Annual Review of Biomedical Data Science最新文献_第9页

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

IF 6

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

IF 6

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。

IF 6

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

IF 6

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

IF 6

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

IF 6

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

Opportunities and Challenges of Whole-Cell and -Tissue Simulations of the Outer Retina in Health and Disease 外视网膜全细胞和组织模拟在健康和疾病中的机遇和挑战

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013356

P. Luthert, Luis Serrano, C. Kiel

引用次数: 4

Science as a Culinary Art: How Data Science and Informatics Will Change Knowledge Discovery for Everyone 科学作为烹饪艺术：数据科学和信息学将如何改变每个人的知识发现

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BD-01-041718-100011

N. Tatonetti

{"title":"Science as a Culinary Art: How Data Science and Informatics Will Change Knowledge Discovery for Everyone","authors":"N. Tatonetti","doi":"10.1146/ANNUREV-BD-01-041718-100011","DOIUrl":"https://doi.org/10.1146/ANNUREV-BD-01-041718-100011","url":null,"abstract":"There are 7.6 billion scientists on this planet. Every one of us uses the scientiﬁc method in our daily lives. We are continually forming new hypotheses—the fastest route for the morning commute, the best strategy for keeping an orchid healthy, or the appropriate cooking time for a bone-in ribeye. We then test these hypotheses against our observations, reevaluate and adjust our views, and then do it all over again. Granted, these are not the rigorous randomized experiments used by research laboratories, but not all knowledge comes from controlled studies. The example of cooking is especially interesting, as I personally think culinary science to be humanity’s most advanced. For 1.9 million years (1), nearly every human has come up with new ideas about how to prepare food. Today alone, billions will form hypotheses about the right combination of spices, temperatures,andwinepairings.Eachofthesehypotheseswillbetested,evaluatedfortheirsuccess, and accepted or rejected, ultimately contributing to the body of human culinary knowledge. Imaginehowadvancedmedicinewouldbeifeveryhumanwasequippedtoformandtestbiomedical research hypotheses the way that we do for cooking! Not only would the mass of knowledge be greater, but it would arguably be more useful as well. The knowledge generated would naturally be contextual—in other words, knowledge speciﬁc to particular regions or subpopulations would emerge. Medicine as a scientiﬁc discipline will especially beneﬁt from contextual knowledge. The needs and risks of those living in, say, sub-Saharan Africa are much different than those of Inuits living near the Arctic Circle. The push toward precision medicine is evidence that contextual knowledge is recognized as necessary to advance human health. Contextual knowledge made possible by newly available 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-BD-01-041718-100011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49021964","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: 0

Large-Scale Analysis of Genetic and Clinical Patient Data 遗传和临床患者数据的大规模分析

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013508

M. Ritchie

{"title":"Large-Scale Analysis of Genetic and Clinical Patient Data","authors":"M. Ritchie","doi":"10.1146/ANNUREV-BIODATASCI-080917-013508","DOIUrl":"https://doi.org/10.1146/ANNUREV-BIODATASCI-080917-013508","url":null,"abstract":"Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.","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-080917-013508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46186041","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: 13

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture 从组织到细胞类型再返回:组织结构的单细胞基因表达分析

IF 6

Annual Review of Biomedical Data Science Pub Date : 2018-07-20 DOI: 10.1146/ANNUREV-BIODATASCI-080917-013452

Xi Chen, S. Teichmann, K. Meyer

引用次数: 77