Bioinformatics for Geneticists

Michael R. Barnes: Bioinformatics, GlaxoSmithKline Pharmaceuticals, UK

• Foreword xiPreface xvContributors xviiGlossary xixSection I An Introduction to Bioinformatics for The Geneticist 11 Bioinformatics challenges for the geneticist 3Michael R. Barnes1.1 Introduction 31.2 The role of bioinformatics in genetics research 41.3 Genetics in the post-genome era 51.4 Conclusions 12References 152 Managing and manipulating genetic data 17Karl W. Broman and Simon C. Heath2.1 Introduction 172.2 Basic principles 182.3 Data entry and storage 202.4 Data manipulation 212.5 Examples of code 222.6 Resources 302.7 Summary 31References 31Section II Mastering Genes, Genomes and Genetic Variation Data 333 The HapMap – A haplotype map of the human genome 35Ellen M. Brown and Bryan J. Barratt3.1 Introduction 353.2 Accessing the data 383.3 Application of HapMap data in association studies 423.4 Future perspectives 54References 544 Assembling a view of the human genome 59Colin A. M. Semple4.1 Introduction 594.2 Genomic sequence assembly 604.3 Annotation from a distance: the generalities 644.4 Annotation up close and personal: the specifics 704.5 Annotation: the next generation 78References 805 Finding, delineating and analysing genes 85Christopher Southan and Michael R. Barnes5.1 Introduction 855.2 Why learn to predict and analyse genes in the complete genome era? 865.3 The evidence cascade for gene products 885.4 Dealing with the complexities of gene models 955.5 Locating known genes in the human genome 975.6 Genome portal inspection 1005.7 Analysing novel genes 1015.8 Conclusions and prospects 102References 1036 Comparative genomics 105Martin S. Taylor and Richard R. Copley6.1 Introduction 1056.2 The genomic landscape 1066.3 Concepts 1096.4 Practicalities 1136.5 Technology 1186.6 Applications 1326.7 Challenges and future directions 1376.8 Conclusion 138References 139Section III Bioinformatics for Genetic Study Design and Analysis 1457 Identifying mutations in single gene disorders 147David P. Kelsell, Diana Blaydon and Charles A. Mein7.1 Introduction 1477.2 Clinical ascertainment 1477.3 Genome-wide mapping of monogenic diseases 1487.4 The nature of mutation in monogenic diseases 1527.5 Considering epigenetic effects in mendelian traits 1607.6 Summary 162References 1628 From Genome Scan to Culprit Gene 165Ian C. Gray8.1 Introduction 1658.2 Theoretical and practical considerations 1668.3 A stepwise approach to locus refinement and candidate gene identification 1768.4 Conclusion 1808.5 A list of the software tools and Web links mentioned in this chapter 181References 1829 Integrating Genetics, Genomics and Epigenomics to Identify Disease Genes 185Michael R. Barnes9.1 Introduction 1859.2 Dealing with the (draft) human genome sequence 1869.3 Progressing loci of interest with genomic information 1879.4 In silico characterization of the IBD5 locus – a case study 1919.5 Drawing together biological rationale – hypothesis building 2099.6 Identification of potentially functional polymorphisms 2119.7 Conclusions 212References 21310 Tools for statistical genetics 217Aruna Bansal, Charlotte Vignal and Ralph McGinnis10.1 Introduction 21710.2 Linkage analysis 21710.3 Association analysis 22310.4 Linkage disequilibrium 22910.5 Quantitative trait locus (QTL) mapping in experimental crosses 23510.6 Closing remarks 239References 241Section IV Moving From Associated Genes to Disease Alleles 24711 Predictive functional analysis of polymorphisms: An overview 249Mary Plumpton and Michael R. Barnes11.1 Introduction 24911.2 Principles of predictive functional analysis of polymorphisms 25211.3 The anatomy of promoter regions and regulatory elements 25611.4 The anatomy of genes 25811.5 Pseudogenes and regulatory mRNA 26611.6 Analysis of novel regulatory elements and motifs in nucleotide sequences 26611.7 Functional analysis of non-synonymous coding polymorphisms 26811.8 Integrated tools for functional analysis of genetic variation 27311.9 A note of caution on the prioritization of in silico predictions for further laboratory investigation 27511.10 Conclusions 275References 27612 Functional in silico analysis of gene regulatory polymorphism 281Chaolin Zhang, Xiaoyue Zhao, Michael Q. Zhang12.1 Introduction 28112.2 Predicting regulatory regions 28212.3 Modelling and predicting transcription factor-binding sites 28812.4 Predicting regulatory elements for splicing regulation 29512.5 Evaluating the functional importance of regulatory polymorphisms 300References 30213 Amino-acid properties and consequences of substitutions 311Matthew J. Betts and Robert B. Russell13.1 Introduction 31113.2 Protein features relevant to amino-acid behaviour 31213.3 Amino-acid classifications 31613.4 Properties of the amino acids 31813.5 Amino-acid quick reference 32113.6 Studies of how mutations affect function 33413.7 A summary of the thought process 339References 34014 Non-coding RNA bioinformatics 343James R. Brown, Steve Deharo, Barry Dancis, Michael R. Barnes and Philippe Sanseau14.1 Introduction 34314.2 The non-coding (nc) RNA universe 34414.3 Computational analysis of ncRNA 34914.4 ncRNA variation in disease 35614.5 Assessing the impact of variation in ncRNA 36214.6 Data resources to support small ncRNA analysis 36314.7 Conclusions 363References 364Section V Analysis at the Genetic and Genomic Data Interface 36915 What are microarrays? 371Catherine A. Ball and Gavin Sherlock15.1 Introduction 37115.2 Principles of the application of microarray technology 37315.3 Complementary approaches to microarray analysis 37715.4 Differences between data repository and research database 37715.5 Descriptions of freely available research database packages 377References 38516 Combining quantitative trait and gene-expression data 389Elissa J. Chesler16.1 Introduction: the genetic regulation of endophenotypes 38916.2 Transcript abundance as a complex phenotype 39016.3 Scaling up genetic analysis and mapping models for microarrays 39416.4 Genetic correlation analysis 39716.5 Systems genetic analysis 40016.6 Using expression QTLs to identify candidate genes for the regulation of complex phenotypes 40316.7 Conclusions 408References 40817 Bioinformatics and cancer genetics 413Joel Greshock17.1 Introduction 41317.2 Cancer genomes 41417.3 Approaches to studying cancer genetics 41517.4 General resources for cancer genetics 41817.5 Cancer genes and mutations 42017.6 Copy number alterations in cancer 42517.7 Loss of heterozygosity in cancer 43117.8 Gene-expression data in cancer 43217.9 Multiplatform gene target identification 43517.10 The epigenetics of cancer 43817.11 Tumour modelling 43817.12 Conclusions 439References 43918 Needle in a haystack? Dealing with 500 000SNP genome scans 447Michael R. Barnes and Paul S. Derwent18.1 Introduction 44718.2 Genome scan analysis issues 44918.3 Ultra-high-density genome-scanning technologies 45918.4 Bioinformatics for genome scan analysis 46918.5 Conclusions 489References 49019 A bioinformatics perspective on genetics in drug discovery and development 495Christopher Southan, Magnus Ulvsbäck and Michael R. Barnes19.1 Introduction 49519.2 Target genetics 49819.3 Pharmacogenetics (PGx) 50819.4 Conclusions: toward ‘personalized medicine’ 525References 525Appendix I 529Appendix II 531Index 537

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