生物信息学中的计算机技术(影印版)
生物信息学中的计算机技术(影印版)
Cynthia Gibas, Per Jambeck
出版时间:2002年07月
页数:448
生物信息学 -- 将计算和分析方法应用于生物学问题 -- 是一个迅速发展的科学领域。基因组测序工作正在产生来自许多不同有机体的大量生物学数据,这些数据被更多地存储于公共数据库。没有基于计算机的辅助工具,即便是最热心的研究者在此领域工作也是不可能的。生物信息学就是要构建此类工具。
《生物信息学中的计算机技术》行文简明,引人入胜,是对生物信息学中一些最重要主题的创见性导论。本书介绍并解释用于生物信息学研究的许多流行软件工具,还包括背景理论,以帮助理解怎样充分运用这些工具和为什么这些工具是重要的。
《生物信息学中的计算机技术》涵盖:
*生物信息学工作站构建
*面向生物学工作者的Unix
*适于在生物学序列、基因组和分子结构数据库中搜索信息的计算技术
*基因鉴别工具和基因家族特征图谱检测工具
*系统发育关系、分子结构和生化性质的模拟工具
*数据处理和数据分析过程自动化
*数据库组建
*数据提取和数据可视化工具
无论是初学生物学计算方法的学生,还是正要用计算机处理数据的有经验的研究者,本书将助你拓展对生物学数据的规划探讨和对所需分析工具的坚实理解。
  1. Preface
  2. I Introduction
  3. 1 Biology in the Computer Age
  4. How Is Computing Changing Biology?
  5. Isn't Bioinformatics Just About Building Databases?
  6. What Does Informatics Mean to Biologists?
  7. What Challenges Does Biology Offer Computer Scientists?
  8. What Skills Should a Bioinformatician Have?
  9. Why Should Biologists Use Computers?
  10. How Can I Configure a PC to Do Bioinformatics Research?
  11. What Information and Software Are Available?
  12. Can I Learn a Programming Language Without Classes?
  13. How Can I Use Web Information?
  14. How Do I Understand Sequence Alignment Data?
  15. How Do I Write a Program to Align Two Biological Sequences?
  16. How Do I Predict Protein Structure from Sequence?
  17. What Questions Can Bioinformatics Answer?
  18. 2 Computational Approaches to Biological Questions
  19. Molecular Biology's Central Dogma
  20. What Biologists Model
  21. Why Biologists Model
  22. Computational Methods Covered in This Book
  23. A Computational Biology Experiment
  24. II The Bioinformatics Workstation
  25. 3 Setting Up Your Workstation
  26. Working on a Unix system
  27. Setting Up a Linux Workstation
  28. How to Get Software Working
  29. What Software Is Needed?
  30. 4 Files and Directories in Unix
  31. Filesystem Basics
  32. Commands for Working with Directories and Files
  33. Working in a Multiuser Environment
  34. 5 Working on a Unix System
  35. The Unix Shell
  36. Issuing Commands on a Unix System
  37. Viewing and Editing Files
  38. Transformations and Filters
  39. File Statistics and Comparisons
  40. The Language of Regular Expressions
  41. Unix Shell Scripts
  42. Communicating with Other Computers
  43. Playing Nicely with Others in a Shared Environment
  44. III Tools for Bioinformatics
  45. 6 Biological Research on the Web
  46. Using Search Engines
  47. Finding Scientific Articles
  48. The Public Biological Databases
  49. Searching Biological Databases
  50. Depositing Data into the Public Databases
  51. Finding Software
  52. Judging the Quality of Information
  53. 7 Sequence Analysis, Pairwise Alignment, and Database Searching
  54. Chemical Composition of Biomolecules
  55. Composition of DNA and RNA
  56. Watson and Crick Solve the Structure of DNA
  57. Development of DNA Sequencing Methods
  58. Genefinders and Feature Detection in DNA
  59. DNA Translation
  60. Pairwise Sequence Comparison
  61. Sequence Queries Against Biological Databases
  62. Multifunctional Tools for Sequence Analysis
  63. 8 Multiple Sequence Alignments, Trees, and Profiles
  64. The Morphological to the Molecular
  65. Multiple Sequence Alignment
  66. Phylogenetic Analysis
  67. Profiles and Motifs
  68. 9 Visualizing Protein Structures and Computing Structural Properties
  69. A Word About Protein Structure Data
  70. The Chemistry of Proteins
  71. Web-Based Protein Structure Tools
  72. Structure Visualization
  73. Structure Classification
  74. Structural Alignment
  75. Structure Analysis
  76. Solvent Accessibility and Interactions
  77. Computing Physicochemical Properties
  78. Structure Optimization
  79. Protein Resource Databases
  80. Putting It All Together
  81. 10 Predicting Protein Structure and Function from Sequence
  82. Determining the Structures of Proteins
  83. Predicting the Structures of Proteins
  84. From 3D to 1D
  85. Feature Detection in Protein Sequences
  86. Secondary Structure Prediction
  87. Predicting 3D Structure
  88. Putting It All Together: A Protein Modeling Project
  89. Summary
  90. 11 Tools for Genomics and Proteomics
  91. From Sequencing Genes to Sequencing Genomes
  92. Sequence Assembly
  93. Accessing Genome Information on the Web
  94. Annotating and Analyzing Whole Genome Sequences
  95. Functional Genomics: New Data Analysis Challenges
  96. Proteomics
  97. Biochemical Pathway Databases
  98. Modeling Kinetics and Physiology
  99. Summary
  100. IV Databases and Visualization
  101. 12 Automating Data Analysis with Perl
  102. Why Perl?
  103. Perl Basics
  104. Pattern Matching and Regular Expressions
  105. Parsing BLAST Output Using Perl
  106. Applying Perl to Bioinformatics
  107. 13 Building Biological Databases
  108. Types of Databases
  109. Database Software
  110. Introduction to SQL
  111. Installing the MySQL DBMS
  112. Database Design
  113. Developing Web-Based Software That Interacts with Databases
  114. 14 Visualization and Data Mining
  115. Preparing Your Data
  116. Viewing Graphics
  117. Sequence Data Visualization
  118. Networks and Pathway Visualization
  119. Working with Numerical Data
  120. Visualization: Summary
  121. Data Mining and Biological Information
  122. Bibliography
  123. Index
书名:生物信息学中的计算机技术(影印版)
国内出版社:科学出版社
出版时间:2002年07月
页数:448
书号:7-03-010421-8
原版书出版商:O'Reilly Media
Cynthia Gibas
 
Cynthia Gibas是位于弗吉尼亚州Blackburg的弗吉尼亚理工学院的生物学副教授。她在计算生物学受到青睐之前就已经是计算生物学家了。目前她正在推动全新的家庭Linux集群。她的研究兴趣包括基因组结构和进化、蛋白质表面和界面特性,以及蛋白质结构预测。她为生物学家们讲授生物信息学方法的课程。她正期盼着下一个假期的来临,可能在2006年的某个时候。
Cynthia Gibas is an assistant professor of biology at Virginia Tech, in Blacksburg,Virginia. She's been a computational biologist since before computational biology was cool, and is currently learning to drive her spankin' new home-built Linux cluster. Her research interests include the structure and evolution of genomes, the properties of protein surfaces and interfaces, and prediction of protein structure. She teaches introductory courses in bioinformatics methods for biologists and islooking forward to her next real vacation, sometime in 2006.
 
 
Per Jambeck
 
Per Jambeck是加州大学圣迭戈分校生物工程系的博士研究生。他从1994年开始从事计算生物学的研究,兴趣主要集中在如何将机器学习应用于多维生物学数据的理解。在谈及空闲时间时,Per总是充满渴望地微笑着,他时常组织主持社区演出和学生广播站。
Per Jambeck is a Ph.D. student in the bioengineering department at the University of California, San Diego. He has worked on computational biology problems since 1994, concentrating on machine learning applications in understanding multidi-mensional biological data. Per smiles wistfully at the mention of free time, but he manages to host shows at community and student-run radio stations anyway.
 
 
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The animal on the cover of Developing Bioinformatics Computer Skills is Caenorhabditis elegans, a small nematode worm. Unlike many of its nastier para-sitic cousins, C. elegans lives in the soil where it feeds on microbes and bacteria. It grows to about 1 mm in length.
In spite of its status as a "primitive" organism, C. elegans shares with H. sapiens many essential biological characteristics. C. elegans begins life as a single cell that divides and grows to form a multicellular adult. It has a nervous system and a brain (more properly known as the circumpharyngeal ring) and a muscular system that supports locomotion. It exhibits behavior and is capable of rudimentary learning. Like humans, it comes in two sexes, but in C. elegans those sexes consist of a male and a self-fertilizing hermaphrodite. C. elegans is easily grown in large numbers in the laboratory, has a short (2-3 week) lifespan, and can be manipu-lated in sophisticated experiments. These characteristics make it an ideal organism for scientific research.
The C. elegans hermaphrodite has 959 cells, 300 of which are neurons, and 81 of which are muscle cells. The entire cell lineage has been traced through develop-ment. The adult has a number of sensory organs in the head region which respond to taste, smell, touch, and temperature. Although it has no eyes, it does react slightly to light. C. elegans has approximately 17,800 distinct genes, and its genome has been completely sequenced. Along with the fruit fly, the mouse, and the weed Arabidopsis, C. elegans has become one of the most studied model organisms in biology since Sydney Brenner first focused his attention on it decades ago.
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