Bioinformatics for Geneticists, 2ed: A Bioinformatics Primer for the Analysis of Genetic Data

Michael R. Barnes

ISBN: 9788126574704

576 pages

Exclusively distributed by Star Educational Books Distributors

 

Description

This book is targeted primarily at research scientists in the field of Human and Mammalian genetics. However, our increasing knowledge of genetic variation is impacting all areas of biology, so that researchers in other fields of biology may add considerably to their work by including genetic data in their analyses. Hence, this book should be of value to any researcher or advanced student with an interest in genetic data. The book will assume only a basic knowledge of bioinformatics. It illustrates the value of bioinformatics as a constantly evolving avenue into novel approaches to study genetics. The book shows how bioinformatics can be applied at all stages of the genetics research process; to improve study design, to assist in marker selection and candidate gene identification, to aid data interpretation and management and to shed new light on the molecular pathology of disease mutations.

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.

 

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.

 

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 close and personal: the specifics.

4.5 Annotation: the next generation.

 

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.

 

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.

 

Section III Bioinformatics 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.

 

8 From Genome Scan 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.

 

9 Integrating Genetics, Genomics and Epigenomics to Identify.

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.

 

10 Tools 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.

 

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 silico predictions for further laboratory investigation.

11.10 Conclusions.

 

12 Functional in silico analysis of gene regulatory polymorphism

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.

 

13 Amino-acid properties and consequences of substitutions

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.

 

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.

 

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.

 

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.

 

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.

 

18 Needle in a haystack? dealing with 500 SNP genome scan

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.

 

19 A bioinformatics perspective on genetics in drug discovery and development  

19.1 Introduction.

19.2 Target genetics.

19.3 Pharmacogenetics (PGx).

19.4 Conclusions: toward personalized medicine'.

 

References.

Appendix I.

Appendix II.

Index.