Annual Review of Biomedical Data Science最新文献_第8页

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":" ","pages":""},"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

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

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

Sketching and Sublinear Data Structures in Genomics 基因组学中的草图和亚线性数据结构

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Annual Review of Biomedical Data Science Pub Date : 2019-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021156

G. Marçais, Brad Solomon, Robert Patro, Carl Kingsford

引用次数: 35

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

Scientific Discovery Games for Biomedical Research. 生物医学研究的科学发现游戏。

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Annual Review of Biomedical Data Science Pub Date : 2019-07-01 DOI: 10.1146/annurev-biodatasci-072018-021139

Rhiju Das, Benjamin Keep, Peter Washington, Ingmar H Riedel-Kruse

引用次数: 17

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis RNA测序数据:表达分析的漫游指南

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Annual Review of Biomedical Data Science Pub Date : 2018-10-17 DOI: 10.1146/ANNUREV-BIODATASCI-072018-021255

K. Van den Berge, Katharina M. Hembach, C. Soneson, S. Tiberi, L. Clement, M. Love, Robert Patro, M. Robinson

{"title":"RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis","authors":"K. Van den Berge, Katharina M. Hembach, C. Soneson, S. Tiberi, L. Clement, M. Love, Robert Patro, M. Robinson","doi":"10.1146/ANNUREV-BIODATASCI-072018-021255","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-072018-021255","url":null,"abstract":"Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-072018-021255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48762878","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: 83

Visualization of Biomedical Data 生物医学数据的可视化

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013424

S. O’Donoghue, B. Baldi, S. Clark, A. Darling, J. Hogan, Sandeep Kaur, L. Maier-Hein, Davis J. McCarthy, W. Moore, Esther Stenau, J. Swedlow, Jenny Vuong, J. Procter

{"title":"Visualization of Biomedical Data","authors":"S. O’Donoghue, B. Baldi, S. Clark, A. Darling, J. Hogan, Sandeep Kaur, L. Maier-Hein, Davis J. McCarthy, W. Moore, Esther Stenau, J. Swedlow, Jenny Vuong, J. Procter","doi":"10.1146/ANNUREV-BIODATASCI-080917-013424","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013424","url":null,"abstract":"The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013424","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48064895","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: 63

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data 理解基于质谱的霰弹枪蛋白质组学数据的计算方法

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Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013516

Pavel Sinitcyn, J. Rudolph, J. Cox

{"title":"Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data","authors":"Pavel Sinitcyn, J. Rudolph, J. Cox","doi":"10.1146/ANNUREV-BIODATASCI-080917-013516","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013516","url":null,"abstract":"Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":" ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/ANNUREV-BIODATASCI-080917-013516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43457511","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: 102