NGS analysis for gene regulation and epigenomics


28 February - 4 March 2022

Due to the COVID-19 outbreak, this course will be held online



General topic: Regulatory function of chromatin



This course will introduce biologists and bioinformaticians to the field of regulatory genomics. We will cover a broad range of software and analysis workflows that extend over the spectrum from the best practices in the quantitative analysis of ChIP-seq and ATAC-seq data to the analysis of the chromatin 3D structure (such as A/B compartments, chromatin loops or TADs). This course will help the attendees gain accurate insights into local and spatial regulatory functions of the chromatin.


Target audience and assumed background


The course is aimed at researchers interested in learning how to extract biological insights from genomics data, such as ChIP-seq, ATAC-seq or Hi-C. It 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 the UNIX command line, Python and R code. We will use Snakemake to ensure reproducible coding,and jupyter-notebook to generate clear interactive reports. We will start by introducing general concepts of chromatin biology. From there, we will then continue to describe all major analysis steps from the raw sequencing data to the processed and usable data. Finally, we will focus more specifically on thedifferent analyses strategies to use to extract information from genomic datasets such as Hi-C, ATAC-seq or ChIP-seq.


Learning outcomes


Characterisation of the global 3D structures from the sequencing data.


Detection of regulatory interactions and quantification of their changes between conditions.


Good practices to avoid confounding variables and pitfalls in the processing.


Proper use of controls and normalization.


Visualisations techniques and integrations of different layers of informations.


Example data


We will use data from yeast and mouse genomes for Hi-C analyses. While mouse and C. elegans data will be used for the ATAC and ChIP-seq analyses. We encourage the participants to bring, analyze (if possible) and discuss their own data



Monday - Classes from 2 to 8 pm Berlin time. Introduction to epigenomics, and 3D organisation ofgenomes.genomes

Lecture 1

Epigenomics introduction

Hi-C protocol

General processing of Hi-C data


Lab 1

Step-by-step processing of Hi-C data and integration with snakemake.

Use of existing Hi-C processing pipelines and common formats.

Manipulation and visualisation of sparse matrices using the cooler package



Dr Jacques Serizay







COst overview

Package 1


                                    480 €

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.