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Zheng D, Zhou S, Chen L, Pang G, Yang J. A deep learning method to predict bacterial ADP-ribosyltransferase toxins. Bioinformatics 2024; 40:btae378. [PMID: 38885365 PMCID: PMC11219481 DOI: 10.1093/bioinformatics/btae378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/03/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. RESULTS We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in training ARTNet. Subsequently, we employed a data optimization strategy by utilizing ART-related domain subsequences instead of the primary full sequences, thereby significantly enhancing the performance of ARTNet. ARTNet achieved a Matthew's correlation coefficient (MCC) of 0.9351 and an F1-score (macro) of 0.9666 on repeated independent test datasets, outperforming three other deep learning models and six traditional machine learning models in terms of time efficiency and accuracy. Furthermore, we empirically demonstrated the ability of ARTNet to predict novel bARTTs across domain superfamilies without sequence similarity. We anticipate that ARTNet will greatly facilitate the screening and identification of novel bARTTs from bacterial genomes. AVAILABILITY AND IMPLEMENTATION ARTNet is publicly accessible at http://www.mgc.ac.cn/ARTNet/. The source code of ARTNet is freely available at https://github.com/zhengdd0422/ARTNet/.
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Affiliation(s)
- Dandan Zheng
- NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102629, China
| | - Siyu Zhou
- NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102629, China
| | - Lihong Chen
- NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102629, China
| | - Guansong Pang
- School of Computing and Information Systems, Singapore Management University, Singapore 178902, Singapore
| | - Jian Yang
- NHC Key Laboratory of Systems Biology of Pathogens, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102629, China
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Zhang G, Wang H, Zhang Z, Zhang L, Guo G, Yang J, Yuan F, Ju F. Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework. Brief Bioinform 2024; 25:bbae319. [PMID: 39007592 PMCID: PMC11247404 DOI: 10.1093/bib/bbae319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 05/18/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024] Open
Abstract
High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.
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Affiliation(s)
- Guoqing Zhang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
- Center of Synthetic Biology and Integrated Bioengineering, Westlake University, Hangzhou, Zhejiang 310030, China
| | - Hui Wang
- Representation Learning Laboratory, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
| | - Zhiguo Zhang
- Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
| | - Lu Zhang
- Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
| | - Guibing Guo
- Software College, Northeastern University, Shenyang, Liaoning 110169, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Fajie Yuan
- Representation Learning Laboratory, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
| | - Feng Ju
- Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
- Center of Synthetic Biology and Integrated Bioengineering, Westlake University, Hangzhou, Zhejiang 310030, China
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
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Cozzi M, Failla M, Gianquinto E, Kovachka S, Buoli Comani V, Compari C, De Bei O, Giaccari R, Marchesani F, Marchetti M, Ronda L, Rolando B, Baroni M, Cruciani G, Campanini B, Bettati S, Faggiano S, Lazzarato L, Spyrakis F. Identification of small molecules affecting the interaction between human hemoglobin and Staphylococcus aureus IsdB hemophore. Sci Rep 2024; 14:8272. [PMID: 38594253 PMCID: PMC11003968 DOI: 10.1038/s41598-024-55931-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/29/2024] [Indexed: 04/11/2024] Open
Abstract
Human hemoglobin (Hb) is the preferred iron source of Staphylococcus aureus. This pathogenic bacterium exploits a sophisticated protein machinery called Iron-regulated surface determinant (Isd) system to bind Hb, extract and internalize heme, and finally degrade it to complete iron acquisition. IsdB, the surface exposed Hb receptor, is a proven virulence factor of S. aureus and the inhibition of its interaction with Hb can be pursued as a strategy to develop new classes of antimicrobials. To identify small molecules able to disrupt IsdB:Hb protein-protein interactions (PPIs), we carried out a structure-based virtual screening campaign and developed an ad hoc immunoassay to screen the retrieved set of commercially available compounds. Saturation-transfer difference (STD) NMR was applied to verify specific interactions of a sub-set of molecules, chosen based on their efficacy in reducing the amount of Hb bound to IsdB. Among molecules for which direct binding was verified, the best hit was submitted to ITC analysis to measure the binding affinity to Hb, which was found to be in the low micromolar range. The results demonstrate the viability of the proposed in silico/in vitro experimental pipeline to discover and test IsdB:Hb PPI inhibitors. The identified lead compound will be the starting point for future SAR and molecule optimization campaigns.
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Affiliation(s)
- Monica Cozzi
- Department of Food and Drug, University of Parma, Parma, Italy
| | | | - Eleonora Gianquinto
- Department of Drug Science and Technology, University of Turin, Turin, Italy
| | - Sandra Kovachka
- Department of Drug Science and Technology, University of Turin, Turin, Italy
- The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, Jupiter, FL, USA
| | | | | | - Omar De Bei
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | | | | | - Luca Ronda
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Institute of Biophysics, National Research Council, Pisa, Italy
| | - Barbara Rolando
- Department of Drug Science and Technology, University of Turin, Turin, Italy
| | - Massimo Baroni
- Molecular Discovery Ltd, Kisnetic Business Centre, Elstree, Borehamwood, Hertfordshire, UK
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy
| | | | - Stefano Bettati
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Institute of Biophysics, National Research Council, Pisa, Italy
| | - Serena Faggiano
- Department of Food and Drug, University of Parma, Parma, Italy.
- Institute of Biophysics, National Research Council, Pisa, Italy.
| | - Loretta Lazzarato
- Department of Drug Science and Technology, University of Turin, Turin, Italy
| | - Francesca Spyrakis
- Department of Drug Science and Technology, University of Turin, Turin, Italy.
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Zhao P, Zhou S, Xu P, Su H, Han Y, Dong J, Sui H, Li X, Hu Y, Wu Z, Liu B, Zhang T, Yang F. RVdb: a comprehensive resource and analysis platform for rhinovirus research. Nucleic Acids Res 2024; 52:D770-D776. [PMID: 37930838 PMCID: PMC10768139 DOI: 10.1093/nar/gkad937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 11/08/2023] Open
Abstract
Rhinovirus (RV), a prominent causative agent of both upper and lower respiratory diseases, ranks among the most prevalent human respiratory viruses. RV infections are associated with various illnesses, including colds, asthma exacerbations, croup and pneumonia, imposing significant and extended societal burdens. Characterized by a high mutation rate and genomic diversity, RV displays a diverse serological landscape, encompassing a total of 174 serotypes identified to date. Understanding RV genetic diversity is crucial for epidemiological surveillance and investigation of respiratory diseases. This study introduces a comprehensive and high-quality RV data resource, designated RVdb (http://rvdb.mgc.ac.cn), covering 26 909 currently identified RV strains, along with RV-related sequences, 3D protein structures and publications. Furthermore, this resource features a suite of web-based utilities optimized for easy browsing and searching, as well as automatic sequence annotation, multiple sequence alignment (MSA), phylogenetic tree construction, RVdb BLAST and a serotyping pipeline. Equipped with a user-friendly interface and integrated online bioinformatics tools, RVdb provides a convenient and powerful platform on which to analyse the genetic characteristics of RVs. Additionally, RVdb also supports the efforts of virologists and epidemiologists to monitor and trace both existing and emerging RV-related infectious conditions in a public health context.
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Affiliation(s)
- Peng Zhao
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Siyu Zhou
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Panpan Xu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Haoxiang Su
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Yelin Han
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Jie Dong
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Hongtao Sui
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Xin Li
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Yongfeng Hu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Zhiqiang Wu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Bo Liu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Ting Zhang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
| | - Fan Yang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 102629, P.R. China
- Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences, Beijing 102629, P.R. China
- State Key Laboratory of Respiratory Health and Multimorbidity, Beijing 102629, P.R. China
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Zhou S, Zheng J, Jia C. SPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in Pseudomonas aeruginosa. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:13294-13305. [PMID: 36654047 DOI: 10.3934/mbe.2022622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is located upstream of the coding gene and acts as the "switch" for gene transcriptional regulation. Lots of promoter predictors have been developed for different bacterial species, but only a few are designed for Pseudomonas aeruginosa, a widespread Gram-negative conditional pathogen in nature. In this work, an ensemble model named SPREAD is proposed for the recognition of promoters in Pseudomonas aeruginosa. In SPREAD, the DNA sequence autoencoder model LSTM is employed to extract potential sequence information, and the mean output probability value of CNN and RF is applied as the final prediction. Compared with G4PromFinder, the only state-of-the-art classifier for promoters in Pseudomonas aeruginosa, SPREAD improves the prediction performance significantly, with an accuracy of 0.98, recall of 0.98, precision of 0.98, specificity of 0.97 and F1-score of 0.98.
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Affiliation(s)
- Shengming Zhou
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Jia Zheng
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, China
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6
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Liu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res 2021; 50:D912-D917. [PMID: 34850947 PMCID: PMC8728188 DOI: 10.1093/nar/gkab1107] [Citation(s) in RCA: 418] [Impact Index Per Article: 139.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 12/20/2022] Open
Abstract
The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is dedicated to presenting a comprehensive knowledge base and a versatile analysis platform for bacterial virulence factors (VFs). Recent developments in sequencing technologies have led to increasing demands to analyze potential VFs within microbiome data that always consist of many different bacteria. Nevertheless, the current classification of VFs from various pathogens is based on different schemes, which create a chaotic situation and form a barrier for the easy application of the VFDB dataset for future panbacterial metagenomic analyses. Therefore, based on extensive literature mining, we recently proposed a general category of bacterial VFs in the database and reorganized the VFDB dataset accordingly. Thus, all known bacterial VFs from 32 genera of common bacterial pathogens collected in the VFDB are well grouped into 14 basal categories along with over 100 subcategories in a hierarchical architecture. The new coherent and well-defined VFDB dataset will be feasible and applicable for future panbacterial analysis in terms of virulence factors. In addition, we introduced a redesigned JavaScript-independent web interface for the VFDB website to make the database readily accessible to all users with various client settings worldwide.
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Affiliation(s)
- Bo Liu
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Dandan Zheng
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Siyu Zhou
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Lihong Chen
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
| | - Jian Yang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P. R. China
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Wu S, Fang Z, Tan J, Li M, Wang C, Guo Q, Xu C, Jiang X, Zhu H. DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach. Gigascience 2021; 10:giab056. [PMID: 34498685 PMCID: PMC8427542 DOI: 10.1093/gigascience/giab056] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactions with bacterial hosts and regulation of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method, DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment. FINDINGS DeePhage uses a "one-hot" encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on 5-fold cross-validation reaches as high as 89%, nearly 10% and 30% higher than that of 2 similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time vs PhagePred and PHACTS by 245 and 810 times, respectively, under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease. CONCLUSIONS DeePhage is a novel tool developed to rapidly and efficiently identify 2 kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.
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Affiliation(s)
- Shufang Wu
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
| | - Zhencheng Fang
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
| | - Jie Tan
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
| | - Mo Li
- Peking University-Tsinghua University - National Institute of Biological Sciences (PTN) joint PhD program, School of Life Sciences, Peking University, Beijing 100871, Beijing, China
| | - Chunhui Wang
- Peking University-Tsinghua University - National Institute of Biological Sciences (PTN) joint PhD program, School of Life Sciences, Peking University, Beijing 100871, Beijing, China
| | - Qian Guo
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University,
GA 30332, Atlanta, USA
| | - Congmin Xu
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University,
GA 30332, Atlanta, USA
| | - Xiaoqing Jiang
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
| | - Huaiqiu Zhu
- State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing 100871, Beijing, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University,
GA 30332, Atlanta, USA
- Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, Beijing, China
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