检索结果-内蒙古大学图书馆

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Hematopoietic Stem Cells in JAK2-Mutant Myeloproliferative Neoplasms

引用

BLOOD 2020年 136卷 7-8页

作者： Van Egeren, Debra Escabi, Javier Nguyen, Maximilian Liu, Shichen Reilly, Christopher R. Patel, Sachin Kamaz, Baransel Kalyva, Maria DeAngelo, Daniel J. Galinsky, Ilene Wadleigh, Martha Winer, Eric S. Luskin, Marlise R. Stone, Richard M. Garcia, Jacqueline S. Hobbs, Gabriela S. Camargo, Fernando D. Michor, Franziska Mullally, Ann Cortes-Ciriano, Isidro Hormoz, Sahand Department of Systems Biology Harvard Medical School Boston Princeton University Princeton Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Boston Department of Medical Oncology Dana-Farber Cancer Institute Boston Hematology/Oncology Department and Stem Cell Program Boston Children's Hospital Boston Division of Hematology Brigham and Women's Hospital Boston European Bioinformatics Institute Cambridge United Kingdom Department of Medical Oncology Dana-Farber Cancer Inst. Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Leukemia Center Massachusetts General Hospital Boston MA

*equal contribution, # co-corresponding authors Although the JAK2V617F mutation is the most common MPN phenotypic driver mutation, the precise consequences of the mutation on the behavior of individual human hematopoietic stem cells (HSCs) in vivo remains unknown. We used whole genome sequencing and single-cell profiling of hematopoietic stem and progenitor cells (HSPCs) to quantify the impact of JAK2V617F on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual newly diagnosed MPN patients. We reconstructed the lineage history of individual HSCs obtained from patients with newly diagnosed essential thrombocythemia (ET), using the pattern of spontaneous somatic mutations accrued in their genomes over decades (Figure 1). Intriguingly, our analysis indicates that the JAK2V617F mutation occurred in a single HSC many years before MPN diagnosis - at age 9±2 years in a 34 year-old patient, and at age 19±3 years in a 63 year-old patient. In each patient, we inferred the number of mutated HSCs over the years and computed their fitness. After escaping stochastic extinction, the population of mutated HSCs grew exponentially by 63±15% and 44±13% every year in the two patients respectively. To contrast the differentiation trajectories of the JAK2 -mutant HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single HSPCs in the two ET patients in whom we had performed whole genome sequencing and in one additional ET patient (N=3 total) and also in patients with polycythemia vera (PV) (N=3). We observed, at the time of MPN diagnosis, a consistent lineage bias of JAK2 -mutant HSPCs toward megakaryocyte-erythrocyte fate, across ET and PV patients. Exploiting our ability to discriminate JAK2 -mutant cells from JAK2 wild-type cells within individual MPN patients, we identified genes involved in antigen presentation and inflammation as differentially up-regulated in JAK2 -mutant HS

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex

引用

Cell Genomics 2025年第6期5卷 100879页

作者： Johansen, Nelson J. Kempynck, Niklas Zemke, Nathan R. Somasundaram, Saroja De Winter, Seppe Hooper, Marcus Dwivedi, Deepanjali Lohia, Ruchi Wehbe, Fabien Li, Bocheng Abaffyová, Darina Armand, Ethan J. De Man, Julie Ekşi, Eren Can Hecker, Nikolai Hulselmans, Gert Konstantakos, Vasilis Mauduit, David Mich, John K. Partel, Gabriele Daigle, Tanya L. Levi, Boaz P. Zhang, Kai Tanaka, Yoshiaki Gillis, Jesse Ting, Jonathan T. Ben-Simon, Yoav Miller, Jeremy Ecker, Joseph R. Ren, Bing Aerts, Stein Lein, Ed S. Tasic, Bosiljka Bakken, Trygve E. Allen Institute for Brain Science Seattle 98109 WA United States VIB Center for AI & Computational Biology VIB-KU Leuven Center for Brain and Disease Research & KU Leuven Department of Human Genetics Leuven Belgium Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla 92093 CA United States Physiology Department and Donnelly Centre for Cellular and Biomolecular Research University of Toronto Toronto ON Canada Maisonneuve-Rosemont Hospital Research Centre University of Montreal Montreal QC Canada School of Life Sciences Westlake University Zhejiang Hangzhou China Bioinformatics and Systems Biology Program University of California San Diego La Jolla 92093 CA United States Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Physiology and Biophysics University of Washington Seattle 98195 WA United States Salk Institute for Biological Studies La Jolla 92037 CA United States

Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the “Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics” to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex. © 2025 The Author(s)

关键词： ATAC-seq cortex cross-species DNA sequence model enhancer-AAV prediction benchmark single-cell multiomics TF codes

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of gene regulation in single cells using Compass

引用

Cell Reports Methods 2025年第5期5卷 101035页

作者： Wan, Changxin Qu, Yilong Ye, Zhiyou Zhang, Tianbei Ma, Huifang Chen, Ming Hou, Wenpin Ji, Zhicheng Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC United States Program of Computational Biology and Bioinformatics Duke University School of Medicine Durham NC United States Department of Biomedical Engineering Pratt School of Engineering Duke University Durham NC United States Department of Pathology Duke University School of Medicine Durham NC United States Duke Cancer Institute Duke University Durham NC United States Department of Biostatistics Columbia University Mailman School of Public Health New York City NY United States

Single-cell multi-omics is a transformative technology that measures both gene expression and chromatin accessibility in individual cells. However, most studies concentrate on a single tissue and are unable to determine whether a gene is regulated by a cis-regulatory element (CRE) in just one tissue or across multiple tissues. We developed Compass for comparative analysis of gene regulation across a large number of human and mouse tissues. Compass consists of a database, CompassDB, and an open-source R software package, CompassR. CompassDB contains processed single-cell multi-omics data of more than 2.8 million cells from hundreds of cell types. Building upon CompassDB, CompassR enables visualization and comparison of gene regulation across multiple tissues. We demonstrated that CompassR can identify CRE-gene linkages specific to a tissue type and their associated transcription factors in real examples. © 2025 The Author(s)

关键词： cis-regulatory elements CP: Systems biology gene regulation single-cell ATAC-seq single-cell multi-omics

来源：评论

学校读者我要写书评

暂无评论

Universal mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

arXiv

引用

arXiv 2022年

作者： Jin, Weiwei Datye, Amit Schwarz, Udo D. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemical and Environmental Engineering Yale University New HavenCT06520 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Experimental data on the compressive strength σmax versus strain rate Ε eng for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasses, σmax decreases with increasing Ε eng, for others, σmax increases with increasing Ε eng, and for others σmax versus Ε eng is nonmonotonic. Using numerical simulations of metallic glasses undergoing uniaxial compression at nonzero strain rate and temperature, we show that they obey a universal relation for the compressive strength versus temperature, which determines their mechanical response. At low Ε eng, increasing strain rate leads to increases in temperature and decreases in σmax∗, whereas at high Ε eng, increasing strain rate leads to decreases in temperature and increases in σmax∗. This non-monotonic behavior of σmax∗ versus temperature causes the nonmonotonic behavior of σmax∗ versus Ε eng. Variations in the internal dissipation change the characteristic strain rate at which the nonmonotonic behavior occurs. These results are general for a wide range of metallic glasses with different atomic interactions, damping coefficients, and chemical compositions. © 2022, CC BY.

关键词： Strain rate

来源：评论

学校读者我要写书评

暂无评论

Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

引用

arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

来源：评论

学校读者我要写书评

暂无评论

Resisting efficiency’s overreach

引用

Science 2023年第6663期381卷 1162页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies and Harry Radzyner Law School Reichman University Herzliya Israel. The reviewer is at the Department of Molecular Biophysics and Biochemistry Yale University New Haven CT USA. The reviewer is at the Program in Computational Biology and Bioinformatics and Department of Computer Science Yale University New Haven CT USA.

来源：评论

学校读者我要写书评

暂无评论

White Paper on Enabling Global Image Data Sharing in the Life Sciences

arXiv

引用

arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A general kernel boosting framework integrating pathways for predictive modeling based on genomic data

arXiv

引用

arXiv 2020年

作者： Zeng, Li Yu, Zhaolong Zhang, Yiliang Zhao, Hongyu Department of Biostatistics Yale University New HavenCT06511 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Genetics Yale School of Medicine New HavenCT06510 United States

Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently. Analysis incorporating pathway information can boost discovery power and better connect new findings with biological mechanisms. In this article, we propose a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. We introduce appropriate loss functions and optimization procedures for different outcome types. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure. Through extensive simulations and case studies in drug response and cancer survival datasets, we demonstrate that PKB can substantially outperform other competing methods, better identify biological pathways related to drug response and patient survival, and provide novel insights into cancer pathogenesis and treatment response. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

引用

Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

来源：评论

学校读者我要写书评

暂无评论

Algal biomass based bio-refineries: Concurrent pre-treatment strategies and perspectives for sustainable feedstock

引用

Biomass and Bioenergy 2024年 187卷

作者： Shashikumar, Uday Ranjan, Kumar Rakesh Sharma, Ankit Naina Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Biswas, Shelly Ponnusamy, Vinoth Kumar Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung City 807 Taiwan Department of Chemistry Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida201303 India Department of Mechanical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Institute of Environmental Engineering National Sun Yat-Sen University Taiwan Master Program of Green Energy Science and Technology Feng Chia University Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Department of Space Engineering and Rocketry Birla Institute of Technology Mesra Jharkhand Ranchi835215 India 807 Kaohsiung City Taiwan 807 Kaohsiung City Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung City81157 Taiwan Department of Food Science National Pingtung University of Science and Technology Pingtung91201 Taiwan

The quest for viable and scalable biofuel sources has been at the forefront of scientific innovation for the past three decades. Due to its rich chemical constituents, microalgal biomass has emerged as a pivotal sustainable and scalable feedstock for biorefineries. This comprehensive review critically analyzes the different types of microalgae feedstock, concurrent extraction technologies, bio-pre-treatment procedures, and the key chemical and physical parameters influencing lipid formation and algal biofuel production. We propose a novel approach of photo-initiated culturing of algal biomass using photobioreactors (PBRs) to address the limitations of concurrent space and time-related constraints. The innovative photo bio-refinery strategy presented herein aims to enhance sustainability factors while minimizing emissions, catering to the needs of futuristic non-electric vehicles. A comparative quality analysis of microalgae-derived biofuel against conventional fossil fuels and other biofuels is conducted, considering chemical, environmental, economic, and social perspectives. Furthermore, we elucidate the efficacy of bio-pre-treatment strategies such as dehydration, hydrothermal liquefaction, pyrolysis, and gasification in optimizing biofuel production. The proposed photo biorefineries exhibit the potential to yield a diverse range of value-added products, including biodiesel, biogases, bio-fertilizers, bio-pesticides, bio-alcohols, dyes, proteins, carotenoids, and drug vitals. This review provides a comprehensive framework for the development of sustainable and efficient microalgae-based biorefineries, paving the way for a greener and more economically viable future in the biofuel industry. © 2024 Elsevier Ltd

关键词： Feedstocks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：