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组学数据生物信息学：研究方法与实验方案（导读版） 
定 价：178 元 丛书名：实验室解决方案
9 7 3 8 5 7 9 0 3 3 0 0 8 读者对象:组学应用研究人员 
《组学数据生物信息学：研究方法与实验方案（导读版）》特邀本领域专业研究人员撰写，以便向读者提供一本实用指南。《组学数据生物信息学：研究方法与实验方案（导读版）》向读者展示了一个全新的研究领域——组学数据生物信息学。这一新领域交汇并整合了分子生物学、应用信息学和统计学等不同学科。
《组学数据生物信息学：研究方法与实验方案（导读版）》内容十分详尽，全书分为三大部分。首先介绍组学数据的基本分析策略、标准化、管理指南，以及基础统计学等。接着，按基因组、转录组、蛋白质组、代谢组等不同专题介绍各种数据的特定分析策略。最后，以疾病相关生物标记和靶标鉴定等为例，说明组学生物信息学的具体应用。《组学数据生物信息学：研究方法与实验方案（导读版）》秉承Springer《分子生物学方法》系列丛书的一贯风格，阐述明晰、便于使用，各章包括专题简介、必备材料、易于操作的实验方案、疑难问题的主意事项，以及如何避免常见错误。 《组学数据生物信息学：研究方法与实验方案（导读版）》既具权威性，又力求通俗易懂，叫作为不同专业北京研究人员的理想指南，也为读者描绘了本研究领域引人入胜的图景。 更多科学出版社服务，请扫码获取。 
《组学数据生物信息学(研究方法与实验方案导读版)》从多个侧面对组学数据生物信息学做了详尽的介绍。本书共分三篇。第一篇介绍核心分析策略、标准分析规范、数据管理指南，以及用于分析组学数据的基本统计方法。第二篇介绍用于基因组、转录组、蛋白质组、代谢组等各种不同组学数据的生物信息学分析方法，包括基本概念和实验背景，以及原始数据预处理和深入分析的基本方法。第三篇则介绍如何利用生物信息学进行组学数据分析的实例，包括人类疾病相关生物标记鉴定和靶标识别等具体例子。本书由迈尔著。
目录
前言 v 撰稿人 ix 第一篇 组学生物信息学基础 第一章 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
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 of dealing with high-throughput data. This overview touches several fundamental aspects of Omics 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 .elds, we concentrate in three main Omics .elds 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-increasing 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 of data 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 of data generated. Omics requires the use of Bernd Mayer (ed.), Bioinformatics for Omics Data: Methods and Protocols, Methods in Molecular Biology, vol. 719, DOI 10.1007/978-1-61779-027-0_1, . Springer Science+Business Media, LLC 2011 3 Schneider and Orchard techniques that can handle extremely complex biological samples in large quantities (e.g. high throughput) with high sensitivity and speci.city. Next generation analytical tools require improved robustness, .exibility and cost ef.ciency. All of these aspects are being continuously improved, potentially enabling institutes such as the Wellcome Trust Sanger Sequencing Centre (see Note 1) to generate thousands of millions of base pairs per day, rather than the current output of 100 million per day (http://www. yourgenome.org/sc/nt). 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 suf.x signifying the measurement of the 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. “Omics” spans an increasingly wide range of .elds, which now range from genomics (the quantitative study of protein coding genes, regulatory elements and noncoding sequences), transcrip-tomics (RNA and gene expression), proteomics (e.g. focusing on protein abundance), and metabolomics (metabolites and meta-bolic networks) to advances in the era of post-genomic biology and medicine: pharmacogenomics (the quantitative study of how genetics affects a host response to drugs), physiomics (physiologi-cal dynamics and functions of whole organisms) and in other .elds: nutrigenomics (a rapidly growing discipline that focuses on iden-tifying the genetic factors that in.uence the body’s response to diet and studies how the bioactive constituents of food affect gene expression), phylogenomics (analysis involving genome data and evolutionary reconstructions, especially phylogenetics) and inter-actomics (molecular interaction networks). Though in the remain-der of this chapter we concentrate on an isolated few examples of Omics technologies, much of what is said, for example about data standardisation, data sharing, storage and analysis requirements are true for all of these different technological .elds. There are already large amounts of data generated by these technologies and this trend is increasing, for example second and third generation sequencing technologies are leading to an exponential increase in the amount of sequencing data available. From a computational point of view, in order to address the 2. Materials 2.1. Genomics High-Throughput Technologies 2.2. Transcriptomics High-Throughput Technologies Omics Technologies, Data and Bioinformatics Principles complexity of these data, understand molecular regulation and gain the most from such comprehensive set of information, knowledge discovery ? the process of automatically searching large volumes of data for patterns ? is a crucial step. This process of bioinformatics analysis includes: (1) data processing and molecule (e.g. protein) identi.cation, (2) statistical data analysis, (3) pathway analysis, and (4) data modelling in a system wide context. In this chapter we will present some of these analytical methods and discuss ways in which data can be made accessible to both the specialised bioinformatician, but in particular to the research scientist. There are a variety of de.nitions of the term HT; however we can loosely apply this term to cases where automation is used to increase the throughput of an experimental procedure. HT tech-nologies exploit robotics, optics, chemistry, biology and image analysis research. The explosion in data production in the public domain is a consequence of falling equipment prices, the opening of major national screening centres and new HT core facilities at universities and other academic institutes. The role of bioinfor-matics in HT technologies is of essential importance. High-Throughput Sequencing (HTS) technologies are used not only for traditional applications in genomics and metagenomics (see Note 2), but also for novel applications in the .elds of tran-scriptomics, metatranscriptomics (see Note 3), epigenomics (see Note 4), and studies of genome variation (see Note 5). Next gen-eration sequencing platforms allow the determination of the sequence data from ampli.ed single DNA fragments and have been developed speci.cally to lend themselves to robotics and par-allelisation. Current methods can directly sequence only relatively short (300?1,000 nucleotides long) DNA fragments in a single reaction. Short-read sequencing technologies dramatically reduce the sequencing cost. There were initial fears that the increase in quantity might result in a decrease in quality, and improvements in accuracy and read length are being looked for. However, despite this, these advances have signi.cantly reduced the cost of several sequencing applications, such as resequencing individual genomes (2) readout assays (e.g. ChIP-seq (3) and RNAseq (4)). The transcriptome is the set of all messenger RNA (mRNA) molecules, or “transcripts”, produced in one or a population of cells. Several methods have been developed in order to gain expression information at high throughput level.
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