This course will introduce biologists and bioinformaticians to the field of regulatory epigenomics. We will cover a range of software and analysis workflows for processing and quantitative analysis of ChIP-seq, MNase and ATAC-seq data, RNA-seq, and Hi-C data. We will start by introducing general concepts about epigenomics. From there, we will then continue to describe the main analysis steps to go from raw sequencing data to processed and usable data. We will present classical analysis workflows, their output and the possible paths to investigate downstream of this. Towards the end of the workshop, we will focus on multi-omics data integration. Throughout the workshop, real-world datasets will be analysed using bash tools and R/Bioconductor packages.

Target audience and assumed background

This course is primarily targeting researchers who are relatively new to the field of bioinformatics, with practical experience in the experimental side of epigenomics. Attendees should have a background in biology as well as be familiar with genomic data and common file formats from NGS sequencing experiments (fastq, BAM, BED).
Practical exercises will use command-line Linux and R code and will be presented as notebooks to ensure reproducible coding. Attendees are expected to know the basics of the R programming language and have prior experience with the Unix command line.

Learning outcomes

By the end of the course, participants will be able to:

• Understand the principles and experimental designs of key genomic assays.
• Perform preprocessing, alignment, and quality control for RNA-seq, ATAC-seq, ChIP-seq, and
Hi-C data.
• Conduct downstream analyses such as differential expression, peak calling, motif analysis, and
interaction visualization.
• Integrate multiple genomic data types to derive biological insights.

Day 1: Gene Expression Analysis.  2-8 PM Berlin time

• Lecture: Overview of RNA-seq: Introduction to gene expression analysis, experimental design, and preprocessing (quality control, adapter trimming).
• Practical: Hands-on processing of RNA-seq data using FastQC, trim_galore, and alignment.
• Lecture: Quantification of gene expression: Counting reads, generating count matrices, and exploring data.
• Practical: Use of featureCounts and DESeq2 for gene expression quantification and differential expression analysis in R.

Day 2: Chromatin Accessibility.  2-8 PM Berlin time

• Lecture: Introduction to chromatin accessibility assays (ATAC-seq, MNase-seq): Biological principles, protocols, and data structure.
• Practical: Preprocessing ATAC-seq/MNase-seq data: Quality control and alignment using bowtie2 and samtools.
• Lecture: Peak calling and accessibility visualization: Understanding peaks and enriched regions.
• Practical: Use of YAPC for peak calling and Bioconductor’s dedicated tools for coverage visualization.

Day 3: Chromatin Composition.  2-8 PM Berlin time

• Lecture: Introduction to ChIP-seq: Understanding transcription factor binding and histone modification profiling.
• Practical: Preprocessing ChIP-seq data: Quality control, alignment, and handling replicates.
• Lecture: Peak calling and motif analysis: Identifying regions of interest and associated motifs.
• Practical: Use of MACS2 and motif analysis tools like HOMER or R packages such as TFBSTools.

Day 4: Chromatin Interactions.  2-8 PM Berlin time

• Lecture: Introduction to chromatin interactions and Hi-C: Basics of 3D genome organization and Hi-C experimental protocols.
• Practical: Processing Hi-C data: Alignments and quality assessment using hicstuff.
• Lecture: Visualization and analysis of chromatin interaction maps.
• Practical: OHCA R package suite for visualization and interaction matrix analysis.

Day 5: Multi-Omics Integration.  2-8 PM Berlin time

• Lecture: Overview of multi-omics integration: Strategies, challenges, and biological insights from integrating epigenomics data.
• Practical: Combining RNA-seq, ATAC-seq, ChIP-seq and Hi-C data with Bioconductor.
• Lecture: Case studies in multi-omics analysis: From data to biological insights.
• Practical: Guided analysis of a multi-omics dataset.

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.