PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.

It is vital to investigate the complex mechanisms underlying tumors to better understand cancer and develop effective treatments. Metabolic abnormalities and clinical phenotypes can serve as essential biomarkers for diagnosing this challenging disease. Additionally, genetic alterations provide profound insights into the fundamental aspects of cancer. This study introduces Cancer-Alterome, a literature-mined dataset that focuses on the regulatory events of an organism's biological processes or clinical phenotypes caused by genetic alterations. By proposing and leveraging a text-mining pipeline, we identify 16,681 thousand of regulatory events records encompassing 21K genes, 157K genetic alterations and 154K downstream bio-concepts, extracted from 4,354K pan-cancer literature. The resulting dataset empowers a multifaceted investigation of cancer pathology, enabling the meticulous tracking of relevant literature support. Its potential applications extend to evidence-based medicine and precision medicine, yielding valuable insights for further advancements in cancer research.

Defect Detection in Steel Using a Hybrid Attention Network.

Defect detection in steel surface focuses on accurately identifying and precisely locating defects on the surface of steel materials. Methods of defect detection with deep learning have gained significant attention in research. Existing algorithms can achieve satisfactory results, but the accuracy of defect detection still needs to be improved. Aiming at this issue, a hybrid attention network is proposed in this paper. Firstly, a CBAM attention module is used to enhance the model's ability to learn effective features. Secondly, an adaptively spatial feature fusion (ASFF) module is used to improve the accuracy by extracting multi-scale information of defects. Finally, the CIOU algorithm is introduced to optimize the training loss of the baseline model. The experimental results show that the performance of our method in this work is superior on the NEU-DET dataset, with an 8.34% improvement in mAP. Compared with major algorithms of object detection such as SSD, EfficientNet, YOLOV3, and YOLOV5, the mAP was improved by 16.36%, 41.68%, 20.79%, and 13.96%, respectively. This demonstrates that the mAP of our proposed method is higher than other major algorithms.

Romboutsia lituseburensis JCM1404 supplementation ameliorated endothelial function via gut microbiota modulation and lipid metabolisms alterations in obese rats.

Our previous study revealed moderate-intensity exercise improved endothelial function associated with decreasing Romboutsia in rats on a high-fat diet. However, whether Romboutsia influences endothelial function remains unclear. The aim of this study was to determine the effects of Romboutsia lituseburensis JCM1404 on the vascular endothelium of rats under standard diet (SD) or high-fat diet (HFD). Romboutsia lituseburensis JCM1404 had a better improvement effect on endothelial function under HFD groups, while no significant effect on small-intestinal and blood vessel morphology. HFD significantly decreased villus height of small intestine and increased outer diameter and media thickness of the vascular tissue. After the treatments by R. lituseburensis JCM1404, the expression of claudin5 was increased in the HFD groups. Romboutsia lituseburensis JCM1404 was found to increase alpha diversity in the SD groups, with an increase in beta diversity in the HFD groups. The relative abundance of Romboutsia and Clostridium_sensu_stricto_1 was decreased significantly in both diet groups after R. lituseburensis JCM1404 intervention. The functions of human diseases and endocrine and metabolic diseases significantly downregulated in the HFD groups by Tax4Fun analysis. Furthermore, we found Romboutsia was significantly associated with bile acids, triglycerides, amino acids and derivatives and organic acids and derivatives in the SD groups, while Romboutsia was significantly associated with triglycerides and free fatty acid in the HFD groups. Romboutsia lituseburensis JCM1404 significantly upregulated several metabolism-related pathways by KEGG analysis in the HFD groups, including glycerolipid metabolism, cholesterol metabolism, regulation of lipolysis in adipocytes, insulin resistance, fat digestion and absorption, thermogenesis. Overall, R. lituseburensis JCM1404 supplementation ameliorated endothelial function via gut microbiota modulation and lipid metabolisms alterations in obese rats.

FoxO3 Regulates the Progress and Development of Aging and Aging-Related Diseases.

Aging is an inevitable risk factor for many diseases, including cardiovascular diseases, neurodegenerative diseases, cancer, and diabetes. Investigation into the molecular mechanisms involved in aging and longevity will benefit the treatment of age-dependent diseases and the development of preventative medicine for agingrelated diseases. Current evidence has revealed that FoxO3, encoding the transcription factor (FoxO)3, a key transcription factor that integrates different stimuli in the intrinsic and extrinsic pathways and is involved in cell differentiation, protein homeostasis, stress resistance and stem cell status, plays a regulatory role in longevity and in age-related diseases. However, the precise mechanisms by which the FoxO3 transcription factor modulates aging and promotes longevity have been unclear until now. Here, we provide a brief overview of the mechanisms by which FoxO3 mediates signaling in pathways involved in aging and aging-related diseases, as well as the current knowledge on the role of the FoxO3 transcription factor in the human lifespan and its clinical prospects. Ultimately, we conclude that FoxO3 signaling pathways, including upstream and downstream molecules, may be underlying therapeutic targets in aging and age-related diseases.

Hierarchical Sampling for the Visualization of Large Scale-Free Graphs.

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.

Aerobic Exercise Prevents Arterial Stiffness and Attenuates Hyperexcitation of Sympathetic Nerves in Perivascular Adipose Tissue of Mice after Transverse Aortic Constriction.

We aimed to investigate the efficacy of exercise on preventing arterial stiffness and the potential role of sympathetic nerves within perivascular adipose tissue (PVAT) in pressure-overload-induced heart failure (HF) mice. Eight-week-old male mice were subjected to sham operation (SHAM), transverse aortic constriction-sedentary (TAC-SE), and transverse aortic constriction-exercise (TAC-EX) groups. Six weeks of aerobic exercise training was performed using a treadmill. Arterial stiffness was determined by measuring the elastic modulus. The elastic and collagen fibers of the aorta and sympathetic nerve distribution in PVAT were observed. Circulating noradrenaline (NE), expressions of ß3-adrenergic receptor (ß3-AR), and adiponectin in PVAT were quantified. During the recovery of cardiac function by aerobic exercise, thoracic aortic collagen elastic modulus (CEM) and collagen fibers were significantly decreased (p < 0.05, TAC-SE vs. TAC-EX), and elastin elastic modulus (EEM) was significantly increased (p < 0.05, TAC-SE vs. TAC-EX). Circulating NE and sympathetic nerve distribution in PVAT were significantly decreased (p < 0.05, TAC-SE vs. TAC-EX). The expression of ß3-AR was significantly reduced (p < 0.05, TAC-SE vs. TAC-EX), and adiponectin was significantly increased (p < 0.05, TAC-SE vs. TAC-EX) in PVAT. Regular aerobic exercise can effectively prevent arterial stiffness and extracellular matrix (ECM) remodeling in the developmental course of HF, during which sympathetic innervation and adiponectin within PVAT might be strongly implicated.

High-quality gene/disease embedding in a multi-relational heterogeneous graph after a joint matrix/tensor decomposition.

MOTIVATION: Node embedding of biological entity network has been widely investigated for the downstream application scenarios. To embed full semantics of gene and disease, a multi-relational heterogeneous graph is considered in a scenario where uni-relation between gene/disease and other heterogeneous entities are abundant while multi-relation between gene and disease is relatively sparse. After introducing this novel graph format, it is illuminative to design a specific data integration algorithm to fully capture the graph information and bring embeddings with high quality. RESULTS: First, a typical multi-relational triple dataset was introduced, which carried significant association between gene and disease. Second, we curated all human genes and diseases in seven mainstream datasets and constructed a large-scale gene-disease network, which compromising 163,024 nodes and 25,265,607 edges, and relates to 27,165 genes, 2,665 diseases, 15,067 chemicals, 108,023 mutations, 2,363 pathways, and 7.732 phenotypes. Third, we proposed a Joint Decomposition of Heterogeneous Matrix and Tensor (JDHMT) model, which integrated all heterogeneous data resources and obtained embedding for each gene or disease. Forth, a visualized intrinsic evaluation was performed, which investigated the embeddings in terms of interpretable data clustering. Furthermore, an extrinsic evaluation was performed in the form of linking prediction. Both intrinsic and extrinsic evaluation results showed that JDHMT model outperformed other eleven state-of-the-art (SOTA) methods which are under relation-learning, proximity-preserving or message-passing paradigms. Finally, the constructed gene-disease network, embedding results and codes were made available. DATA AND CODES AVAILABILITY: The constructed massive gene-disease network is available at: https://hzaubionlp.com/heterogeneous-biological-network/. The codes are available at: https://github.com/bionlp-hzau/JDHMT.

LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19.

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition.

Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (NEN) are two common tasks aiming at identifying and linking biologically important entities such as genes or gene products mentioned in the literature to biological databases. In this paper, we present an updated version of OryzaGP, a gene and protein dataset for rice species created to help natural language processing (NLP) tools in processing NER and NEN tasks. To create the dataset, we selected more than 15,000 abstracts associated with articles previously curated for rice genes. We developed four dictionaries of gene and protein names associated with database identifiers. We used these dictionaries to annotate the dataset. We also annotated the dataset using pre-trained NLP models. Finally, we analysed the annotation results and discussed how to improve OryzaGP.

A Novel Metric to Quantify the Effect of Pathway Enrichment Evaluation With Respect to Biomedical Text-Mined Terms: Development and Feasibility Study.

BACKGROUND: Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation. OBJECTIVE: The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect. METHODS: Four biomedical text mining methods were selected to represent natural language processing methods on drug-related gene mining. Subsequently, a pathway enrichment experiment was performed by using the mined genes, and a series of inverse pathway frequency (IPF) metrics was proposed accordingly to evaluate the effect of pathway enrichment. Thereafter, 7 IPF metrics and traditional P value metrics were compared in simulation experiments to test the robustness of the proposed metrics. RESULTS: IPF metrics were evaluated in a case study of rapamycin-related gene set. By applying the best IPF metrics in a pathway enrichment simulation test, a novel discovery of drug efficacy of rapamycin for breast cancer was replicated from the data chosen prior to the year 2000. Our findings show the effectiveness of the best IPF metric in support of knowledge discovery in new drug use. Further, the mechanism underlying the drug-disease association was visualized by Cytoscape. CONCLUSIONS: The results of this study suggest the effectiveness of the proposed IPF metrics in pathway enrichment evaluation as well as its application in drug use discovery.

Bridging heterogeneous mutation data to enhance disease gene discovery.

Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a 'Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)' pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass significant test but represent supportive evidences of functional biological process in literature. Eventually, we applied GDAMDB in Alzheimer's disease (AD) and predicted 79 AD-associated genes. Besides, 12 of them from the original GWAS, 60 of them are supported to be AD-related by other GWAS or literature report, and rest of them are newly predicted genes. Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

Oxidative stress by Ca2+ overload is critical for phosphate-induced vascular calcification.

Hyperphosphatemia is the primary risk factor for vascular calcification, which is closely associated with cardiovascular morbidity and mortality. Recent evidence showed that oxidative stress by high inorganic phosphate (Pi) mediates calcific changes in vascular smooth muscle cells (VSMCs). However, intracellular signaling responsible for Pi-induced oxidative stress remains unclear. Here, we investigated molecular mechanisms of Pi-induced oxidative stress related with intracellular Ca2+ ([Ca2+]i) disturbance, which is critical for calcification of VSMCs. VSMCs isolated from rat thoracic aorta or A7r5 cells were incubated with high Pi-containing medium. Extracellular signal-regulated kinase (ERK) and mammalian target of rapamycin were activated by high Pi that was required for vascular calcification. High Pi upregulated expressions of type III sodium-phosphate cotransporters PiT-1 and -2 and stimulated their trafficking to the plasma membrane. Interestingly, high Pi increased [Ca2+]i exclusively dependent on extracellular Na+ and Ca2+ as well as PiT-1/2 abundance. Furthermore, high-Pi induced plasma membrane depolarization mediated by PiT-1/2. Pretreatment with verapamil, as a voltage-gated Ca2+ channel (VGCC) blocker, inhibited Pi-induced [Ca2+]i elevation, oxidative stress, ERK activation, and osteogenic differentiation. These protective effects were reiterated by extracellular Ca2+-free condition, intracellular Ca2+ chelation, or suppression of oxidative stress. Mitochondrial superoxide scavenger also effectively abrogated ERK activation and osteogenic differentiation of VSMCs by high Pi. Taking all these together, we suggest that high Pi activates depolarization-triggered Ca2+ influx via VGCC, and subsequent [Ca2+]i increase elicits oxidative stress and osteogenic differentiation. PiT-1/2 mediates Pi-induced [Ca2+]i overload and oxidative stress but in turn, PiT-1/2 is upregulated by consequences of these alterations.NEW & NOTEWORTHY The novel findings of this study are type III sodium-phosphate cotransporters PiT-1 and -2-dependent depolarization by high Pi, leading to Ca2+ entry via voltage-gated Ca2+ channels in vascular smooth muscle cells. Cytosolic Ca2+ increase and subsequent oxidative stress are indispensable for osteogenic differentiation and calcification. In addition, plasmalemmal abundance of PiT-1/2 relies on Ca2+ overload and oxidative stress, establishing a positive feedback loop. Identification of mechanistic components of a vicious cycle could provide novel therapeutic strategies against vascular calcification in hyperphosphatemic patients.

Hybrid phenotype mining method for investigating off-target protein and underlying side effects of anti-tumor immunotherapy.

BACKGROUND: It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and brought significant improvement in cancer management. However, on the other hand, immune checkpoints blockade (ICB) by monoclonal antiboties may cause common and severe adverse reactions (ADRs), the cause of which remains largely undetermined. We hypothesize that ICB-agents may induce adverse reactions through off-target protein interactions, similar to the ADR-causing off-target effects of small molecules. In this study, we propose a hybrid phenotype mining approach which integrates molecular level information and provides new mechanistic insights for ICB-associated ADRs. METHODS: We trained a conditional random fields model on the TAC 2017 benchmark training data, then used it to extract all drug-centric phenotypes for the five anti-PD-1/PD-L1 drugs from the drug labels of the DailyMed database. Proteins with structure similar to the drugs were obtained by using BlastP, and the gene targets of drugs were obtained from the STRING database. The target-centric phenotypes were extracted from the human phenotype ontology database. Finally, a screening module was designed to investigate off-target proteins, by making use of gene ontology analysis and pathway analysis. RESULTS: Eventually, through the cross-analysis of the drug and target gene phenotypes, the off-target effect caused by the mutation of gene BTK was found, and the candidate side-effect off-target site was analyzed. CONCLUSIONS: This research provided a hybrid method of biomedical natural language processing and bioinformatics to investigate the off-target-based mechanism of ICB treatment. The method can also be applied for the investigation of ADRs related to other large molecule drugs.

A multimodal deep learning framework for predicting drug-drug interaction events.

MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. RESULTS: In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug-drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision-recall curve of 0.9208. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/YifanDengWHU/DDIMDL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy.

Epilepsy is a common neurological disorder that affects mammalian species including human beings and dogs. In order to discover novel drugs for the treatment of canine epilepsy, multiomics data were analyzed to identify epilepsy associated genes. In this research, the original ranking of associated genes was obtained by two high-throughput bioinformatics experiments: Genome Wide Association Study (GWAS) and microarray analysis. The association ranking of genes was enhanced by a re-ranking system, HPO-Shuffle, which integrated information from GWAS, microarray analysis and Human Phenotype Ontology database for further downstream analysis. After applying HPO-Shuffle, the association ranking of epilepsy genes were improved. Afterward, a weighted gene coexpression network analysis (WGCNA) led to a set of gene modules, which hinted a clear relevance between the high ranked genes and the target disease. Finally, WGCNA and connectivity map (CMap) analysis were performed on the integrated dataset to discover a potential drug list, in which a well-known anticonvulsant phensuximide was included.

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

Forkhead box O3 protects the heart against paraquat-induced aging-associated phenotypes by upregulating the expression of antioxidant enzymes.

Paraquat (PQ) promotes cell senescence in brain tissue, which contributes to Parkinson's disease. Furthermore, PQ induces heart failure and oxidative damage, but it remains unknown whether and how PQ induces cardiac aging. Here, we demonstrate that PQ induces phenotypes associated with senescence of cardiomyocyte cell lines and results in cardiac aging-associated phenotypes including cardiac remodeling and dysfunction in vivo. Moreover, PQ inhibits the activation of Forkhead box O3 (FoxO3), an important longevity factor, both in vitro and in vivo. We found that PQ-induced senescence phenotypes, including proliferation inhibition, apoptosis, senescence-associated ß-galactosidase activity, and p16INK4a expression, were significantly enhanced by FoxO3 deficiency in cardiomyocytes. Notably, PQ-induced cardiac remolding, apoptosis, oxidative damage, and p16INK4a expression in hearts were exacerbated by FoxO3 deficiency. In addition, both in vitro deficiency and in vivo deficiency of FoxO3 greatly suppressed the activation of antioxidant enzymes including catalase (CAT) and superoxide dismutase 2 (SOD2) in the presence of PQ, which was accompanied by attenuation in cardiac function. The direct in vivo binding of FoxO3 to the promoters of the Cat and Sod2 genes in the heart was verified by chromatin immunoprecipitation (ChIP). Functionally, overexpression of Cat or Sod2 alleviated the PQ-induced senescence phenotypes in FoxO3-deficient cardiomyocyte cell lines. Overexpression of FoxO3 and CAT in hearts greatly suppressed the PQ-induced heart injury and phenotypes associated with aging. Collectively, these results suggest that FoxO3 protects the heart against an aging-associated decline in cardiac function in mice exposed to PQ, at least in part by upregulating the expression of antioxidant enzymes and suppressing oxidative stress.

Three Dimensions of Reproducibility in Natural Language Processing.

Despite considerable recent attention to problems with reproducibility of scientific research, there is a striking lack of agreement about the definition of the term. That is a problem, because the lack of a consensus definition makes it difficult to compare studies of reproducibility, and thus to have even a broad overview of the state of the issue in natural language processing. This paper proposes an ontology of reproducibility in that field. Its goal is to enhance both future research and communication about the topic, and retrospective meta-analyses. We show that three dimensions of reproducibility, corresponding to three kinds of claims in natural language processing papers, can account for a variety of types of research reports. These dimensions are reproducibility of a conclusion, of a finding, and of a value. Three biomedical natural language processing papers by the authors of this paper are analyzed with respect to these dimensions.

CRISPR/Cas9-mediated efficient and precise targeted integration of donor DNA harboring double cleavage sites in Xenopus tropicalis.

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) 9 system has emerged as a powerful tool for knock-in of DNA fragments via donor plasmid and homology-independent DNA repair mechanism; however, conventional integration includes unnecessary plasmid backbone and may result in the unfaithful expression of the modified endogenous genes. Here, we report an efficient and precise CRISPR/Cas9-mediated integration strategy using a donor plasmid that harbors 2 of the same cleavage sites that flank the cassette at both sides. After the delivery of donor plasmid, together with Cas9 mRNA and guide RNA, into cells or fertilized eggs, concurrent cleavages at both sides of the exogenous cassette and the desired chromosomal site result in precise targeted integration without plasmid backbone. We successfully used this approach to precisely integrate the EGFP reporter gene into the myh6 locus or the GAPDH locus in Xenopus tropicalis or human cells, respectively. Furthermore, we demonstrate that replacing conventional terminators with the endogenous 3UTR of target genes in the cassette greatly improves the expression of reporter gene after integration. Our efficient and precise method will be useful for a variety of targeted genome modifications, not only in X. tropicalis, but also in mammalian cells, and can be readily adapted to many other organisms.-Mao, C.-Z., Zheng, L., Zhou, Y.-M., Wu, H.-Y., Xia, J.-B., Liang, C.-Q., Guo, X.-F., Peng, W.-T., Zhao, H., Cai, W.-B., Kim, S.-K., Park, K.-S., Cai, D.-Q., Qi, X.-F. CRISPR/Cas9-mediated efficient and precise targeted integration of donor DNA harboring double cleavage sites in Xenopus tropicalis.