- ISBN：9787030359308
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 开本：其他
- 页数：604
- 出版时间：2022-12-01
- 条形码：9787030359308 ; 978-7-03-035930-8
内容简介
组学数据生物信息学交叉融合了多个学科领域，包括分子生物学、应用信息学和统计学等。本书汇集了经验丰富的研究人员，针对这一复杂的研究方向提供了实用的指南。本书分为三个部分：1.组学生物信息学基础：数据分析的核心策略、标准化和数据管理的指导方针，组学分析的统计学基础。2.组学数据和分析策略：特定组学数据的生物信息学分析方法，基因组、转录组、蛋白质组和代谢组水平的分析。3.组学生物信息学应用：综合应用的实例分类，人类疾病中关于生物标记和目标鉴定的案例研究。作为《分子生物学方法》系列丛书的一卷，本书各章均包含针对标题的导言、推荐材料与试剂的清单、分步骤且易于操作的实验室方案、疑难问题的注意事项和易犯失误的避免。本书专业权威、简明易懂，为各种专业背景的研究人员提供了理想的指南，旨在传递适合该领域的研究方法。
目录
前言 v
撰稿人 ix
**篇 组学生物信息学基础
**章 组学技术、数据和生物信息学原理 3
第二章 组学数据的数据标准：数据共享和重用 3l
第三章 组学数据管理和注释 7l
第四章 交叉组学研究项目的数据和知识管理 97
第五章 组学数据的统计分析原理 ll3
第六章 不同层次组学数据综合分析的统计方法和模型 l33
第七章 时序组学数据集的分析 l53
第八章 “组学”术语的恰当使用 l73
第二篇 几种常用组学数据及分析方法
第九章 高通量测序数据的计算分析 199
第十章 对照研究中的单核苷酸多态性分析 219
第十一章 拷贝数变异数据的生物信息学分析 235
第十二章 基于免疫共沉淀的芯片数据处理：从原始图像生成到分析结果浏览 25l
第十三章 基于基因表达谱的全局机制分析和疾病相关性 269
第十四章 转录组数据的生物信息学分析 299
第十五章 定性和定量蛋白组数据的生物信息学分析 33l
第十六章 质谱数据代谢组数据的生物信息学分析 35l
第三篇 实用组学生物信息学
第十七章 组掌数据处理过程中的计算分析流程 379
第十八章 组学数据的整合、储存和分析策略 399
第十九章 信号通路、相互作用网络构建和功能分析研究中组学数据的整合 415
第二十章 时间依赖型组学数据的网络推断 435
第二十一章 组学和文献挖掘 457
第二十二章 组学和生物信息学在临床数据处理中的应用 479
第二十三章 基于组学的病理和生理过程分析 499
第二十四章 基于组学的生物标记发现中的数据挖掘方法 5ll
第二十五章 癌症靶标识别的综合生物信息学分析 527
第二十六章 基于组学的分子靶标和生物标记鉴定 547
索引 573
（罗静初 译）
Contents
Preface v
Contributors ix
PART I OMICS BIOINFORMATICS FUNDAMENTALS
1 Omics Technologies， Data and Bioinformatics Principles 3
Maria V.Schneider and Sandra Orchard
2 Data Standards for Omics Data: The Basis of Data Sharing and Reuse 31
Stephen A.Chervitz， Eric W.Deutsch， Dawn Field， Helen Parkinson，John Quackenbush， Phillipe Rocca-Serra， Susanna-Assunta Sansone，Christian J.Stoeckert， Jr.， Chris F.Taylor， Ronald Taylor，and Catherine A.Ball
3 Omics Data Management and Annotation 71
Arye Harel， Irina Dalah， Shmuel Pietrokovski， Marilyn Safran，and Doron Lancet
4 Data and Knowledge Management in Cross-Omics Research Projects 97
Martin Wiesinger， Martin Haiduk， Marco Behr， Henrique Lopes de Abreu Madeira， Gernot Glockler， Paul Perco， and Arno Lukas
5 Statistical Analysis Principles for Omics Data 113
Daniela Dunkler， Fatima Sanchez-Cabo， and Georg Heinze
6 Statistical Methods and Models for Bridging Omics Data Levels 133
Simon Rogers
7 Analysis of Time Course Omics Datasets 153
Martin G.Grigorov
8 The Use and Abuse of-Omes 173
Sonja J.Prohaska and Peter F.Stadler
PART II OMICS DATA AND ANALYSIS TRACKS
9 Computational Analysis of High Throughput Sequencing Data 199
Steve Hoffmann
10 Analysis of Single Nucleotide Polymorphisms in Case–Control Studies 219
Yonghong Li， Dov Shiffman， and Rainer Oberbauer
11 Bioinformatics for Copy Number Variation Data 235
Melissa Warden， Roger Pique-Regi， Antonio Ortega，and Shahab Asgharzadeh
12 Processing ChIP-Chip Data: From the Scanner to the Browser 251
Pierre Cauchy， Touati Benoukraf， and Pierre Ferrier
13 Insights Into Global Mechanisms and Disease by Gene Expression Profiling 269
Fatima Sanchez-Cabo， Johannes Rainer， Ana Dopazo，Zlatko Trajanoski， and Hubert Hackl
14 Bioinformatics for RNomics 299
Kristin Reiche， Katharina Schutt， Kerstin Boll，Friedemann Horn， and Jorg Hackermüller
15 Bioinformatics for Qualitative and Quantitative Proteomics 331
Chris Bielow， Clemens Gropl， Oliver Kohlbacher， and Knut Reinert
16 Bioinformatics for Mass Spectrometry-Based Metabolomics 351
David P.Enot， Bernd Haas， and Klaus M.Weinberger
PART III APPLIED OMICS BIOINFORMATICS
17 Computational Analysis Workflows for Omics Data Interpretation 379
Irmgard Mühlberger， Julia Wilflingseder， Andreas Bernthaler，Raul Fechete， Arno Lukas， and Paul Perco
18 Integration， Warehousing， and Analysis Strategies of Omics Data 399
Srinubabu Gedela
19 Integrating Omics Data for Signaling Pathways， Interactome Reconstruction，and Functional Analysis 415
Paolo Tieri， Alberto de la Fuente， Alberto Termanini，and Claudio Franceschi
20 Network Inference from Time-Dependent Omics Data 435
Paola Lecca， Thanh-Phuong Nguyen， Corrado Priami， and Paola Quaglia
21 Omics and Literature Mining 457
Vinod Kumar
22 Omics–Bioinformatics in the Context of Clinical Data 479
Gert Mayer， Georg Heinze， Harald Mischak， Merel E.Hellemons，Hiddo J.Lambers Heerspink， Stephan J.L.Bakker， Dick de Zeeuw，Martin Haiduk， Peter Rossing， and Rainer Oberbauer
23 Omics-Based Identification of Pathophysiological Processes 499
Hiroshi Tanaka and Soichi Ogishima
24 Data Mining Methods in Omics-Based Biomarker Discovery 511
Fan Zhang and Jake Y.Chen
25 Integrated Bioinformatics Analysis for Cancer Target Identification 527
Yongliang Yang， S.James Adelstein， and Amin I.Kassis
26 Omics-Based Molecular Target and Biomarker Identification 547
Zgang–Zhi Hu， Hongzhan Huang， Cathy H.Wu， Mira Jung，Anatoly Dritschilo， Anna T.Riegel， and Anton Wellstein
Index 573
节选
Part I Omics Bioinformatics Fundamentals Chapter 1 Omics Technologies， Data and Bioinformatics Principles Maria V. Schneider and Sandra Orchard Abstract We provide an overview on the state of the art for the Omics technologies， the types of omics data and the bioinformatics resources relevant and related to Omics. We also illustrate the bioinformatics chal-lenges ofdealing with high-throughput data. This overview touches several fundamental aspects ofOmics and bioinformatics: data standardisation， data sharing， storing Omics data appropriately and exploring Omics data in bioinformatics. Though the principles and concepts presented are true for the various dif-ferent technological fields， we concentrate in three main Omics fields namely: genomics， transcriptomics and proteomics. Finally we address the integration of Omics data， and provide several useful links for bioinformatics and Omics. Key words: Omics， Bioinformatics， High-throughput， Genomics， Transcriptomics， Proteomics， Interactomics， Data integration， Omics databases， Omics tools 1. Introduction The last decade has seen an explosion in the amount of biological data generated by an ever-mcreasing number of techniques enabling the simultaneous detection of a large number of altera-tions in molecular components (1). The Omics technologies uti-lise these high-throughput (HT) screening techniques to generate the large amounts ofdata required to enable a system level under-standing of correlations and dependencies between molecular components. Omics techniques are required to be high throughput because they need to analyse very large numbers of genes， gene expression， or proteins either in a single procedure or a combina-tion of procedures. Computational analysis， i.e.， the discipline now known as bioinformatics， is a key requirement for the study of the vast amounts ofdata generated. Omics requires the use of techniques that can handle extremely complex biological samples in large quantities (e.g. high throughput) with high sensitivity and specificity. Next generation analytical tools require improved robustness， flexibility and cost efficiency. All of these aspects are being continuously improved， potentially enabling institutes such as the Wellcome Trust Sanger Sequencing Centre (see Note l) to generate thousands of millions of base pairs per day， rather than the current output of 100 million per day. However， all this data production makes sense only if one is equipped with the necessary analytical resources and tools to understand it. The evolution of the laboratory techniques has therefore to occur in parallel with a corresponding improvement in analytical methodology and tools to handle the data. The phrase Omics -a suffix signifying the measurement ofthe entire comple-ment of a given level of biological molecules and information -encompasses a variety of new technologies that can help explain both normal and abnormal cell pathways， networks， and processes via the simultaneous monitoring of thousands of molecular com-ponents. Bioinformaticians use computers and statistics to perform extensive Omics-related research by searching biological databases and comparing gene sequences and proteins on a vast scale to identify sequences or proteins that differ between diseased and healthy tissues， or more general between different phenotypes.
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