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Characterizing RNA Pseudouridylation by Convolutional Neural Networks

Genomics, Proteomics & bioinformatics 2021年第5期19卷 815-833页

作者： Xuan He Sai Zhang Yanqing Zhang Zhixin Lei Tao Jiang Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA MOE Key Lab of Bioinformatics and Bioinformatics Division BNRIST/Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Institute of Integrative Genome Biology University of CaliforniaRiversideCA 92521USA MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China

Pseudouridine(Ψ)is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and m ***,the functions,mechanisms,and precise distribution ofΨs(especially in m RNAs)still remain largely *** landscape ofΨs across the transcriptome has not yet been fully ***,we present a highly effective model based on a convolutional neural network(CNN),called Pseudo Uridy Lation Site Estimator(PULSE),to analyze large-scale profiling data ofΨsites and characterize the contextual sequence features of ***,consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network,can automatically learn the hidden patterns of pseudouridylation from the local sequence *** validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy,thus enabling us to further characterize the transcriptome-wide landscape ofΨ*** further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation,such as the regulations of RNA secondary structure,codon usage,translation,and RNA stability,and the connection to single nucleotide *** source code and final model for PULSE are available at https://***/mlcb-thu/PULSE.

关键词： Pseudouridylation Convolution neural network Sequence motif Translation RNA stability

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Applications of phage-derived RNA-based technologies in synthetic biology

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Synthetic and Systems Biotechnology 2020年第4期5卷 343-360页

作者： Wenhui Zhang Qiong Wu MOE Key Lab.Bioinformatics School of Life SciencesTsinghua UniversityBeijing100084China Center for Synthetic and Systems Biology Tsinghua UniversityBeijing100084China

As the most abundant biological entities with incredible diversity,bacteriophages(also known as phages)have been recognized as an important source of molecular machines for the development of genetic-engineering *** the same time,phages are crucial for establishing and improving basic theories of molecular *** on phages provide rich sources of essential elements for synthetic circuit design as well as powerful support for the improvement of directed evolution ***,phages play a vital role in the development of new technologies and central scientific *** the RNA world hypothesis was proposed and developed,novel biological functions of RNA continue to be *** and its related elements are widely used in many fields such as metabolic engineering and medical diagnosis,and their versatility led to a major role of RNA in synthetic *** development of RNA-based technologies will advance synthetic biological tools as well as provide verification of the RNA world *** synthetic biology efforts are based on reconstructing existing biological systems,understanding fundamental biological processes,and developing new ***-based technologies derived from phages will offer abundant sources for synthetic biological ***,phages as well as RNA have high impact on biological evolution,which is pivotal for understanding the origin of life,building artificial life-forms,and precisely reprogramming biological *** review discusses phage-derived RNA-based technologies terms of phage components,the phage lifecycle,and interactions between phages and *** significance of RNA-based technology derived from phages for synthetic biology and for understanding the earliest stages of biological evolution will be highlighted.

关键词： Bacteriophages Synthetic biology RNA imaging RNA replication Phage-bacterial interactions

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Tissue-specific transcription reprogramming promotes liver metastasis of colorectal cancer

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细胞研究（英文版） 2020年第1期30卷 34-49页

作者： Shuaishuai Teng Yang Eric Li Ming Yang Rui Qi Yiming Huang Qianyu Wang Yanmei Zhang Shanwen Chen Shasha Li Kequan Lin Yang Cao Qunsheng Ji Qingyang Gu Yujing Cheng Zai Chang Wei Guo Pengyuan Wang Ivan Garcia-Bassets Zhi John Lu Dong Wang MOE Key Lab of Bioinformatics Department of Basic Medical Sciences School of Medicine Tsinghua University Beijing 100084 China MOE Key Lab of Bioinformatics Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing China Ludwig Institute for Cancer Research 9500 Gilman Drive La Jolla CA 92093 USA MOE Key Lab of Bioinformatics Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing China PKU-THU Center for Life Sciences Tsinghua University Beijing China Division of General Surgery Peking University First Hospital Peking University Beijing China WuXi AppTec(Shanghai)Co. Ltd. Shanghai 200131 China Zhejiang University-University of Edinburgh Institute Haining China School of Medicine University of California San Diego La Jolla CA USA MOE Key Lab of Bioinformatics Department of Basic Medical Sciences School of Medicine Tsinghua University Beijing 100084 China Collaborative Innovation Center for Biotherapy West China Hospital Sichuan University Chengdu China School of Basic Medical Sciences Chengdu University of Traditional Chinese Medicine Chengdu 611137 China

Metastasis,the development of secondary malignant growths at a distance from a primary tumor,is the cause of death for 90％ of cancer patients,but little is known about how metastatic cancer cells adapt to and colonize new tissue ***,using clinical samples,patient-derived xenograft (PDX) samples,PDX cells,and primary/metastatic cell lines,we discovered that liver metastatic colorectal cancer (CRC) cells lose their colon-specific gene transcription program yet gain a liver-specific gene transcription *** showed that this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical enhancers and ***,we identified that the liver-specific transcription factors FOXA2 and HNF1A can bind to the gained enhancers and activate the liver-specific gene transcription,thereby driving CRC liver ***,similar transcription reprogramming can be obsenved in multiple cancer *** data suggest that reprogrammed tissue-specific transcription promotes metastasis and should be targeted therapeutically.

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Inferring Intercellular Communication and Intracellular Regulations From Single-Cell or Spatial Transcriptomics Data: A Systematic Survey and Benchmarking

SSRN

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SSRN 2022年

作者： Luo, Jiaxin Yan, Lulu Wei, Lei Deng, Minghua Zhang, Xuegong Sun, Xiaoqiang School of Medicine Sun Yat-sen University Guangzhou510080 China School of Mathematics Sun Yat-sen University Guangzhou510275 China Bioinformatics Division of BNRIST Department of Automation MOE Key Lab of Bioinformatics Tsinghua University Beijing100084 China School of Mathematical Sciences Peking University Beijing100871 China

Cell-cell communication (CCC) is important for coordinating cell fates and functioning in multicellular organisms. With the emergence of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) data, an increasing number of CCC inference methods have been developed. However, their thorough comparison is lacking. Here, we systematically investigated 25 scRNA-seq data-based CCC inference methods and 10 ST data-based CCC inference methods. Moreover, we benchmarked 18 methods for intercellular communication prediction based on differential ligand-receptor (LR) associations between close and distant cell pair groups, as well as 5 methods for ligand/receptor-target prediction based on cell line perturbation datasets, using breast cancer as a real scenario. Several tools (e.g., CellChat, iTALK, and scSeqComm for predicting intercellular LR interactions and stMLnet for predicting ligand/receptor-target regulations) with good performance were identified. Our results suggest that, in addition to appropriately integrating prior information and preprocessing the data, considering intracellular responses and spatial relationships can benefit the CCC inference. In addition, we examined the similarity between different tools and the impact of typical parameter selection on CCC inference. Furthermore, we discuss the challenges and future directions for CCC inference. This study provides valuable information and guidelines for advancing the methodology and applications of CCC *** Information: XS was supported by grants from the National key R&D Program of China (2021YFF1200903), the National Natural Science Foundation of China (62273364, 11871070), the Guangdong Basic and Applied Basic Research Foundation (2020B151502120).Declaration of Interests: The authors declare that they have no competing interests. © 2022, The Authors. All rights reserved.

关键词： RNA

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uniLIVER: a human liver cell atlas for data-driven cellular state mapping

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Journal of Genetics and Genomics 2025年

作者： Wu, Yanhong Fan, Yuhan Miao, Yuxin Li, Yuman Du, Guifang Chen, Zeyu Diao, Jinmei Chen, Yu-Ann Ye, Mingli You, Renke Chen, Amin Chen, Yixin Li, Wenrui Guo, Wenbo Dong, Jiahong Zhang, Xuegong Wang, Yunfang Gu, Jin MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Clinical Translational Science Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Fuzhou Institute of Data Technology Fujian Fuzhou 350207 China Center for Synthetic and Systems Biology School of Life Sciences and School of Medicine Tsinghua University Beijing 100084 China

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://***/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues. © 2025 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China

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AS-0164 Inhibition of Hypoxia Inducible Factor 2α Suppresses Vascular Smooth Muscle Cell Phenotype Switching and Hypertension via Targeting Nox4-Mediated Mitochondrial Oxidative Stress

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Journal of the American College of Cardiology 2024年第22期84卷 C1-C2页

作者： Qi Zhang Xinxin Zhu Jinmeng Jia Xia Wang Yutao Ma Nan Zhao YutongCui Jinhao Liao Sijia Guan Jinlong He Aijuan Qu MOE Key Lab of Bioinformatics Bioinformatics Division of BNRIST and Department of Automation Tsinghua University Beijing China Tianjin Key Laboratory of Metabolic Diseases The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics Department of Physiology and Pathophysiology Tianjin Medical University Tianjin China

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Predicting microvascular invasion of hepatocellular carcinoma by texture analysis of multi-phase Mr image 12

Predicting microvascular invasion of hepatocellular carcinom...

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12th International Conference on bioinformatics and Computational Biology, BICOB 2020

作者： Yu, Qin Chen, Geng Li, Jiaqi Liu, Xiaolong Zhang, Xuegong Lu, Haiming BNRIST Department of Automation Tsinghua University Beijing China BNRIST Tsinghua University Beijing China The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province Mengchao Hepatobiliary Hospital of Fujian Medical University Fuzhou China Department of Automation Bioinformatics Division BNRIST and MOE Key Lab of Bioinformatics Tsinghua University Beijing China

Microvascular invasion (MVI) diagnosis is of vital importance in the curative treatment of hepatocellular carcinoma patients due to its close relationship with prognostic analysis. Currently, MVI detection often bases on surgical specimen, which is invasive for patients. This study, we extracted texture features of multi-phase MR image to predict the presence of MVI. Feature extraction employed neighboring gray level dependency emphasis (NGLDM) method, which is a common texture feature analysis method. Next, we built a SVM classifier to predict the presence of MVI using extracted features. Especially, multi-phase features were designed to enhance the precision of prediction. Enhanced MR images of pre-contrast phase and portal vein phase were used to extracting features. The method was tested by 5-fold cross-validation on the dataset. The precision of prediction was 91.31%, compared with the baseline method of 70.71%. To make the prediction more interpretable, the relationship between NGLDM texture features and the presence of MVI was discussed in the end. © 2020, EasyChair. All rights reserved.

关键词： Forecasting

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Development of methods for detecting the fate of mesenchymal stem cells regulated by bone bioactive materials

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Bioactive Materials 2021年第3期6卷 613-626页

作者： Jiang Le Liu Zhongqun Wang Zhaoyan Su Yijun Wang Yingjin Wei Yaojie Jiang Yanan Jia Zhanrong Ma Chunyang Gang Fangli Xu Nan Zhao Lingyun Wang Xiumei Wu Qiong Lu Xiong Sun Xiaodan State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing100084People's Republic of China Key Laboratory of Advanced Materials of Ministry of Education of China School of Materials Science and EngineeringTsinghua UniversityBeijing100084People's Republic of China Key Lab of Advanced Technologies of Materials of Ministry of Education School of Materials Science and EngineeringSouthwest Jiaotong UniversityChengdu610031People's Republic of China MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing100084People's Republic of China Center for Synthetic and Systems Biology Tsinghua UniversityBeijing100084People's Republic of China School of Life Sciences Tsinghua UniversityBeijing100084People's Republic of China

The fate of mesenchymal stem cells(MSCs)is regulated by biological,physical and chemical *** in biotechnology and materials science promoted the occurrence of bioactive materials which can provide physical and chemical signals for MSCs to regulate their *** order to design and synthesize materials that can precisely regulate the fate of MSCs,the relationship between the properties of materials and the fate of mesenchymal stem cells need to be clarified,in which the detection of the fate of mesenchymal stem cells plays an important *** the past 30 years,a series of detection technologies have been developed to detect the fate of MSCs regulated by bioactive materials,among which high-throughput technology has shown great advantages due to its ability to detect large amounts of data at one *** this review,the latest research progresses of detecting the fate of MSCs regulated by bone bioactive materials(BBMs)are systematically reviewed from traditional technology to high-throughput technology which is emphasized ***,current problems and the future development direction of detection technologies of the MSCs fate regulated by BBMs are *** aim of this review is to provide a detection technical framework for researchers to establish the relationship between the properties of BMMs and the fate of MSCs,so as to help researchers to design and synthesize BBMs better which can precisely regulate the fate of MSCs.

关键词： Bone bioactive materials High-throughput technology Transcriptomics Single cell RNA-seq Proteomics

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Pixel-Level Clustering Reveals Intra-Tumor Heterogeneity in Non-Small Cell Lung Cancer

Pixel-Level Clustering Reveals Intra-Tumor Heterogeneity in ...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jiaqi Li Sijie Chen Haiming Lu Xuegong Zhang Xiang Fang Department of Automation Bioinformatics Division BNRIST and MOE Key Lab of Bioinformatics Tsinghua University Bioinformatics Division BNRIST and MOE Key Lab of Bioinformatics Tsinghua University Department of Automation Bioinformatics Division BNRIST and MOE Key Lab of Bioinformatics CSSB School of Life Sciences Tsinghua University

ISBN: (纸本)9781728118680

Intra-tumor heterogeneity in non-small cell lung cancer (NSCLC) is a current topic of many genomic studies, but there is no effective technology for characterizing tumor heterogeneity before surgery or biopsy. Since computed tomography (CT) is a standard technology for the diagnosis of lung cancer, it will be of great scientific and clinical use if tumor heterogeneity information can be extracted from CT images. In this work, we proposed a strategy for studying intra-tumor heterogeneity using pixel-level multi-resolution clustering on radiomics features of CT images. On a set of pretreatment CT images of NSCLC patients, we observed three high-order image patterns "uni-core", "multi-core" and "diffused" that can be associated with characteristics of intra-tumor heterogeneity. We checked the clinical records of the two groups of patterns: "uni-core" and "diffused" patterns that have enough samples, and found significant difference in their survival time. The results showed the potential of using radiomic analysis on CT images to characterize intra-tumor heterogeneity and to predict patient prognosis based on high-order image patterns.

关键词： intra-tumor heterogeneity pixel-level clustering radiomic features survival analysis Non-Small-Cell Lung Carcinoma Heterogeneous Survival Analysis bedding heterogeneity Lung Cancer X-Ray Computed Tomography images

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