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第五届全国生物信息学与系统生物学学术大会

作者： Xinjun Zhang Huiying Zhao Yadong Wang Guohua Wang Yaoqi Zhou Yunlong Liu School of Computer Sciences and Technology Harbin Institute of Technology Harbin Heilongjiang 150001 China School of Informatics and Computing Indiana University Bloomington IN 47408 USA School of Computer Sciences and Technology Harbin Institute of Technology Harbin Heilongjiang 150001 China Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 USA Bioinformatics program School of Informatics Indiana University Purdue University Indianapolis Indianapolis IN 46202 USA Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis IN 46202 USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 USA Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN 46202 USA

Background: Small insertions and deletions (INDELs) compose of the second largest category of genetic variants (next to single nucleotide polymorphism) in the human genome, which accounts for 18% of all the variants *** from the 1,000 Genome Project indicated an estimate of 1 million INDELs per human *** to SNPs (Single Nucleotide Polymorphisms) and large structural variations, INDELs are of significant interest due to their potentials in affecting gene molecular functions and therefore cause *** of its functional importance, lacking a highly accurate bioinformatics tool for INDEL classifications has become a major obstacle in understanding the molecular ***: In this study, we develop a bioinformatics tool for INDEL *** constructed a training dataset that includes all the disease-causing and neutral INDELs documented in the Human Genome Mutation Database (HGMD) and 1,000 Genome *** the two sets of INDELs, we systematically investigated a series of features on their potentials in disrupting both protein structures and pre-mRNA *** then designed a predictor to best distinguish the disease-causing and neutral INDELs using machine-learning ***: In this study, we focused on the disease relevance of the INDELs in the exonic regions, which include 25,923 and 4,643 disease-causing and neutral INDELs, *** found that the disease-causing INDELs more likely occur in predicted structured regions and neutral INDELs are more likely in unstructured, intrinsically disordered *** also found that INDEL sites matched more to homologous sequences are more like *** RNA processing, we found disease-causing INDELs are twice likely to disrupt binding sites of RNA-binding *** also found that disease-causing INDELs tend to be longer, and be closer to the splice *** addition, the genomic loci for disease-causing INDELs are more evolutionarily conserved, a

关键词： insertion deletions INDELs genetic variants next generation sequencing

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Unexpected failure rates for modular assembly of engineered zinc fingers (vol 5, pg 374, 2008)

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NATURE METHODS 2008年第6期5卷 575-575页

作者： Ramirez, Cherie L. Foley, Jonathan E. Wright, David A. Mueller-Lerch, Felix Rahman, Shamim H. Cornu, Tatjana I. Winfrey, Ronnie J. Sander, Jeffry D. Fu, Fengli Townsend, Jeffrey A. Cathomen, Toni Voytas, Daniel F. Joung, J. Keith These authors contributed equally to this work. These authors contributed equally to this work. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Interdepartmental Graduate Program in Bioinformatics and Computational Biology 2114 Molecular Biology Building Iowa State University Ames Iowa 50011 USA. Interdepartmental Graduate Program in Bioinformatics and Computational Biology 2114 Molecular Biology Building Iowa State University Ames Iowa 50011 USA. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Charité Medical School Institute of Virology Hindenburgdamm 27 D-12203 Berlin Germany. Department of Genetics Development and Cell Biology Iowa State University 1035A Roy J. Carver Co-Lab Ames Iowa 50011 USA. Department of Pathology Harvard Medical School Boston Massachusetts 02115 USA.

A letter to the editor is presented in response to an article on zinc fingers in a previous issue.

关键词： LETTERS to the editor ZINC

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Multistate modeling and simulation for regulatory networks 10

Multistate modeling and simulation for regulatory networks

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Simulation Winter Conference

作者： Zhen Liu Umme Juka Mobassera Clifford A. Shaffer Layne T. Watson Yang Cao Department of Computer Science Virginia Technology Blacksburg VA USA Program in Genetics Bioinformatics and Computational Biology Virginia Technology Blacksburg VA USA Department of Computer Science Department of Mathematics Virginia Technology Blacksburg VA USA

ISBN: (纸本)9781424498642

Many protein regulatory models contain chemical species best represented as having multiple states. Such models stem from the potential for multiple levels of phosphorylation or from the formation of multiprotein complexes. We seek to support such models by augmenting an existing modeling and simulation system. Interactions between multistate species can lead to a combinatorial explosion in the potential state space. This creates a challenge when using Gillespie's stochastic simulation algorithm (SSA). Both the network-free algorithm (NFA) and various rules-based methods have been proposed to more efficiently simulate such models. We show how to further improve NFA to integrate population-based and particle-based features. We then present a population-based scheme for the stochastic simulation of rule-based models. A complexity analysis is presented comparing the proposed simulation methods. We present numerical experiments for two sample models that demonstrate the power of the proposed simulation methods.

关键词： Mathematical model Biological system modeling Numerical models Chemicals Stochastic processes Equations Proteins

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Distinct adipose progenitor cells emerging with age drive active adipogenesis

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Science (New York, N.Y.) 2025年第6745期388卷 eadj0430页

作者： Wang, Guan Li, Gaoyan Song, Anying Zhao, Yutian Yu, Jiayu Wang, Yifan Dai, Wenting Salas, Martha Qin, Hanjun Medrano, Leonard Dow, Joan Li, Aimin Armstrong, Brian Fueger, Patrick T. Yu, Hua Zhu, Yi Shao, Mengle Wu, Xiwei Jiang, Lei Campisi, Judith Yang, Xia Wang, Qiong A. Department of Molecular and Cellular Endocrinology Arthur Riggs Diabetes and Metabolism Research Institute City of Hope Medical Center Duarte CA USA Department of Integrative Biology and Physiology University of California Los Angeles CA United States Light Microscopy Core City of Hope Medical Center Duarte CA USA The Integrative Genomics Core City of Hope Medical Center Duarte CA USA Division of Developmental and Translational Diabetes and Endocrinology Research City of Hope Medical Center Duarte CA USA Comprehensive Metabolic Phenotyping Core City of Hope Medical Center Duarte CA USA Pathology Core of Shared Resources City of Hope Medical Center Duarte CA USA Comprehensive Cancer Center Beckman Research Institute City of Hope Medical Center Duarte CA USA Children's Nutrition Research Center Department of Pediatrics Baylor College of Medicine Houston TX United States Key Laboratory of Immune Response and Immunotherapy Shanghai Institute of Immunity and Infection Chinese Academy of Sciences Shanghai China Buck Institute for Research on Aging Novato CA United States Bioinformatics Interdepartmental Program University of California Los Angeles CA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles CA United States Molecular and Medical Pharmacology University of California Los Angeles CA United States

Starting at middle age, adults often suffer from visceral adiposity and associated adverse metabolic disorders. Lineage tracing in mice revealed that adipose progenitor cells (APCs) in visceral fat undergo extensive adipogenesis during middle age. Thus, despite the low turnover rate of adipocytes in young adults, adipogenesis is unlocked during middle age. Transplantations quantitatively showed that APCs in middle-aged mice exhibited high adipogenic capacity cell-autonomously. Single-cell RNA sequencing identified a distinct APC population, the committed preadipocyte, age-enriched (CP-A), emerging at this age. CP-As demonstrated elevated proliferation and adipogenesis activity. Pharmacological and genetic manipulations indicated that leukemia inhibitory factor receptor signaling was indispensable for CP-A adipogenesis and visceral fat expansion. These findings uncover a fundamental mechanism of age-dependent adipose remodeling, offering critical insights into age-related metabolic diseases.

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Bayesian Spectral Graph Denoising with Smoothness Prior

Bayesian Spectral Graph Denoising with Smoothness Prior

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Annual Conference on Information Sciences and Systems (CISS)

作者： Sam Leone Xingzhi Sun Michael Perlmutter Smita Krishnaswamy Program for Applied Mathematics Yale University Department of Computer Science Yale University Department of Mathematics Boise State University Department of Genetics Yale School of Medicine Wu Tsai Institute Yale University FAIR Meta AI Computational Biology and Bioinformatics Program Yale University

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms’ ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mikyung Je Myeongji Cho Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.

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Tegavivint triggers TECR-dependent nonapoptotic cancer cell death

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Nature chemical biology 2025年 2025 May 26页

作者： Logan Leak Ziwei Wang Alby J Joseph Brianna Johnson Alyssa A Chan Cassandra M Decosto Leslie Magtanong Pin-Joe Ko Weaverly Colleen Lee Joan Ritho Sophia Manukian Alec Millner Shweta Chitkara Jennifer J Salinas Rachid Skouta Matthew G Rees Melissa M Ronan Jennifer A Roth Chad L Myers Jason Moffat Charles Boone Steven J Bensinger David A Nathanson G Ekin Atilla-Gokcumen Everett J Moding Scott J Dixon Department of Biology Stanford University Stanford CA USA. Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA. Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA. Department of Molecular and Medical Pharmacology University of California Los Angeles Los Angeles CA USA. Department of Chemistry University of Massachusetts Amherst Amherst MA USA. Department of Biology University of Massachusetts Amherst Amherst MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin Cities Minneapolis MN USA. Program in Genetics & Genome Biology The Hospital for Sick Children Toronto Ontario Canada. Department of Molecular Genetics University of Toronto Toronto Ontario Canada. Donnelly Centre University of Toronto Toronto Ontario Canada. RIKEN Center for Sustainable Resource Science Saitama Japan. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA USA. UCLA Lipidomics Laboratory University of California Los Angeles Los Angeles CA USA. Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Biology Stanford University Stanford CA USA. sjdixon@stanford.edu.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

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Dynamic mechanical finite element model of biological cells for studying cellular pattern formation

Dynamic mechanical finite element model of biological cells ...

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

作者： Jieling Zhao Hammad Naveed Sema Kachalo Youfang Cao Wei Tian Jie Liang Bioinformatics Program Department of Broengineering University of Illinois at Chicago CAS-MPG partner Institute for Computational Biology Intel Graphics Unit Intel Corporation Department of Bioengineering University of Illinois at Chicago Faculty of Department of Bioengineering University of Illinois at Chicago

ISBN: (纸本)9781457702150

Understanding the geometric, topologic, and mechanical properties of cells and their interactions is critical for studying tissue pattern formation and organ development. computational model and tools for simulating cell pattern formation have broad implications in studying embryogenesis, blood-vessel development, tissue regeneration, and tumor growth. Although a number of cell modeling methods exist, they do not simultaneously account for detailed cellular shapes as well as dynamic changes in cell geometry and topology. Here we describe a dynamic finite element cell model (dFEMC) for studying populations of cells and tissue development. By incorporating details of cell shape, cell growth and shrinkage, cell birth and death, cell division and fusion, our method can model realistically a variety problems of cell pattern formation. We give two examples of applying our method to the study of cell fusion and cell apoptosis. The dFEMC model developed here provides a general computational framework for studying dynamics pattern formation of tissue.

关键词： Pattern formation embryo pattern formation cytology Mechanical properties Histogenic Process Cell Fusion Organogenesis

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ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics

arXiv

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arXiv 2024年

作者： Viswanath, Siddharth Bhaskar, Dhananjay Johnson, David R. Rocha, João Felipe Castro, Egbert Grady, Jackson D. Grigas, Alex T. Perlmutter, Michael A. O’Hern, Corey S. Krishnaswamy, Smita Department of Computer Science Yale University United States Department of Genetics Yale University United States Program in Computing Boise State University United States Computational Biology and Bioinformatics Program Yale University United States Department of Mathematics Boise State University United States

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations. Importantly, we demonstrate that ProtSCAPE generalizes effectively from short to long trajectories and from wild-type to mutant proteins, surpassing traditional approaches by delivering more precise and interpretable upsampling of dynamics. © 2024, CC BY.

关键词： Molecular docking

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Expanding the computational Toolbox for Single-Particle Cryo-Electron Microscopy

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Microscopy and Microanalysis 2019年第S2期25卷 2686-2687页

作者： Alberto Bartesaghi Department of Computer Science Trinity College of Arts & Sciences Department of Biochemistry Duke University School of Medicine Department of Electrical and Computer Engineering Pratt School of Engineering Duke Institute for Brain Sciences Computational Biology and Bioinformatics Program Duke University Durham NC USA.

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