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This course is intended to provide the students with the prerequisites needed to be able to follow the courses of the Bioinformatics track dedicated to the analysis of NGS data (B4, B5, B6, B7, B8, B9, B10, B11).

The course has three main parts:
1 - Main UNIX commands
2 - Main R commands / getting acquainted with R scripts and Rstudio
3 - Main Galaxy commands

B4 - Basic concepts in HTS data analysis

Objectives:

Understand the key concepts underlying the quantification and comparison of OMIC datasets
Perform the initial steps of most HTS data analysis

B5 - Expression, quantification, differential analysis

Objectives:

Design a successful transcriptomics experiment
How to get differential expressed genes from raw data

B6-Variant calling

Objectives:

Learn the key steps to obtain genetic reliable variants from raw sequencing reads
First applications : use filtered files of variants to perform basic analyses

B7 - Genotype data & association studies

Objectives:

Getting exposed to important applications in the analysis of genetic data, genomewide
Analysing genomic data with R and other popular software

B8 - ChIP-seq data analysis

Objective:

Be able to analyze ChIP-seq data from sequencing reads to functional interpretation

B9 - Metagenomics

B10 - Single cell data analysis

Objectives:

Get acquainted with the key concepts of scRNAseq raw data analysis, quantification, functional characterization and visualization.

B11 - Functional analysis

Objectives

Understand the key concepts of functional analysis
Perform functional analysis on RNA-Seq data on a simple design using R tools

B12 - Advanced Unix Command

Objectives:

understand more complex but useful options and commands
learn regular expressions and few commands using them,
everything needed to create your firsts own scripts.

IA3 2024-2025: Quantification of large 2D images using QuPath

Date: Tuesday, January the 8th, 14:00am to 17:30pm.

Where: room 5, building 6, under the canteen.

Teacher: Stéphane Rigaud

Assistant teacher: Marie Anselmet and Gaëlle Letort

This course provides a comprehensive introduction to QuPath, an open-source software platform designed for bioimage analysis of large 2D images, from digital-pathology to large slide scan. Participants will learn the basic of the software, how to navigate and uses QuPath for analyzing images, object annotations, and segmentation.

The course covers essential topics such as project creation, annotation, tissue and nuclei segmentation, and quantification, enabling users to perform quantitative analysis of tissue samples. Using both QuPath integrated tools and State of the Art Deep-Learning extensions.

Course materials:

A laptop along with a mouse and a power connector
Download and install QuPath: https://qupath.github.io/
Download the following datasets:

Additional material: QuPath Documentation

Teacher: Marie ANSELMET
Teacher: Gaëlle LETORT
Teacher: Stephane RIGAUD

IA5 2024-2025: Bioimage analysis with Python

This course is an introductory course on utilizing Python for the analysis of biological images. While Fiji is great for quick and user-friendly image processing workflows, Python offers unparalleled flexibility and access to advanced tools like deep learning-based segmentation, batch processing, and custom analysis pipelines tailored to your research needs. The course is meant for students who have no or little experience with Python and basic knowledge in bio-image analysis (for example from following IA1).

Teacher: Marvin ALBERT
Teacher: Marie ANSELMET

IA4 2024-2025: Tracking cells and organelles with TrackMate

Using TrackMate for analysis of time-lapse microscopy movies.

This course is aimed at beginners in bioimage analysis, that wants to learn how to analyse time-lapse movies. Such movies can follow the movement of cells, in 2D or 3D over time, or the motility of organelles and large objects. They offer unique insight into the dynamics of biology objects, on the evolution of their morphology and of molecular processes, imaged via transmission light or fluorescence microscopy.

This half a day course includes a brief theoretical introduction about tracking algorithms, and is followed by a hands-on tutorial. For the tutorial we will use TrackMate (https://imagej.net/plugins/trackmate/), which is a somewhat popular Fiji plugin for tracking, developed in the Image Analysis Hub.