Epigenomics Data Analysis From Bulk to Single Cell
Description
The aim of this workshop is to introduce best practice bioinformatics methods for processing, analyses and integration of epigenomics data. The online teaching includes lectures, programming tutorials and interactive group sessions.
Topics covered include:
- Data processing and analyses for differential DNA methylation with Illumina EPIC arrays and Bisulfite-seq
- ChIP-seq: overview of the methods, peak calling, peak independent/dependent quality metrics, joint visualization of different chromatin marks
- ATAC-seq: peak calling, peak independent/dependent quality metrics, differential accessibility analysis
- Integrative visualizations of epigenomics datasets
- Motif analyses: PWMs, motif enrichment, regression-based approaches
- Functional analyses, including finding nearest genes and custom features, GO terms and Reactome pathways enrichment
- Introduction to analysis of single cell omics such as scATAC-seq
Content Providers
Learning Outcomes
Upon completion of this course, you will be able to process and analyse various types of epigenomics data. In addition, you will learn to perform quality checks specifically on epigenomics data and carry out functional downstream analysis including data integration.
Prerequisites & Technical Requirements
Prerequisites
Basic knowledge in Linux
Basic R programming experience
Desired, to make learning experience easier:
Experience working on the SNIC center Uppmax or another HPC
Previous experience with NGS data analyses
Completing NBIS workshops “Introduction to Bioinformatics using NGS data” and “R Foundations for Life Scientists” or equivalent
Technical requirements
BYOL, bring your own laptop with R and RStudio installed
Affiliations & Networks
Activity log
