 |
Bioinformatics for geneticists : a bioinformatics primer for the analysis of genetic data / [edited by] Michael R. Barnes. — 2nd ed. — Chichester : wiley, c2007. –(58.17115/B615/2nd ed.) |
Contents
Contents
Foreword
Preface
Contributors
Glossary
SECTION I AN INTRODUCTION TO BIOINFORMATICS FOR THE GENETICIST
1 Bioinformatics challenges for the geneticist
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
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
SECTION II MASTERING GENES， GENOMES AND GENETIC VARIATION DATA
3 The HapMap - A haplotype map of the human genome
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
4.1 Introduction
4.2 Genomic sequence assembly
4.3 Annotation from a distance: the generalities
4.4 Annotation up dose and personal: the specifics
4.5 Annotation: the next generation
References
5 Finding， delineating and analysing genes
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
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 BIOINFORMA'I1CS FOR GENETIC STUDY DESIGN AND ANALYSIS
7 Identifying mutations in single gene disorders
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 to Culprit Gene
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
9.1 Introduction
9.2 Dealing with the (draft) human genome sequence
9.3 Progressing Loci of interest with genomic information
9.4 In silica 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 Toots for statistical genetics
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
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 silica predictions for further laboratory investigation
11.10 Conclusions
References
12 Functional in silico analysis of gene regulatory polymorphism 281
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 311
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
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?
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
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
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 500 000 SNP genome scans
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 495
19.1 Introduction
19.2 Target genetics
19.3 Pharmacogenetics (PGx)
19.4 Conclusions: toward 'personalized medicine'
References
Appendix I
Appendix II
Index
