Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data

Front Cover
Michael R. Barnes
John Wiley & Sons, Apr 16, 2007 - Computers - 554 pages
1 Review
Praise from the reviews:

"Without reservation, I endorse this text as the best resource I've encountered that neatly introduces and summarizes many points I've learned through years of experience.  The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity." CIRCGENETICS

"This book may really help to get geneticists and bioinformaticians on 'speaking-terms'... contains some essential reading for almost any person working in the field of molecular genetics." EUROPEAN JOURNAL OF HUMAN GENETICS 

"... an excellent resource... this book should ensure that any researcher's skill base is maintained." GENETICAL RESEARCH

“… one of the best available and most accessible texts on bioinformatics and genetics in the postgenome age… The writing is clear, with succinct subsections within each chapter….Without reservation, I endorse this text as the best resource I’ve encountered that neatly introduces and summarizes many points I’ve learned through years of experience. The gems of truth found in this book will serve well those who wish to apply bioinformatics in their daily work, as well as help them advise others in this capacity.”  CIRCULATION: CARDIOVASCULAR GENETICS

A fully revised version of the successful First Edition, this one-stop reference book enables all geneticists to improve the efficiency of their research.

The study of human genetics is moving into a challenging new era. New technologies and data resources such as the HapMap are enabling genome-wide studies, which could potentially identify most common genetic determinants of human health, disease and drug response. With these tremendous new data resources at hand, more than ever care is required in their use. Faced with the sheer volume of genetics and genomic data, bioinformatics is essential to avoid drowning true signal in noise. Considering these challenges, Bioinformatics for Geneticists, Second Edition works at multiple levels: firstly, for the occasional user who simply wants to extract or analyse specific data; secondly, at the level of the advanced user providing explanations of how and why a tool works and how it can be used to greatest effect. Finally experts from fields allied to genetics give insight into the best genomics tools and data to enhance a genetic experiment.

Hallmark Features of the Second Edition:

  • Illustrates the value of bioinformatics as a constantly evolving avenue into novel approaches to study genetics
  • The only book specifically addressing the bioinformatics needs of geneticists
  • More than 50% of chapters are completely new contributions
  • Dramatically revised content in core areas of gene and genomic characterisation, pathway analysis, SNP functional analysis and statistical genetics
  • Focused on freely available tools and web-based approaches to bioinformatics analysis, suitable for novices and experienced researchers alike

Bioinformatics for Geneticists, Second Edition describes the key bioinformatics and genetic analysis processes that are needed to identify human genetic determinants. The book is based upon the combined practical experience of domain experts from academic and industrial research environments and is of interest to a broad audience, including students, researchers and clinicians working in the human genetics domain.

 

What people are saying - Write a review

LibraryThing Review

User Review  - brett_in_nyc - LibraryThing

A real compendium to understand the state of the science in how genomic data and information may someday translate into greater efficiency in producing drugs and therapies. This is a real reference book for my future work. Read full review

Selected pages

Contents

Bioinformatics challenges for the geneticist
3
12 The role of bioinformatics in genetics research
4
13 Genetics in the postgenome era
5
14 Conclusions
12
References
15
Managing and manipulating genetic data
17
22 Basic principles
18
23 Data entry and storage
20
MOVING FROM ASSOCIATED GENES TO DISEASE ALLELES
247
Predictive functional analysis of polymorphisms An overview
249
112 Principles of predictive functional analysis of polymorphisms
252
113 The anatomy of promoter regions and regulatory elements
256
114 The anatomy of genes
258
115 Pseudogenes and regulatory mRNA
266
117 Functional analysis of nonsynonymous coding polymorphisms
268
118 Integrated tools for functional analysis of genetic variation
274

24 Data manipulation
21
25 Examples of code
22
26 Resources
30
27 Summary
31
MASTERING GENES GENOMES AND GENETIC VARIATION DATA
33
The HapMap A haplotype map of the human genome
35
32 Accessing the data
38
33 Application of HapMap data in association studies
42
34 Future perspectives
54
Assembling a view of the human genome
59
42 Genomic sequence assembly
60
the generalities
64
the specifics
70
the next generation
78
References
80
Finding delineating and analysing genes
85
52 Why learn to predict and analyse genes in the complete genome era?
86
53 The evidence cascade for gene products
88
54 Dealing with the complexities of gene models
95
55 Locating known genes in the human genome
97
56 Genome portal inspection
100
57 Analysing novel genes
101
58 Conclusions and prospects
102
References
103
Comparative genomics
105
62 The genomic landscape
106
63 Concepts
109
64 Practicalities
113
65 Technology
118
66 Applications
132
67 Challenges and future directions
137
68 Conclusion
138
References
139
BIOINFORMATICS FOR GENETIC STUDY DESIGN AND ANALYSIS
145
Identifying mutations in single gene disorders
147
73 Genomewide mapping of monogenic diseases
148
74 The nature of mutation in monogenic diseases
152
75 Considering epigenetic effects in mendelian traits
160
76 Summary
162
From Genome Scan to Culprit Gene
165
82 Theoretical and practical considerations
166
83 A stepwise approach to locus refinement and candidate gene identification
176
84 Conclusion
180
85 A list of the software tools and Web links mentioned in this chapter
181
References
182
Integrating Genetics Genomics and Epigenomics to Identify Disease Genes
185
92 Dealing with the draft human genome sequence
186
93 Progressing loci of interest with genomic information
187
94 In silico characterization of the IBD5 locus a case study
191
95 Drawing together biological rationale hypothesis building
209
96 Identification of potentially functional polymorphisms
211
97 Conclusions
212
References
213
Tools for statistical genetics
217
103 Association analysis
223
104 Linkage disequilibrium
229
105 Quantitative trait locus QTL mapping in experimental crosses
235
106 Closing remarks
239
References
241
119 A note of caution on the prioritization of in silico predictions for further laboratory investigation
276
Functional in silico analysis of gene regulatory polymorphism
281
122 Predicting regulatory regions
284
123 Modelling and predicting transcription factorbinding sites
288
124 Predicting regulatory elements for splicing regulation
295
125 Evaluating the functional importance of regulatory polymorphisms
300
References
302
Aminoacid properties and consequences of substitutions
311
132 Protein features relevant to aminoacid behaviour
312
133 Aminoacid classifications
316
134 Properties of the amino acids
318
135 Aminoacid quick reference
321
136 Studies of how mutations affect function
334
137 A summary of the thought process
339
References
340
Noncoding RNA Bioinformatics
343
142 The noncoding nc RNA universe
344
143 Computational analysis of ncRNA
349
144 ncRNA variation in disease
356
145 Assessing the impact of variation in ncRNA
362
146 Data resources to support small ncRNA analysis
363
References
364
ANALYSIS AT THE GENETIC AND GENOMIC DATA INTERFACE
369
What are microarrays?
371
152 Principles of the application of microarray technology
373
153 Complementary approaches to microarray analysis
377
References
385
Combining quantitative trait and geneexpression data
389
162 Transcript abundance as a complex phenotype
390
163 Scaling up genetic analysis and mapping models for microarrays
394
164 Genetic correlation analysis
397
165 Systems genetic analysis
400
166 Using expression QTLs to identify candidate genes for the regulation of complex phenotypes
403
167 Conclusions
408
Bioinformatics and cancer genetics
413
172 Cancer genomes
414
173 Approaches to studying cancer genetics
415
174 General resources for cancer genetics
418
175 Cancer genes and mutations
420
176 Copy number alterations in cancer
425
177 Loss of heterozygosity in cancer
431
178 Geneexpression data in cancer
432
179 Multiplatform gene target identification
435
1710 The epigenetics of cancer
438
1712 Conclusions
439
Needle in a haystack? Dealing with 500 000 SNP genome scans
447
182 Genome scan analysis issues
449
183 Ultrahighdensity genomescanning technologies
459
184 Bioinformatics for genome scan analysis
469
185 Conclusions
489
References
490
A bioinformatics perspective on genetics in drug discovery and development
495
192 Target genetics
498
193 Pharmacogenetics PGx
508
toward personalized medicine
525
Appendix I
529
Appendix II
533
Index
537

Other editions - View all

Common terms and phrases

About the author (2007)

Michael R. Barnes: Bioinformatics, GlaxoSmithKline Pharmaceuticals, UK

Bibliographic information