Understanding Bioinformatics

Voorkant
Garland Science, 2008 - 772 pagina's

Suitable for advanced undergraduates and postgraduates, Understanding Bioinformatics provides a definitive guide to this vibrant and evolving discipline. The book takes a conceptual approach. It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms. Understanding Bioinformatics is an invaluable companion for students from their first encounter with the subject through to more advanced studies.

The book is divided into seven parts, with the opening part introducing the basics of nucleic acids, proteins and databases. Subsequent parts are divided into 'Applications' and 'Theory' Chapters, allowing readers to focus their attention effectively. In each section, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and 'getting started'. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics. In Part 2, Sequence Alignments, the Applications Chapter shows the reader how to get started on producing and analyzing sequence alignments, and using sequences for database searching, while the next two chapters look closely at the more advanced techniques and the mathematical algorithms involved. Part 3 covers evolutionary processes and shows how bioinformatics can be used to help build phylogenetic trees. Part 4 looks at the characteristics of whole genomes. In Parts 5 and 6 the focus turns to secondary and tertiary structure - predicting structural conformation and analysing structure-function relationships. The last part surveys methods of analyzing data from a set of genes or proteins of an organism and is rounded off with an overview of systems biology.

The writing style of Understanding Bioinformatics is notable for its clarity, while the extensive, full-color artwork has been designed to present the key concepts with simplicity and consistency. Each chapter uses mind-maps and flow diagrams to give an overview of the conceptual links within each topic.

 

Inhoudsopgave

Background Basics
1
Evolutionary Processes
7
Translation involves transfer RNAs
13
Summary
23
Automated methods can be used to check for data
63
Sequence Alignments
69
73
95
81
102
of the COILS algorithm
453
THEORY CHAPTER
461
There are several different measures of
469
The simplest prediction methods are based on
476
Chapter
478
Predictions can be significantly improved
484
51
513
Analyzing StructureFunction Relationships
519

Summary
159
126
179
Optimal global alignments are produced using
187
Time can be saved with a loss of rigor by
193
17
196
18
204
Summary
218
Obtaining Secondary Structure from Sequence
221
Tree topology can be described in several ways
230
Most related sequences have many positions
236
Major changes affecting large regions of
247
249
291
All phylogenetic analyses must start with
297
49
301
APPLICATIONS CHAPTER
354
Tertiary Structures
366
Splice sites can be predicted by sequence patterns
393
Modeling Protein Structure
411
Secondary Structures
435
Neural nets in transmembrane prediction
445
491
533
better models
539
Cells and Organisms
542
Nonidentical amino acid side chains are modeled
547
Automated methods available on the Web
554
Chapter
584
Assessment of structure prediction
585
Fragment docking identifies potential substrates
591
Clustering Methods and Statistics
597
Serial analysis of gene expression SAGE is also
604
52
619
Facilitating the integration of data from different
633
614
641
independent methods
650
APPENDICES Background Theory
695
Support vector machines are another form
699
Function Optimization
709
Redundancy in the system can provide robustness
721
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