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Tracking SARS-CoV-2 Omicron diverse spike gene mutations identifies multiple inter-variant recombination events

Signal Transduction and Targeted Therapy 2022年第5期7卷 1808-1816页

作者： Junxian Ou Wendong Lan Xiaowei Wu Tie Zhao Biyan Duan Peipei Yang Yi Ren Lulu Quan Wei Zhao Donald Seto James Chodosh Zhen Luo Jianguo Wu Qiwei Zhang Guangdong Provincial Key Laboratory of Virology Institute of Medical MicrobiologyJinan University510632 GuangzhouChina BSL-3 Laboratory(Guangdong) Guangdong Provincial Key Laboratory of Tropical Disease ResearchSchool of Public HealthSouthern Medical University510515 GuangzhouChina Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVA 20110USA Department of Ophthalmology Howe Laboratory Massachusetts Eye and EarHarvard Medical SchoolBostonMA 02114USA Foshan Institute of Medical Microbiology 528315 FoshanChina

The current pandemic of COVID-19 is fueled by more infectious emergent Omicron *** concerns of emergent variants include possible recombinants,as genome recombination is an important evolutionary mechanism for the emergence and re-emergence of human viral *** this study,we identified diverse recombination events between two Omicron major subvariants(BA.1 and BA.2)and other variants of concern(VOCs)and variants of interest(VOIs),suggesting that co-infection and subsequent genome recombination play important roles in the ongoing evolution of *** scanning high-quality completed Omicron spike gene sequences.

关键词： subsequent diverse recombination

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P.R.E.S.S. — An R-package for exploring residual-level protein structural statistics

P.R.E.S.S. — An R-package for exploring residual-level prot...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Yuanyuan Huang Steve Bonett Andrzej Kloczkowski Robert Jernigan Zhijun Wu Program on Bioinformatics and Computational Biology Department of Mathematics Iowa State University Ames IA USA Summer REU Program on Computational Systems Biology Iowa State University Ames IA USA Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA Program on Bioinformatics and Computational Biology Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA USA

P.R.E.S.S. is an R package developed to allow researchers to get access to and manipulate on a large set of statistical data on protein residue-level structural properties such as residue-level virtual bond lengths, virtual bond angles, and virtual torsion angles. A large set of high-resolution protein structures are downloaded and surveyed. Their residue-level structural properties are calculated and documented. The statistical distributions and correlations of these properties can be queried and displayed. Tools are also provided for modeling and analyzing a given structure in terms of its residue-level structural properties. In particular, new tools for computing residue-level statistical potentials and displaying residue-level Ramachandran-like plots are developed for structural analysis and refinement. P.R.E.S.S. will be released in R as an open source software package, with a user-friendly GUI, accessible and executable by a public user in any R environment.

关键词： Correlation Proteins Graphical user interfaces Potential energy Protein engineering Software Graphics

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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RETRACTION: RETRACTED: Crystal structure of a Baeyer-Villiger flavin-containing monooxygenase from Staphylococcus aureus MRSA strain MU50 (Retraction of 2014)

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PROTEINS-STRUCTURE FUNCTION AND bioinformatics 2018年第2期86卷 269-269页

作者： Hwang, William C. Xu, Qingping Wu, Bainan Godzik, Adam Bioinformatics Core Joint Center for Structural Genomics (JCSG) La Jolla California 92037 Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute La Jolla California 92037.

The above article from Proteins: Structure, Function, and bioinformatics, published online on 5 August 2014 in Wiley Online Library (http://***/doi/10.1002/prot.24661/full), has been retracted by agreement between William C. Hwang, Qingping Xu, Bainan Wu, Adam Godzik, the Editor‐in‐Chief, Bertrand E. Garcia‐Moreno, and Wiley Periodicals, Inc. The retraction has been agreed because submission was made without agreement from co‐author Adam Godzik.

关键词： Baeyer-Villiger reaction FMO antibiotics resistance methicillin vancomycin

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International Conference on Research in Computational Molecular biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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Evolutionary learning-derived lncRNA signature with biomarker discovery for predicting stage of colon adenocarcinoma

Evolutionary learning-derived lncRNA signature with biomarke...

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Annual International Conference of the IEEE Engineering in Medicine and biology Society (EMBC)

作者： Yann-Lin Ho Yann-Jen Ho Fang-Yu Ko Shinn-Ying Ho Arete Honors Program National Yang Ming Chiao Tung University Hsinchu Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as valuable biomarkers for cancer diagnosis and prognosis prediction. This work proposes an evolutionary learning-based method, EL-COAD, to identify a robust lncRNA signature with biomarker discovery for predicting stages of colon adenocarcinoma (COAD). The COAD patient cohorts were obtained from both the Cancer Genome Atlas and Gene Expression Omnibus (gse17536) databases. EL-COAD incorporates a bi-objective combinatorial genetic algorithm with a support vector machine for selecting a minimal number of lncRNAs while maximizing prediction accuracy. EL-COAD identified a 15-lncRNA signature and achieved a five-fold cross-validation and area under receiver operating characteristic curve of 79.4% and 0.792, respectively. Utilising the 10 lncRNAs from the signature for an independent dataset gse17536, the Sequential Minimal Optimization model achieved a test accuracy of 64.15%. Furthermore, the lncRNAs of the signature were prioritized, with the top five being TMEM105, DUXAP8, APCDD1L-DT, PCAT6, and a novel transcript, ENSG00000226308. Furthermore, both Kyoto Encyclopedia of Genes and Genomes pathway and Disease Ontology analyses provided strong support for the viability of this model-independent signature, emphasising ENSG00000226308 as a promising biomarker.

关键词： Support vector machines Accuracy Regulators RNA Genomics Receivers Biomarkers Colon bioinformatics Cancer

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Mutual dependency between lncRNA LETN and protein NPM1 in controlling the nucleolar structure and functions sustaining cell proliferation

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细胞研究（英文版） 2021年第6期31卷 664-683页

作者： Xianteng Wang Xiaolin Hu Wanlu Song Hui Xu Zhengtao Xiao Rongyao Huang Qingran Bai Fan Zhang Yongzhen Chen Yu Liu Jianhuo Fang Xin Li Qin Shen Haitao Zhao Xuerui Yang MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Tsinghua UniversityBeijing 100084China Center for Synthetic & Systems Biology Tsinghua UniversityBeijing 100084China School of Life Sciences Tsinghua UniversityBeijing 100084China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Beijing 100084China Brain and Spinal Cord Innovative Research Center of Tongji Hospital School of Life Sciences and TechnologyTongji University and Frontier Science Research Center for Stem Cells of Ministry of EducationShanghai 200092China Department of Liver Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC)Beijing 100730China

Fundamental processes such as ribosomal RNA synthesis and chromatin remodeling take place in the nucleolus,which is hyperactive in fast-proliferating *** sophisticated regulatory mechanism underlying the dynamic nucleolar structure and functions is yet to be fully *** present study uncovers the mutual functional dependency between a previously uncharacterized human long non-coding RNA,which we renamed LETN,and a key nucleolar protein,***,being upregulated in multiple types of cancer,LETN resides in the nucleolus via direct binding with *** plays a critical role in facilitating the formation of NPM1 pentamers,which are essential building blocks of the nucleolar granular component and control the nucleolar *** of LETN or NPM1 led to similar and profound changes of the nucleolar morphology and arrest of the nucleolar functions,which led to proliferation inhibition of human cancer cells and neural progenitor ***,this inter-dependency between LETN and NPM1 is associated with the evolutionarily new variations of NPM1 and the coincidental emergence of LETN in higher *** propose that this human-specific protein-lncRNA axis renders an additional yet critical layer of regulation with high physiological relevance in both cancerous and normal developmental processes that require hyperactive nucleoli.

关键词： Cell division Long non-coding RNAs

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Author Correction: Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues

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Nature biotechnology 2025年 2025 May 12页

作者： Lei Chang Yang Xie Brett Taylor Zhaoning Wang Jiachen Sun Ethan J Armand Shreya Mishra Jie Xu Melodi Tastemel Audrey Lie Zane A Gibbs Hannah S Indralingam Tuyet M Tan Rafael Bejar Clark C Chen Frank B Furnari Ming Hu Bing Ren Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA. Medical Scientist Training Program University of California San Diego La Jolla CA USA. Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic Foundation Cleveland OH USA. Systems Biology and Bioinformatics PhD Program Case Western Reserve University School of Medicine Cleveland OH USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Moores Cancer Center UC San Diego La Jolla CA USA. Department of Neurosurgery University of Minnesota Minneapolis MN USA. Department of Medicine University of California San Diego School of Medicine La Jolla CA USA. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA. biren@health.ucsd.edu. Moores Cancer Center UC San Diego La Jolla CA USA. biren@health.ucsd.edu. Center for Epigenomics Institute for Genomic Medicine School of Medicine University of California San Diego La Jolla CA USA. biren@health.ucsd.edu.

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Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects

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Journal of Proteome Research 2024年第1期23卷 301-315页

作者： Wang, Wei-Hsuan Kao, Yi-Chun Hsieh, Chiao-Hui Tsai, Shin-Yu Cheung, Chantal Hoi Yin Huang, Hsuan-Cheng Juan, Hsueh-Fen Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei 106 Taiwan Department of Life Science National Taiwan University Taipei 106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei 106 Taiwan

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1’s inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition. © 2023 American Chemical Society.

关键词： Mdivi-1 multiomics neuroblastoma phosphoproteome proteome targeted metabolome

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Screening of Gold Biomineralization Mechanism in Cyanobacteria

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Procedia Computer Science 2013年 23卷 129-136页

作者： Sivamoke Dissook Krittima Anekthanakul Weerayuth Kittichotirat Bioinformatics and System biology program School of bioresources and technology King Mongkut's University of Technology Thonburi Bangkhunthian Bangkok 10150 Thailand Biotechnology program School of bioresources and technology King Mongkut's University of Technology Thonburi Bangkok 10150 Thailand Systems biology and bioinformatics research group Pilot Plant Development and Training Institute King Mongkut's University of Technology Thonburi Bangkhuntien Bangkok 10150 Thailand

The metal-uptake ability known as biomineralization of microorganisms including fungi, yeasts and bacteria has been known for a long time. This process caused by metal ion stress response from microorganisms offering an efficient way to extract metal in solid state from environment, thus it can be applied in heavy metals bioremediation and precious metal recovery. Since metal biomineralization is the matter of ion stress response from microorganisms and cyanobacteria is one of the most successful organisms on the planet, therefore in this study we try to evaluate the biomineralization potential of cyanobacteria based on their genomes using gold biomineralization as a case study. According to our method we found that there are six cyanobacteria strains which have high potential for gold biomineralization.

关键词： screening tools biomineralization cyanobacteria

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