The main goal of the course is to demonstrate main uses of next-generation sequencing in medicine and biology, highlighting both great opportunities and notable dangers of "big data" approaches.
Course Prerequisites
Molecular biology (advanced), Genetics (basic), Cytology (basic), programming in R or Python (optional)
Teaching Methods
Lectures. Additionally, some practical problems would be offered to the motivated students to work in their spare time.
Assessment Methods
Students will be offered a simple multiple-choice test via a Google form. The same form would be used to collect feedback.
Texts
Most of my lectures rely on open-access resources and scientific papers and reviews that are either open access, or can be requested from authors. However, there are several books that are very useful for practical applications, which I won't be able to cover in my lecture course. First two are commercial, and the last two are free online "books":
1. The evolution of quantitative methods in biology. Introduction to bioinformatics. Data science and computational biology approaches to biological problems. Next-generation sequencing revolution and its implications.
2. Medical genetics and next-generation sequencing. Human genome and human genetics: how much do we really understand now? Using genomic data to help patients with rare conditions and to prevent disease.
3. Modern tendencies in drug development via the prism of 'omics' methods. Transcriptomics, proteomics, GWAS, CRISPR screens, and other technologies in modern medicine. The cycle of hype, open drug development, and startup culture in modern translational medicine.
4. Single cell methods as a leap into the 21-st century physiology. Creation of multidimensional atlases of human body. The evolution of mechanistic understanding of health and disease: a glimpse of hope for systems biology.
5. Tales of caution: discussion of most spectacular failures of big data approaches in biology and medicine.
