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QB: Embracing the future of quantitative understanding and engineering of life

Frontiers of Electrical and Electronic Engineering in China 2019年第1期7卷 1-2页

作者： Xuegong Zhang Bioinformatics Division BNRist a nd Departme nt of AutomationTsinghua Un iversityBeiji ng 100084China MOE Key Lab of Bioinformatics and School of Life Sciences Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniersityBeijing 100084China

As a new inter- and multi-disciplinary forum for modeling, engineering and understanding life, our journal Quantitative Biology (QB) is celebrating its 6th armiversary and the start of a new Editorial Board in this issue. The past 6 years have evideneed tremendous progress in the research on computational biology, systems biology and synthetic biology. Omics studies have expanded from genomes and transcriptomes to multiple aspects of epigenomes and their interactions. People's understanding and modeling on genomes is becoming 3D or 4D. The developme nt of high throughput sin gle-cell geno mics tech no logies has aroused huge enthusiasm for building the Human Cell Atlas as an ultimate referenee for all future studies on human.

关键词： multi-disciplinary Quantitative Biology tremendous

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Expression and functional study of VpV262 Pol,a moderately halophilic DNA polymerase from the Vibrio parahaemolyticus phage VpV262

Expression and functional study of VpV262 Pol,a moderately h...

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中国生物化学与分子生物学会2020全国学术在线会议

作者： Yaping Gao Yun He Igor Ivanov Xuerui Yang Hui Tian Xing Liu Research Center of Molecular Diagnostics and Sequencing Research Institute of Tsinghua University in Shenzhen MOE Key Lab of Bioinformatics School of Life SciencesTsinghua University

Halophilic organisms are found widely in environments where the salt concentration is higher than 0.2 *** proteins isolated from these organisms maintain structural integrity and function under high salt stress,whereas their non-halophilic homologs tend to aggregate and *** we report for the first time the expression and function of a DNA polymerase（DNAPol） VpV262 Pol,which belongs to DNAPol Family A from Vibrio parahaemolyticus phage *** activity assay revealed that VpV262 Pol possessed 5'-3' polymerase activity as well as 3'-5' proofreading exonuclease ***262 Pol requires Mg or Mn to catalyze the polymerization *** activity assay under a wide range of salt concentrations showed that VpV262 Pol maintains the highest polymerase activity with 0-0.3 M of NaCl/KCl and0-0.5 M KAc（potassium acetate）/KGlc（potassium gluconate） when treated with 0-1 M corresponding salts,in contrast to significantly decreased activity of Phi29 Pol and Taq Pol above 0.2 *** with typical features of other halophilic proteins,negatively-charged amino acids are more frequently distributed on the surface of VpV262 Pol,contributing to highly solubility and enhanced *** 3 D-Structure of VpV262 Pol needs to be confirmed by experimental data further,this study here has added a member for the relatively small family of halotolerant DNA polymerase,and provides a valuable reference in isolation and characterization of DNA polymerases from halophilic organisms.

关键词： DNA polymerase salt tolerance halophilic enzyme Vibrio parahaemolyticus phage VpV262 3D-Structure random coil

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Comprehensive simulation of metagenomic sequencing data with non-uniform sampling distribution

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Frontiers of Electrical and Electronic Engineering in China 2018年第2期6卷 175-185页

作者： Shansong Liu Kui Hua Sijie Chen Xuegong Zhang MOE Key Lab of Bioinformatics Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China

Background： Metagenomic sequencing is a complex sampling procedure from unknown mixtures of many genomes. Having metagenome data with known genome compositions is essential for both benchmarking bioinformatics software and for investigating influences of various factors on the data. Compared to data from real microbiome samples or from defined microbial mock community, simulated data with proper computational models are better for the purpose as they provide more flexibility for controlling multiple factors. Methods： We developed a non-uniform metagenomic sequencing simulation system （nuMetaSim） that is capable of mimicking various factors in real metagenomic sequencing to reflect multiple properties of real data with customizable parameter settings. Results： We generated 9 comprehensive metagenomic datasets with different composition complexity from of 203 bacterial genomes and 2 archaeal genomes related with human intestine system. Conclusion： The data can serve as benchmarks for comparing performance of different methods at different situations, and the software package allows users to generate simulation data that can better reflect the specific properties in their scenarios.

关键词： simulation metagenomic sequencing data non-uniform sampling nuMetaSim

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Dropout training for SVMs with data augmentation

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Frontiers of Computer Science 2018年第4期12卷 694-713页

作者： Ning CHEN Jun ZHU Jianfei CHEN Ting CHEN MOE Key lab of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology TNLIST Tsinghua University Beijing 100084 China State Key Lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China

Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines （SVMs）, one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square （IRLS） algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re- weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we con- sider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representa- tions. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.

关键词： dropout SVMs logistic regression data aug- mentation iteratively reweighted least square

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BCMA: an integrative and versatile database for multi-scale and multi-omics molecular atlas of breast cancer

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Computational and Structural Biotechnology Journal 2025年

作者： Wenbo Guo Zikang Yin Qinglin Mei Lianshuo Li Yonghui Gong Xinqi Li Wei Zhang Wenjie Lei Bingqiang Liu Lin Hou Mei Yang Jin Gu MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing China State Key Laboratory of Experimental Hematology National Clinical Research Center for Blood Diseases Haihe Laboratory of Cell Ecosystem Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China Hainan Zhongke Data Source Technology Co. Ltd. Hainan China Department of Biomedical Engineering School of Control Science and Engineering Shandong University Jinan Shandong China School of Mathematics Shandong University Jinan China Department of Statistics and Data Science Tsinghua University Beijing China MOE Key Laboratory of Bioinformatics School of Life Sciences Tsinghua University Beijing China Department of Breast Surgery Zhujiang Hospital of Southern Medical University (the Second Clinical Medical College) Guangzhou China

Breast cancer (BC) is one of the most common cancer types among women worldwide. Understanding the complex molecular and cellular characteristics of BC is crucial for advancing precision treatment. To enable more reliable and reproducible biological discoveries, it is critical to collect molecular data from diverse BC cohorts and establish an integrative, versatile analysis platform. Here, we present BCMA (Breast Cancer Molecular Atlas, http://***/database/bcma/ ), a multi-scale, multi-omics BC database that encompasses 6 bulk multi-omics datasets and 9 single-cell transcriptomics datasets, collectively covering 5,424 cases and 236,363 cells. The BCMA systemically characterizes the molecular features of BC, including gene mutations, copy number alterations, RNA expression, miRNA expression, DNA methylation, as well as clinical phenotypes and cell heterogeneity. Meanwhile, a user-friendly interface for gene-centered search is provided, achieving the clinical information statistics, genomic events analysis, differential multi-omics feature identification, functional enrichment analysis, survival analysis, co-expression analysis, as well as single-cell gene expression profiling and cell type annotation. This platform holds great potential to enhance the understanding of molecular characteristics underlying BC and to facilitate the identification of disease-associated biomarkers.

关键词： breast cancer database molecular atlas multi-omics Single-cell

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Role of SENP6 in suppressing stress response and epigenetic alteration-induced articular chondrocyte senescence and OA susceptibility

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Osteoarthritis and Cartilage 2025年 33卷 S367-S367页

作者： Ye Liu Yaguang Zhao Seung-Gi Jin Vladimir Molchanov Jennifer Johnson Daisy Fu Di Lu Huadie Liu H. Josh Jang Gerd P. Pfeifer Tao N. Yang Department of Cell Biology Van Andel Institute Grand Rapids Michigan United States Department of Epigenetics Van Andel Institute Grand Rapids Michigan United States Bioinformatics & Biostats Core Van Andel Institute Grand Rapids Michigan United States MOE Key Lab of Rare Pediatric Disease Hengyang College of Medicine Hengyang Hunan China

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Corrigendum to“Development of methods for detecting the fate of mesenchymal stem cells regulated by bone bioactive materials”[***.6(3)(2021)613-626]

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Bioactive Materials 2023年第6期24卷 251-251页

作者： Le Jiang Zhongqun Liu Zhaoyan Wang Yijun Su Yingjin Wang Yaojie Wei Yanan Jiang Zhanrong Jia Chunyang Ma Fangli Gang Nan Xu Lingyun Zhao Xiumei Wang Qiong Wu Xiong Lu Xiaodan Sun State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing100084PR China Key Laboratory of Advanced Materials of Ministry of Education of China School of Materials Science and EngineeringTsinghua UniversityBeijing100084PR China Key Lab of Advanced Technologies of Materials of Ministry of Education School of Materials Science and EngineeringSouthwest Jiaotong UniversityChengdu610031PR China MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing100084PR China Center for Synthetic and Systems Biology Tsinghua UniversityBeijing100084PR China School of Life Sciences Tsinghua UniversityBeijing100084PR China

The authors regret that the last name and first name of all authors are reversed in the original *** correct writing is as follows:Le Jiang,a,b#;Zhongqun Liu,a,b#;Zhaoyan Wang,d,e,f;Yijun Su,a,b;Yingjin Wang,a,b;Yaojie Wei,a,b;Yanan Jiang,c;Zhanrong Jia,c;Chunyang Ma,a,b;Fangli Gang,a,b;Nan Xu,a,b;Lingyun Zhao,a,b;Xiumei Wang,a,b;Qiong Wu,d,e,f;Xiong Lu,c,*and Xiaodan Sun,a,b,**** key laboratory of New Ceramics and Fine Processing,School of Materials Science and Engineering,Tsinghua University,Beijing 100084,People’s Republic of China;*** laboratory of Advanced Materials of Ministry of Education of China,School of Materials Science and Engineering,Tsinghua University,Beijing 100084,People’s Republic of China;*** lab of Advanced Technologies of Materials of Ministry of Education,School of Materials Science and Engineering,Southwest Jiaotong University,Chengdu 610031,People’s Republic of China *** key laboratory of bioinformatics,Tsinghua University,Beijing,100084,People’s Republic of China *** for Synthetic and Systems Biology,Tsinghua University,Beijing,100084,People’s Republic of China *** of Life Sciences,Tsinghua University,Beijing,100084,People’s Republic of China.

关键词： reversed Republic Chengdu

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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Warburg Effects in Cancer and Normal Proliferating Cells:Two Tales of the Same Name

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Genomics, Proteomics & bioinformatics 2019年第3期17卷 273-286页

作者： Huiyan Sun Liang Chen Sha Cao Yanchun Liang Ying Xu The China-Japan Union Hospital Jilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA Faculty of Health Sciences University of MacaoTaipaMacao SAR 999078China Department of Biostatistics School of MedicineIndiana UniversityIndianapolisIN 46202USA Zhuhai Laboratory of MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Zhuhai College of Jilin UniversityZhuhai 519041China

It has been observed that both cancer tissue cells and normal proliferating cells(NPCs)have the Warburg *** goal here is to demonstrate that they do this for different *** accomplish this,we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in *** analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect,to raise the intracellular pH from 6.8 to 7.2 and to prepare for cell division *** cell cycle starts,the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release,to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH *** cells go back to the normal respirationbased ATP production once the cell division phase *** comparison,cancer cells have reached their intracellular pH at 7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are *** cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading ***-expression analyses suggest that lactic acid secretion is regulated by external,non-pH related ***,our data strongly suggest that the two cell types have the Warburg effect for very different reasons.

关键词： Cancer Warburg effect Fenton reaction Cell proliferation pH homeostasis

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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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