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About this book
About this book
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, 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.
Foreword.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.
Michael R. Barnes: Bioinformatics, GlaxoSmithKline Pharmaceuticals, UK