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Science China(Life Sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason tieliu shi center for bioinformatics and computational biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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

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Science China Life Sciences 2024年

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 center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory Biology the Institute of Biomedical Sciences and School of Life Sciences East China Normal University Beijing Institutes of Life Science Chinese Academy of Sciences Peking-Tsinghua center for Life Sciences Academy for Advanced Interdisciplinary Studies Center for Quantitative Biology (CQB) Peking University Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s Hospital Zhejiang University School of Medicine Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai 200062 China Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City) Pharmaceutical Informatics Institute College of Pharmaceutical Sciences Zhejiang University Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of Education Orthopaedic Department Tongji Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Frontier Science center for Stem Cells School of Life Sciences and Technology Tongji University Translational Medical center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Research Institute of Intelligent Computing Zhejiang Lab Shanghai Research Institute for Intelligent Autonomous Systems center for Single-cell Omics School of Public Health Shanghai Jiao Tong University School of Medicine National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River Delta Zhejiang University

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our 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 intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These 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 screening. Single-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

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Towards efficiency in rare disease research： what is distinctive and important？

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Science China(Life Sciences) 2017年第7期60卷 686-691页

作者： Jinmeng Jia tieliu shi The center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory Biology the Institute of Biomedical Sciences and School of Life Sciences East China Normal University Shanghai 200241 China

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million *** the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing(NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.

关键词： rare disease rare disease diagnosis and treatment data standard data sharing orphan drugs

Characterizing and annotating the genome using RNA-seq data

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Science China(Life Sciences) 2017年第2期60卷 116-125页

作者： Geng Chen tieliu shi Leming shi center for Pharmacogenomics School of Pharmacy and School of Life SciencesFudan UniversityShanghai 201203China The 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 State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology School of Life SciencesFudan UniversityShanghai 200433China Fudan-Zhangjiang center for Clinical Genomics Shanghai 201203China Zhangiiang centerfor Translational Medicine Shanghai 201203China

bioinformatics methods for various RNA-seq data analyses are in fast evolution with the improvement of sequencing technologies. However, many challenges still exist in how to efficiently process the RNA-seq data to obtain accurate and comprehensive results. Here we reviewed the strategies for improving diverse transcriptomic studies and the annotation of genetic variants based on RNA-seq data. Mapping RNA-seq reads to the genome and transcriptome represent two distinct methods for quantifying the expression of genes/transcripts. Besides the known genes annotated in current databases, many novel genes/transcripts(especially those long noncoding RNAs) still can be identified on the reference genome using RNA-seq. Moreover, owing to the incompleteness of current reference genomes, some novel genes are missing from them. Genome-guided and de novo transcriptome reconstruction are two effective and complementary strategies for identifying those novel genes/transcripts on or beyond the reference genome. In addition, integrating the genes of distinct databases to conduct transcriptomics and genetics studies can improve the results of corresponding analyses.

关键词： RNA-seq genome-guided transcriptome reconstruction de novo assembly long noncoding RNA genetic variants

The Challenge and promise of rare disease diagnosis in China

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Science China(Life Sciences) 2017年第7期60卷 681-685页

作者： Xin Ni tieliu shi Otolaryngology Head and Neck Surgery Beijing Children's Hospital Capital Medical University National Center for Children's Health Beijing 100045 China Beijing Key Laboratory for Pediatric Diseases of Otolaryngology Head and Neck Surgery Beijing Pediatric Research Institute Beijing Children's Hospital Capital Medical University National Center for Children's Health Beijing 100045 China The center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory Biology the Institute of Biomedical Sciences and School of Life Sciences East China Normal University Shanghai 200241 China

Rare diseases are chronic and serious,featuring early onset at birth or in childhood,rapid deterioration and high mortality rate,which creates a burden on society and public health *** the known rare diseases,80 percent are genetic in origin,and half of those affected worldwide are *** China,the rare disease patients are over 10 million,and70 percent of the patients are children(Song et al.,2012;Liu et al.,2010).

关键词： deterioration childhood burden children featuring promise birth mortality pediatric distinguished

Novel loci and potential mechanisms of major depressive disorder,bipolar disorder,and schizophrenia

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Science China(Life Sciences) 2022年第1期65卷 167-183页

作者： He Wang Zhenghui Yi tieliu shi center for bioinformatics and computational biology Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Shanghai Mental Health center Shanghai Jiao Tong University School of MedicineShanghai 200030China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China Beijing Advanced Innovation center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijing 100083China

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain *** integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for *** discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after *** identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three *** performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling ***,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on ***,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.

关键词： psychiatric disorders multitrait analysis of genome-wide association analysis novel loci Mendelian randomization causal effects

Innovation for better health of children

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Science China(Life Sciences) 2019年第12期62卷 1561-1562页

作者： Ying Shen tieliu shi Beijing Key Laboratory for Chronic Renal Disease and Blood Purification Key Laboratory of Major Diseases in Children of Ministry of EducationNephrology DepartmentBeijing Children's HospitalCapital Medical UniversityNational Center for Children's HealthBeijing 100045China The 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

The past decades have witnessed a rapid development in the pediatric field along with the development of medical sciences in ***,the increasing demand for pediatric healthcare services still cannot be met owing to various *** shortage of pediatric medical resources and the limited access to medical care for pediatric patients have long been the priorities of healthcare reform in China.

关键词： China. pediatric patients

Next-generation sequencing technologies for personalized medicine:promising but challenging

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Science China(Life Sciences) 2013年第2期56卷 101-103页

作者： CHEN Geng shi tieliu center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory BiologyInstitute of Biomedical Sciences and School of Life SciencesEast China Normal University

In the past several years, next-generation sequencing （NGS） technologies have greatly revolutionized our approaches to explore and depict the characteristics and functions of the genomes for various species. The NGS technologies have been broadly used in diverse fields including genomics （genome sequencing and exome sequencing） ,

关键词：测序技术个性化挑战性基因组测序医药基因组学物种

Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq

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Science China(Life Sciences) 2013年第2期56卷 143-155页

作者： LU BingXin ZENG ZhenBing shi tieliu center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory BiologyInstitute of Biomedical Sciences and School of Life SciencesEast China Normal University Software Engineering Institute School of Software EngineeringEast China Normal University

Transcriptome reconstruction is an important application of RNA-Seq,providing critical information for further analysis of *** RNA-Seq offers the potential to identify the whole picture of transcriptome,it still presents special *** handle these difficulties and reconstruct transcriptome as completely as possible,current computational approaches mainly employ two strategies:de novo assembly and genome-guided *** order to find the similarities and differences between them,we firstly chose five representative assemblers belonging to the two classes respectively,and then investigated and compared their algorithm features in theory and real performances in *** found that all the methods can be reduced to graph reduction problems,yet they have different conceptual and practical implementations,thus each assembly method has its specific advantages and disadvantages,performing worse than others in certain aspects while outperforming others in anther aspects at the same *** we merged assemblies of the five assemblers and obtained a much better *** we evaluated an assembler using genome-guided de novo assembly approach,and achieved good *** on these results,we suggest that to obtain a comprehensive set of recovered transcripts,it is better to use a combination of de novo assembly and genome-guided assembly.

关键词： transcriptome reconstruction RNA-Seq de novo assembly genome-guided assembly