BookJu Han Kim.
Summary: This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader's bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. This textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Contents:
Part 1. BIOINFORMATICS FOR LIFE AND PERSONAL GENOME INTERPRETATION
Chapter 1. Bioinformatics For Life
Chapter 2. Next Generation Sequencing and Personal Genome Data Analysis
Chapter 3. Personal Genome Data Analysis
Chapter 4. Personal Genome Interpretation and Disease Risk Prediction
Part 2. ADVANCED MICROARRAY DATA ANALYSIS
Chapter 5. Advanced Microarray Data Analysis
Chapter 6. Gene Expression Data Analysis
Chapter 7. Gene Ontology and Biological Pathway-based Analysis
Chapter 8. Gene-set Approaches and Prognostic Subgroup Prediction
Chapter 9. MicroRNA Data Analysis
Part 3. NETWORK BIOLOGY, SEQUENCE, PATHWAY AND ONTOLOGY INFORMATICS
Chapter 10. Network Biology, Sequence, Pathway and Ontology Informatics
Chapter 11. Motif and Regulatory Sequence Analysis
Chapter 12. Molecular Pathways and Gene Ontology
Chapter 13. Biological Network Analysis
Part 4. SNPS, GWAS AND CNVS, INFORMATICS FOR GENOME VARIANTS
Chapter 14. SNPs, GWAS, CNVs: Informatics for Human Genome Variations
Chapter 15. SNP Data Analysis
Chapter 16. GWAS Data Analysis
Chapter 17. CNV Data Analysis
Part 5. METAGENOME AND EPIGENOME, BASIC DATA ANALYSIS
Chapter 18. Metagenome and Epigenome Data Analysis
Chapter 19. Metagenome Data Analysis
Chapter 20. Epigenome Databases and Tools
Chapter 21. Epigenome Data Analysis
Appendix A. BASIC PRACTICE USING R FOR DATA ANALYSIS
Appendix B. APPLICATION PROGRAM FOR GENOME DATA ANALYSIS INSTALL GUIDE.