Ebook: Bioinformatics for Systems Biology

Хорошая, объёмистая книга о биоинформатике на английском языке. В книге неплохо описаны основные концепции молекулярной биологии, популяционной генетики, статистики и конечно же приведено масса методов и концепций бионформатики.
Contents
1 Structure and Function of the Nucleus and Cell Organelles
2 Transcription and the Control of Gene Expression
3 RNA Processing and Translation
4 DNA Replication, Recombination, and Repair
5 Cell Signaling
6 Epigenetics of Spermiogenesis – Combining In Silico and Proteomic Approaches
in the Mouse Model
7 Genomic Tools for Analyzing Transcriptional Regulatory Networks
8 Probability and Hypothesis Testing
9 Stochastic Models for Biological Patterns
10 Population Genetics
11 Statistical Tools for Gene Expression Analysis and Systems Biology and Related Web Resources
12 What Goes in is What Comes Out: How to Design and Implement a Successful
Microarray Experiment
13 Tools and Approaches for an End-to-End Expression Array Analysis
14 Analysis of Alternative Splicing with Microarrays
15 An Introduction to Multiple Sequence Alignment — and the T-Coffee Shop. Beyond Just Aligning Sequences: How Good can you Make your Alignment, and so What?
16 A Spectrum of Phylogenetic-Based Approaches for Predicting Protein Functional Sites
17 The Role of Transcription Factor Binding Sites in Promoters and Their In Silico Detection
18 In Silico Discovery of DNA Regulatory Sites and Modules
19 Mining the Research Literature in Systems Biology
20 GoPubMed: Exploring PubMed with Ontological Background Knowledge
21 BiblioSphere — Hypothesis Generation in Regulatory Network Analysis
22 Biological Knowledge Extraction – A Case Study of iHOP and Other Language
Processing Systems
23 Using KEGG in the Transition from Genomics to Chemical Genomics
24 Ensembl – Open-Source Software for Large-Scale Genome Analysis
25 Management of Spatially Organized Biological Data using EMAGE
26 Equality of the Sexes? Parent-of-Origin Effects on Transcription and de novo Mutations
27 Methods for Structural Inference and Functional Module Identification in Intracellular Networks
28 Methods for Dynamical Inference in Intracellular Networks
29 ASIAN: Network Inference Web Server
30 Bioinformatics for Metabolomics
31 Virtual Reality Meets Functional Genomics
32 Systems Biology of Personalized Medicine

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The biological sciences are now in the midst of a true life sciences revolution akin to what physics experienced just after the turn of the last century. We are now in a phase of unparalleled growth that is reflected by the amount of data generated from each experiment. At the time of this writing, the rate of data acquisition was approaching 2 terabytes over the course of 5 days with first pass analysis proceeding over the following 2-3 week period. This fundamental shift has provided unprecedented opportunities that for the first time afford us the ability, i.e., means, breadth, and depth of data, to truly address human biology at the systems level. This wealth of information from seemingly disparate datasets and its integration is being realized through bioinformatics. It is with this philosophy that the text Bioinformatics for Systems Biology was born. This revolution has spawned true personalized medicine that encompasses diagnostics and treatment through to cure.

For the physical and computer scientist, this text provides an introduction to the basic biological principles governing a cell. This quickly moves from the fundamentals to exploring the underlying genetic processes. While providing a rudimentary and necessary overview for the life scientist, the physical and computer scientist will be apprised of various nuances within the field reflecting the reality of "wet-bench" science. For those in the life sciences, it I rapidly becoming appreciated that we now progressing from examining our favorite "pet" gene to the system. Statistics is now an essential component to understand the vast datasets and this is emphasized throughout the text.

The majority of the text is devoted to the common ground that these groups share. It provides rich examples of tools, databases, and strategies to mine the databases to reveal novel insights. A host of examples of parsing the data into a series of overlays that use various presentation systems are reviewed. The goal is to provide a representation most comfortable to the user to enable the user to thoroughly explore the data. The text concludes with examples of how the systems information is used to inform personalized medicine in a true "bench to bedside" manner.

Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward in the mostly uncharted and emerging field of Systems Biology.