Description
Book SynopsisAssuming only a basic knowledge of bioinformatics, Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data illustrates the value of bioinformatics as a constantly evolving avenue into novel approaches to study genetics.
Trade Review" …an excellent resource…this book should ensure that any researcher’s skill base is maintained" (
Genetical Research, 2007)
"…this book contains some essential reading for almost any person working in the field of molecular genetics." (European Journal Of Human Genetics, 2007)
“Over 19 chapters, the authors cover an impressive terrain. The focus is mainly on human genetics and genomics, with research in other species also presented, particularly where it supports and advances our understanding of human genetics. Although a thoughtful discussion of the relevant literature and techniques is found in each chapter, the book is not overly technical and does not present advanced mathematical, statistical, or genetic concepts in great depth. Instead, focus is on practical applications, available tools, software, and databases, and the presentation supporting real world research examples. The end result is one of the best available and most accessible texts on bioinformatics and genetics in the postgenome age…this book in its current edition still serves as one of the best resources available, particularly in chapters on noncoding RNAs, pharmacogenetics, and drug discovery, microarrays/gene expression, regulatory polymorphisms, and the potential impacts of amino acid changes. 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, 2008)
Table of ContentsForeword.
Preface.
Contributors.
Glossary.
SECTION I AN INTRODUCTION TO BIOINFORMATICS FOR THE GENETICIST.
1 Bioinformatics challenges for the geneticist (Michael R. Barnes).
1.1 Introduction.
1.2 The role of bioinformatics in genetics research.
1.3 Genetics in the post-genome era.
1.4 Conclusions.
References.
2 Managing and manipulating genetic data (Karl W. Broman and Simon C. Heath).
2.1 Introduction.
2.2 Basic principles.
2.3 Data entry and storage.
2.4 Data manipulation.
2.5 Examples of code.
2.6 Resources.
2.7 Summary.
References.
SECTION II MASTERING GENES, GENOMES AND GENETIC VARIATION DATA.
3 The HapMap – A haplotype map of the human genome (Ellen M. Brown and Bryan J. Barratt).
3.1 Introduction.
3.2 Accessing the data.
3.3 Application of HapMap data in association studies.
3.4 Future Perspectives.
References.
4 Assembling a view of the human genome (Colin A. M. Semple).
4.1 Introduction.
4.2 Genomic sequence assembly.
4.3 Annotation from a distance: the generalities.
4.4 Annotation up close and personal: the specifics.
4.5 Annotation: the next generation.
References.
5 Finding, delineating and analysing genes (Christopher Southan and Michael R. Barnes).
5.1 Introduction.
5.2 Why learn to predict and analyse genes in the complete genome era?
5.3 The evidence cascade for gene products.
5.4 Dealing with the complexities of gene models.
5.5 Locating known genes in the human genome.
5.6 Genome portal inspection.
5.7 Analysing novel genes.
5.8 Conclusions and prospects.
References.
6 Comparative genomics (Martin S. Taylor and Richard R. Copley).
6.1 Introduction.
6.2 The Genomic landscape.
6.3 Concepts.
6.4 Practicalities.
6.5 Technology.
6.6 Applications.
6.7 Challenges and future directions.
6.8 Conclusion.
References.
SECTION III BIOINFORMATICS FOR GENETIC STUDY DESIGN AND ANALYSIS.
7 Identifying mutations in single gene disorders (David P. Kelsell, Diana Blaydon and Charles A. Mein).
7.1 Introduction.
7.2 Clinical Ascertainment.
7.3 Genome-wide mapping of monogenic diseases.
7.4 The nature of mutation in monogenic diseases.
7.5 Considering epigenetic effects in mendelian traits.
7.6 Summary.
References.
8 From Genome Scan Culprit Gene (Ian C. Gray).
8.1 Introduction.
8.2 Theoretical and practical considerations.
8.3 A stepwise approach to locus refinement and candidate gene identification.
8.4 Conclusion.
8.5 A list of the software tools and Web links mentioned in this chapter.
References.
9 Integrating Genetics, Genomics and Epigenomics to Identify.
Disease Genes (Michael R. Barnes).
9.1 Introduction.
9.2 Dealing with the (draft) human genome sequence.
9.3 Progressing loci of interest with genomic information.
9.4 In silico characterization of the IBD5 locus – a case study.
9.5 Drawing together biological rationale – hypothesis building.
9.6 Identification of potentially functional polymorphisms.
9.7 Conclusions.
References.
10 Tools for statistical genetics (Aruna Bansal, Charlotte Vignal and Ralph McGinnis).
10.1 Introduction.
10.2 Linkage analysis.
10.3 Association analysis.
10.4 Linkage disequilibrium.
10.5 Quantitative trait locus (QTL) mapping in experimental crosses.
10.6 Closing remarks.
References.
SECTION IV MOVING FROM ASSOCIATED GENES TO DISEASE ALLELES.
11 Predictive functional analysis of polymorphisms: An overview (Mary Plumpton and Michael R. Barnes).
11.1 Introduction.
11.2 Principles of predictive functional analysis of polymorphisms.
11.3 The anatomy of promoter regions and regulatory elements.
11.4 The anatomy of genes.
11.5 Pseudogenes and regulatory mRNA.
11.6 Analysis of novel regulatory elements and motifs in.
nucleotide sequences.
11.7 Functional analysis of non-synonymous coding polymorphisms.
11.8 Integrated tools for functional analysis of genetic variation.
11.9 A note of caution on the prioritization of in silico predictions for.
further laboratory investigation.
11.10 Conclusions.
References.
12 Functional in silico analysis of gene regulatory polymorphism (Chaolin Zhang, Xiaoyue Zhao, Michael Q. Zhang).
12.1 Introduction.
12.2 Predicting regulatory regions.
12.3 Modelling and predicting transcription factor-binding sites.
12.4 Predicting regulatory elements for splicing regulation.
12.5 Evaluating the functional importance of.
regulatory polymorphisms.
References.
13 Amino-acid properties and consequences of substitutions (Matthew J. Betts and Robert B. Russell).
13.1 Introduction.
13.2 Protein features relevant to amino-acid behaviour.
13.3 Amino-acid classifications.
13.4 Properties of the amino acids.
13.5 Amino-acid quick reference.
13.6 Studies of how mutations affect function.
13.7 A summary of the thought process.
References.
14 Non-coding RNA bioinformatics (James Brown, Steve Deharo, Barry Dancis, Michael R. Barnes and Philippe Sanseau).
14.1 Introduction.
14.2 The non-coding (nc) RNA universe.
14.3 Computational analysis of ncRNA.
14.4 ncRNA variation in disease.
14.5 Assessing the impact of variation in ncRNA.
14.6 Data resources to support small ncRNA analysis.
14.7 Conclusions.
References.
SECTION V ANALYSIS AT THE GENETIC AND GENOMIC DATA INTERFACE.
15 What are microarrays? (Catherine A. Ball and Gavin Sherlock).
15.1 Introduction.
15.2 Principles of the application of microarray technology.
15.3 Complementary approaches to microarray analysis.
15.4 Differences between data repository and research database.
15.5 Descriptions of freely available research database packages.
References.
16 Combining quantitative trait and gene-expression data (Elissa J. Chesler).
16.1 Introduction: the genetic regulation of endophenotypes.
16.2 Transcript abundance as a complex phenotype.
16.3 Scaling up genetic analysis and mapping models for microarrays.
16.4 Genetic correlation analysis.
16.5 Systems genetic analysis.
16.6 Using expression QTLs to identify candidate genes for the regulation of complex phenotypes.
16.7 Conclusions.
References.
17 Bioinformatics and cancer genetics (Joel Greshock).
17.1 Introduction.
17.2 Cancer genomes.
17.3 Approaches to studying cancer genetics.
17.4 General resources for cancer genetics.
17.5 Cancer genes and mutations.
17.6 Copy number alterations in cancer.
17.7 Loss of heterozygosity in cancer.
17.8 Gene-expression data in cancer.
17.9 Multiplatform gene target identification.
17.10 The epigenetics of cancer.
17.11 Tumour modelling.
17.12 Conclusions.
References.
18 Needle in a haystack? dealing with 500 SNP genome scans (Michael R. Barnes and Paul S. Derwent).
18.1 Introduction.
18.2 Genome scan analysis issues.
18.3 Ultra-high-density genome-scanning technologies.
18.4 Bioinformatics for genome scan analysis.
18.5 Conclusions.
References.
19 A bioinformatics perspective on genetics in drug discovery and development (Christopher D. Southan, Magnus Ulvsb¨ack and Michael R. Barnes).
19.1 Introduction.
19.2 Target genetics.
19.3 Pharmacogenetics (PGx).
19.4 Conclusions: toward ‘personalized medicine’.
References.
Appendix I.
Appendix II.
Index.
