Day 1: Introduction to omics technologies
The field of omic data analysis has been rapidly evolving during the last few decades. Moreover, the importance of bioinformatics in biomedical research has steadily continued to increase, becoming almost a required skill for any researcher in the field. Throughout this module, the students will:
- Know the landscape of omic technologies
- Learn which omics may be more suitable for a given research question.
- Learn how to analyze several types of omics.
Lectures
[9:00-9:50] Lecture 1: Course introduction and omics landscape
Speaker: Wen Zhong
Course introduction and a brief overview of the evolution of omics technologies, including sequencing, mass spectrometry, and antibody-based techniques.
[10:00-10:50] Lecture 2: Cutting-edge technologies in the Swedish molecular diagnostics landscape
Speaker: Colum Walsh
The facilities and technologies available to researchers in Sweden, including genomics, proteomics, metabolomics, bioimaging, and bioinformatics
[10:50-11:00] Break
[11:00-11:30] Lecture 3: Sequencing technologies
Speaker: Jyotirmoy Das
Modern high-throughput sequencing techniques, with applications to DNA, RNA and epigenetic sequencing.
[11:30-12:00] Lecture 4: Proteome and metabolome technologies
Speaker: Wen Zhong
Different technolgies that can be used to generate proteomics and metabolomics data, including mass spectrometry, antibody-based (e.g., Olink) and aptamer-based (e.g. SOMAScan).
Workshops
[13:00-16:00] Omics data, visualization, processing
Instructor: Jyotirmoy Das
Introduction of omics data and essential steps of processing, analysis, and visualization (epigenetics and proteomics datasets).
[13:00-16:00 cont.] MethylR, CompleteOlink and OlinkWrapper
Instructor: Jyotirmoy Das
MethylR is an R-based tool designed for the analysis and visualization of DNA methylation data, including differential methylation and functional annotation.
CompleteOlink and OlinkWrapper provide streamlined pipelines for processing, quality control, and analysis of Olink proteomics data, enabling efficient handling of large-scale protein expression datasets.