The study of transcriptomics is key to understanding complex diseases. This new edition will build on the foundation of the first edition while incorporating the progress that has been made in the field of transcriptomics in the past six years, including bioinformatics for data analysis. Written by leading experts, chapters address new subjects such as methodological advances in large-scale sequencing, the sequencing of single-cells, and spatial transcriptomics. The new edition will address how transcriptomics may be used in combination with genetic strategies to identify causative genes in monogenic and complex genetic diseases. Coverage will also explore transcriptomics in challenging groups of diseases, such as cancer, inflammation, bacterial infection, and autoimmune diseases. The updated volume will be useful for geneticists, genome biologists, biomedical researchers, molecular biologists, bioinformaticians, and students, among others.

1. What is the transcriptome and how it is evaluated?

2. ALTERNATIVE SPLICING OF PRE-MESSENGER RNA
3. Transcriptome Analysis Using RNA-seq and scRNA-seq
4. TRANSCRIPTOMICS OF NEONATAL AND INFANT HUMAN THYMUS
5. Transcriptomics at the single cell level and human diseases: opportunities and challenges in data processing and analysis
6. METHODS FOR GENE CO-EXPRESSION NETWORK VISUALIZATION AND ANALYSIS
7. Comparative Analysis of Packages and Algorithms for the Analysis of Spatially Resolved Transcriptomics Data
8. The Interplay Between the Transcriptomics and Proteomics Profiles
9. TRANSCRIPTOME DURING NORMAL CELL DIFFERENTIATION
10. Transcriptomics to dissect the Immune System
11. Transcriptome Profiling in Autoimmune Diseases
12. Transcriptome Profiling in Experimental Inflammatory Arthritis
13. TRANSCRIPTOMICS AND IMMUNE RESPONSE IN HUMAN CANCER
14. MicroRNAs in Cancer
15. OxidativeStress, DNA Damage and Transcriptional Expression of DNA Repair and Stress Response Genes in Diabetes Mellitus
16. Large-scale gene expression in monogenic and complex genetic diseases
17. Transcriptome in Human Mycoses
18. Understanding Chagas Disease by Multi-omics data Integration, Functional and Enrichment Computational Analysis.