Annual Review of Biomedical Data Science最新文献_第10页

Knowledge-Based Biomedical Data Science. 基于知识的生物医学数据科学。

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Annual Review of Biomedical Data Science Pub Date : 2020-07-01 Epub Date: 2020-04-07 DOI: 10.1146/annurev-biodatasci-010820-091627

Tiffany J Callahan, Ignacio J Tripodi, Harrison Pielke-Lombardo, Lawrence E Hunter

引用次数: 0

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis. 通过单细胞测序综合分析解密细胞命运决策

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Annual Review of Biomedical Data Science Pub Date : 2020-07-01 Epub Date: 2020-03-02 DOI: 10.1146/annurev-biodatasci-111419-091750

Sagar, Dominic Grün

{"title":"Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis.","authors":"Sagar, Dominic Grün","doi":"10.1146/annurev-biodatasci-111419-091750","DOIUrl":"10.1146/annurev-biodatasci-111419-091750","url":null,"abstract":"Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during de- velopment, regeneration, homeostasis, and disease is a central goal of mod- ern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality re- duction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single- cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115822/pdf/EMS86588.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38260582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Immunoinformatics: Predicting Peptide-MHC Binding. 免疫信息学:预测多肽- mhc结合。

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Annual Review of Biomedical Data Science Pub Date : 2020-07-01 DOI: 10.1146/annurev-biodatasci-021920-100259

Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus

{"title":"Immunoinformatics: Predicting Peptide-MHC Binding.","authors":"Morten Nielsen, Massimo Andreatta, Bjoern Peters, Søren Buus","doi":"10.1146/annurev-biodatasci-021920-100259","DOIUrl":"https://doi.org/10.1146/annurev-biodatasci-021920-100259","url":null,"abstract":"Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/annurev-biodatasci-021920-100259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9809194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 48

Integrating Imaging and Omics: Computational Methods and Challenges 整合成像和组学:计算方法和挑战

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013328

J. Hériché, S. Alexander, J. Ellenberg

{"title":"Integrating Imaging and Omics: Computational Methods and Challenges","authors":"J. Hériché, S. Alexander, J. Ellenberg","doi":"10.1146/ANNUREV-BIODATASCI-080917-013328","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013328","url":null,"abstract":"Fluorescence microscopy imaging has long been complementary to DNA sequencing- and mass spectrometry–based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43681862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science 生物分子数据资源：生物医学数据科学的生物信息学基础设施

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021321

J. Vamathevan, R. Apweiler, E. Birney

{"title":"Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science","authors":"J. Vamathevan, R. Apweiler, E. Birney","doi":"10.1146/ANNUREV-BIODATASCI-072018-021321","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-072018-021321","url":null,"abstract":"Technological advances have continuously driven the generation of bio-molecular data and the development of bioinformatics infrastructure, which enables data reuse for scientific discovery. Several types of data management resources have arisen, such as data deposition databases, added-value databases or knowledgebases, and biology-driven portals. In this review, we provide a unique overview of the gradual evolution of these resources and discuss the goals and features that must be considered in their development. With the increasing application of genomics in the health care context and with 60 to 500 million whole genomes estimated to be sequenced by 2022, biomedical research infrastructure is transforming, too. Systems for federated access, portable tools, provision of reference data, and interpretation tools will enable researchers to derive maximal benefits from these data. Collaboration, coordination, and sustainability of data resources are key to ensure that biomedical knowledge management can scale with technology shifts and growing data volumes.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-072018-021321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45228710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Connectivity Mapping: Methods and Applications 连通性映射：方法与应用

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021211

A. Keenan, Megan L. Wojciechowicz, Zichen Wang, Kathleen M. Jagodnik, S. L. Jenkins, Alexander Lachmann, Avi Ma’ayan

{"title":"Connectivity Mapping: Methods and Applications","authors":"A. Keenan, Megan L. Wojciechowicz, Zichen Wang, Kathleen M. Jagodnik, S. L. Jenkins, Alexander Lachmann, Avi Ma’ayan","doi":"10.1146/ANNUREV-BIODATASCI-072018-021211","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-072018-021211","url":null,"abstract":"Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2019-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-072018-021211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49485099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

Molecular Heterogeneity in Large-Scale Biological Data: Techniques and Applications 大规模生物学数据中的分子异质性：技术与应用

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021339

C. Deng, Timothy P. Daley, G. Brandine, Andrew D. Smith

引用次数: 5

Discovering Pathway and Cell Type Signatures in Transcriptomic Compendia with Machine Learning 利用机器学习发现转录组简编中的通路和细胞类型特征

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021348

G. Way, C. Greene

引用次数: 10

Imaging, Visualization, and Computation in Developmental Biology 发育生物学中的成像、可视化和计算

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Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021305

F. Cutrale, S. Fraser, Le A. Trinh

引用次数: 13

Genomic Data Compression 基因组数据压缩

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Annual Review of Biomedical Data Science Pub Date : 2019-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021229

M. Hernaez, Dmitri S. Pavlichin, T. Weissman, Idoia Ochoa

引用次数: 22