing accuracy on the six benchmark image recognition datasets, compared with their basic learners and other state-of-the-art algorithms. Moreover, the proposed two deep ELM models need less training time when compared with traditional Deep Neural Network (DNN) based models.

PARylation During Transcription: Insights into the Fine-Tuning Mechanism and Regulation.

Transcription is a multistep, tightly regulated process. During transcription initiation, promoter recognition and pre-initiation complex (PIC) formation take place, in which dynamic recruitment or exchange of transcription activators occur. The precise coordination of the recruitment and removal of transcription factors, as well as chromatin structural changes, are mediated by post-translational modifications (PTMs). Poly(ADP-ribose) polymerases (PARPs) are key players in this process, since they can modulate DNA-binding activities of specific transcription factors through poly-ADP-ribosylation (PARylation). PARylation can regulate the transcription at three different levels: (1) by directly affecting the recruitment of specific transcription factors, (2) by triggering chromatin structural changes during initiation and as a response to cellular stresses, or (3) by post-transcriptionally modulating the stability and degradation of specific mRNAs. In this review, we principally focus on these steps and summarise the recent findings, demonstrating the mechanisms through which PARylation plays a potential regulatory role during transcription and DNA repair.

A simple fluorescent probe for detection of Ag+ and Cd2+ and its Cd2+ complex for sequential recognition of S2.

In this study, we designed and synthesized a simple probe 2-(8-((8-methoxyquinolin-2-yl)methoxy)quinolin-2-yl)benzo[d]thiazole (DQT) for detection of Ag+ and Cd2+ in a CH3OH/HEPES (9 : 1 v/v, pH = 7.30) buffer system. Its structure was characterized by NMR, ESI-HR-MS and DFT calculations, and its fluorescence performance was also investigated. Probe DQT showed fluorescence quenching in response to Ag+ and Cd2+ with low detection limits of 0.42 μM and 0.26 μM, respectively. Importantly, the complexation of the probe with Cd2+ resulted in a red shift from blue to green, making it possible to detect Ag+ and Cd2+ by the naked eye under an ultraviolet lamp. The DQT-Cd2+ complex could be used for sequential recognition of S2−. The recovery response could be repeated 3 times by alternate addition of Cd2+ and S2−. A filter paper strip test further demonstrated the potential of probe DQT as a convenient and rapid assay.

A SAR Target Recognition Based on Guided Reconstruction and Weighted Norm-Constrained Deep Belief Network

The deep learning has some new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). By introducing different constraints, deep belief network (DBN) has become apply to SAR target recognition recent years, but the existing DBN algorithms have some questions including the high training epochs, low recognition rate and complex structure. Therefore, an algorithm based on guided reconstruction and weighted norm-constrained DBN is proposed. Firstly, in order to reduce the dimension of the image output feature, increase the speed of preprocessing, generate a one-dimensional image vector and normalized, a SAR target classification algorithm with two-scale fusion character based on guided filter reconstruction algorithm is introduced. Then, the sparse feature extraction of SAR image is carried out by weighted norm-constrained DBN. By the regularization constraint of the probability distribution, the algorithm can minimize the joint probability distribution of visual layer and hidden layer by samples. The low-dimensional feature is further improved based on the generalized optimization of norm-constrained RBM. Finally, by a regularized Softmax which can classify the targets and obtain output results. The experimental results show that the SAR target recognition algorithm based on the guided reconstruction and weighted constrained deep confidence network not only improves the target recognition performance and generalization ability, but also reduces the output feature dimension and network training times, and the recognition performance of the algorithm is further improved.

Epigenetic regulation affects gene amplification in Drosophila development.

In Drosophila melanogaster, in response to developmental transcription factors, and by repeated initiation of DNA replication of four chorion genes, ovarian follicle cells, form an onion skin-type structure at the replication origins. The DNA replication machinery is conserved from yeast to humans. Subunits of the origin recognition complex (ORC) is comprised of Orc1, Orc2, and Cdc6 genes. While mutations of Orc1 and Orc2 and not Cdc6can be lethal, overexpression of these genes lead to female sterility. Ecdysone, is a steroidal prohormone of the major insect molting hormone 20-hydroxyecdysone that in Drosophila, triggers molting, metamorphosis, and oogenesis. To this end, we identified several ecdysone receptor (EcR) binding sites around gene amplification loci. We also found that H3K4 was trimethylated at chorion gene amplification origins, but not at the act1 locus. Female mutants overexpressing Lsd1 (a dimethyl histone H3K4 demethylase) or Lid (a trimethyl histone H3K4 demethylase), but not a Lid mutant, were sterile. The data suggest that ecdysone signaling determines which origin initiates DNA replication and contributes to the development. Screening strategies using Drosophila offer the opportunity for development of drugs that reduce gene amplification and alter histone modification associated with epigenetic effects.

Novel candidate biomarkers of origin recognition complex 1, 5 and 6 for survival surveillance in patients with hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) has high morbidity and mortality and lacks effective biomarkers for early diagnosis and survival surveillance. Origin recognition complex (ORC), consisting of ORC1-6 isoforms, was examined to assess the potential significance of ORC isoforms for HCC prognosis.Methods: Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases were used to examine differential isoform expression, stage-specific expression, calculate Pearson correlations and perform survival analysis. A human protein atlas database was utilized to evaluate the protein expression of ORCs in liver tissue. The cBioPortal database was used to assess isoform mutations and the survival significance of ORCs in HCC. Cytoscape software was employed to construct gene ontologies, metabolic pathways and gene-gene interaction networks.Results: Differential expression analysis indicated that ORC1 and ORC3-6 were highly expressed in tumor tissues in the Oncomine and GEPIA databases, while ORC2 was not. All the ORCs were showed positive and statistically significant correlations with each other (all P<0.001). ORC1-2 and ORC4-6 expressions were associated with disease stages I-IV (all P<0.05), but ORC3 was not. Survival analysis found that ORC1 and ORC4-6 expressions were associated with overall survival (OS), and ORC1-3 and ORC5-6 expression were associated with recurrence-free survival (RFS; all P<0.05). In addition, low expression of these ORC genes consistently indicated better prognosis compared with high expression. Protein expression analysis revealed that ORC1 and ORC3-6 were expressed in normal liver tissues, whereas ORC2 was not. Enrichment analysis indicated that ORCs were associated with DNA metabolic process, sequence-specific DNA binding and were involved in DNA replication, cell cycle, E2F-enabled inhibition of pre-replication complex formation and G1/S transition.Conclusions: Differentially expressed ORC1, 5 and 6 are candidate biomarkers for survival prediction and recurrence surveillance in HCC.

Su(var)2-10 and the SUMO Pathway Link piRNA-Guided Target Recognition to Chromatin Silencing.

Regulation of transcription is the main mechanism responsible for precise control of gene expression. Whereas the majority of transcriptional regulation ismediated by DNA-binding transcription factors that bind to regulatory gene regions, an elegant alternative strategy employs small RNA guides, Piwi-interacting RNAs (piRNAs) to identify targets of transcriptional repression. Here, we show that inDrosophila the small ubiquitin-like protein SUMOand the SUMO E3 ligase Su(var)2-10 are required for piRNA-guided deposition of repressive chromatin marks and transcriptional silencing of piRNA targets. Su(var)2-10 links the piRNA-guided target recognition complex to the silencing effector by binding the piRNA/Piwi complex and inducing SUMO-dependent recruitment of the SetDB1/Wde histone methyltransferase effector. We propose that in Drosophila, the nuclear piRNA pathway has co-opted a conserved mechanism of SUMO-dependent recruitment of the SetDB1/Wde chromatin modifier to confer repression of genomic parasites.

Origin recognition complex subunit 1 regulates cell growth and metastasis in glioma by altering activation of ERK and JNK signaling pathway

Origin recognition complex subunit 1(ORC1) is reported to be closely associated with the cell cycle. However, studies on the role of ORC1 in glioma remain undefined. The aim of the present study was to determine whether ORC1 affects cell migration, invasion, apoptosis, and proliferation and to explore the possible underlying mechanism. GEO database analysis indicated that ORC1 was significantly upregulated in glioma, while Gene set enrichment analysis (GSEA) analysis indicated that ORC1 primarily regulated the cell cycle and affects apoptotic signaling pathways. Analysis of protein-protein interaction (PPI) and gene ontology (GO) to further study the relevant mechanisms revealed that the function of the interaction between proteins and ORC1 was primarily concentrated in the regulation of cell cycle, and apoptosis played a critical role in the whole PPI network. Western blot assay and RT-PCR assay indicated that ORC1 was significantly upregulated in glioma tissues. Western blot assay and RT-PCR indicated that ORC1 was significantly upregulated in glioma cell lines. Cell migration, invasion, apoptosis, and proliferation were detected using Transwell and wound healing assays, flow cytometry, colony formation, and CCK8, respectively. Furthermore, OCR1 inhibition reduced invasion and migration, promoted cell apoptosis. In addition, OCR1 overexpression promoted cell proliferation and induced G2 phase arrest. Moreover, OCR1 downregulation suppressed activation of the ERK/JNK signaling pathway. The effects of ORC1 on biological processes were reversed by ERK and JNK inhibitors. These results indicate that ORC1 could be a novel prognostic marker of glioma via the activation of the ERK/JNK signaling pathway.

Splicing Kinetics and Coordination Revealed by Direct Nascent RNA Sequencing through Nanopores.

Understanding how splicing events are coordinated across numerous introns in metazoan RNA transcripts requires quantitative analyses of transient RNA processing events in living cells. We developed nanopore analysis of co-transcriptional processing (nano-COP), in which nascent RNAs are directly sequenced through nanopores, exposing the dynamics and patterns of RNA splicing without biases introduced by amplification. Long nano-COP reads reveal that, in human and Drosophila cells, splicing occurs after RNA polymerase II transcribes several kilobases of pre-mRNA, suggesting that metazoan splicing transpires distally from the transcription machinery. Inhibition of the branch-site recognition complex SF3B rapidly diminished global co-transcriptional splicing. We found that splicing order does not strictly follow the order of transcription and is associated with cis-acting elements, alternative splicing, and RNA-binding factors. Further, neighboring introns in human cells tend to be spliced concurrently, implying that splicing of these introns occurs cooperatively. Thus, nano-COP unveils the organizational complexity of RNA processing.