M2 Genetics, Genomics, Epigenetics and Evolution (GenE2)

M1 level in genomics, genetics and molecular biology

This teaching unit explores the latest advances and innovative concepts in functional genomics. Led by leading experts in the field, the course will cover cutting-edge scientific developments in comparative genomics, metagenomics, transcriptomics, proteomics, and their applications.

Key objectives:

• Bridge the gap between scientific questions and the most effective methodologies to address them.
• Foster critical discussions on how global approaches in genomics are reshaping our understanding of gene function and the genome ‘dark matter’.

Interactive learning:

• Roundtable discussions between students and researchers to explore the impact of emerging concepts in genomics.
• A one-day mini-symposium where students present their own reviews on self-selected themes, encouraging active participation and deeper engagement with the material

Basic concepts in Genetics, Genomics and Evolution.
Basic knowledge about gene structure and genome organisation in prokaryotes and eukaryotes.

This course explores key aspects of genome evolution across the tree of life — from bacteria to humans. Topics include mutational processes, complex chromosomal rearrangements, the dynamics of meiosis, and the impact of transposable elements on genome architecture.

Lectures are delivered by researchers actively working in these fields and will address the molecular mechanisms underlying genome dynamics, such as meiotic recombination, mutation, hybridization, transposition, and resistance to extreme DNA damage. These processes will be examined not only at the individual level, but also in the context of their evolutionary consequences for populations. Illustrative examples will be drawn from a wide range of organisms to show how these mechanisms have shaped genome structure and diversity throughout evolution.

Genomes, Phenotypes, Populations is intended for students wishing to deepen their knowledge of evolutionary genomics and gain first-hand experience in creating an original research project in this field. Prior training in population genetics and evolutionary biology will be appreciated but non-mandatory.

M1 levels in genetics, genomics and molecular biology. In addition, notions of high-throughput sequencing methods and epigenetic mechanisms developed in the Master 1 core course and/or several Master 1 teaching units (Epigenetics, Controle of gene expression, ‘génomique fonctionnelle’, ‘analyse et valorisation de données « omiques »’,…) should be understood and comprehended.

• Molecular biology: understanding of the concepts of gene, DNA/RNA, transcription, translation, PCR, sequencing, etc.
• Genetics and genomics: knowledge of genome structure, genetic variations, annotation, and principles of high-throughput sequencing (NGS).
• General bioinformatics: understanding of the principles of analysis pipelines, biological databases, etc. Ability to understand and interpret data generated by high-throughput sequencing.
• Basic knowledge of statistics applied to biology : tests, data representation, normalization.

• Understand the fundamental principles of single-cell omics approaches and their advantages compared to bulk population analyses.
• Describe the key experimental steps involved in single-cell transcriptomics, including cell isolation, RNA capture, UMI, reverse transcription, amplification, and library preparation.
• Identify the main technologies and platforms used in single-cell RNA-seq (primarily 10x Genomics) and understand their respective advantages and limitations.
• Acquire the foundations of bioinformatics analysis of single-cell data: quality control, normalization, dimensionality reduction, clustering, and cell-type annotation.
• Interpret biological results obtained from single-cell data to explore cellular heterogeneity, differentiation trajectories, and transcriptional signatures.
• Understand the principles and methodology of spatial transcriptomics: tissue fixation, in situ hybridization, spatial transcript capture, and molecular mapping.
• Understand the visualization and integrative analysis tools combining single-cell and spatial data (e.g., Seurat, Spatial Transcriptomics Tools).
• Assess the quality and reproducibility of data generated from single-cell and spatial technologies, taking into account technical and biological biases.
• Understand and apply Single-Cell Omics and Spatial Transcriptomics approaches to address complex biological questions, particularly in the study of cellular heterogeneity within solid tumors in cancer research and the characterization of different brain regions in neuroscience.
• Apply a critical approach when interpreting results from single-cell and spatial analyses, especially in relation to the underlying biological questions.
• Design and write a research project incorporating Single-Cell Omics and/or Spatial Transcriptomics methodologies, including scientific objectives, choice of experimental and analytical approaches, and data analysis strategies.

Scientific lecture-type conference delivered by specialists in the field of oncology or neurology, heads of sequencing platforms, or bioinformatics engineers specialized in single-cell omics and/or spatial transcriptomics analyses.

Basic knowledge in microbiology with high interest in the biology of bacteria, phages, and plasmids interacting in communities and in interactions with pluricellular hosts. Strong interest in genomics, big data analysis, and bioinformatics. A mobile, personal computer is required for this course (as it cannot be provided).

This course provides a basic and comprehensive introduction to the dynamic field of metagenomics, which is essential for understanding microbiome-host (holobiont) interactions. Participants will explore diverse topics such as the human gut microbiome, plant-microbe interactions, and the roles of viruses (the ‘virome’) within microbiomes. Metagenomics is key in advancing our understanding of microbial diversity, ecosystem dynamics, and their implications for health, agriculture, and the environment.

Lectures will be given by experts in the field, and participants will analyze real-world datasets by gaining hands-on experience with R (RStudio), computing clusters, and command-line tools. The approaches will include data pre-processing, community analysis, taxonomic profiling, functional annotation, and data visualization.

By the end of the course, participants will have the confidence to approach metagenomic datasets and understand their relevance to scientific challenges in microbiology, ecology, and beyond. The grade will be based on a course grade (65%) from weekly tasks and exercises and a final report (35%) due one week after the course (at the latest). S2: 100% oral exam for participants who did not pass S1. It will cover the lectures and key metagenomic concepts from the course.

Basic knowledge in microbiology with high interest in the biology of bacteria, phages, and plasmids interacting in communities and in interactions with pluricellular hosts. Strong interest in genomics, big data analysis, and bioinformatics. A mobile, personal computer is required for this course (as it cannot be provided).

This course provides a basic and comprehensive introduction to the dynamic field of metagenomics, which is essential for understanding microbiome-host (holobiont) interactions. Participants will explore diverse topics such as the human gut microbiome, plant-microbe interactions, and the roles of viruses (the ‘virome’) within microbiomes. Metagenomics is key in advancing our understanding of microbial diversity, ecosystem dynamics, and their implications for health, agriculture, and the environment.

Lectures will be given by experts in the field, and participants will analyze real-world datasets by gaining hands-on experience with R (RStudio), computing clusters, and command-line tools. The approaches will include data pre-processing, community analysis, taxonomic profiling, functional annotation, and data visualization.

By the end of the course, participants will have the confidence to approach metagenomic datasets and understand their relevance to scientific challenges in microbiology, ecology, and beyond. The grade will be based on a course grade (65%) from weekly tasks and exercises and a final report (35%) due one week after the course (at the latest). S2: 100% oral exam for participants who did not pass S1. It will cover the lectures and key metagenomic concepts from the course.

- To be able to calculate descriptive statistics parameters

- To understand the principles of RNA-seq data generation experiments

- To know the principle and different steps of a statistical test

No prior computer or statistical training is required.

The BigData workshop is intended for students in biology, medicine, pharmacy or any other scientific background, incling PhD Students who wish to acquire skills in the design, manipulation, processing and analysis of high-throughput sequencing data (genomics, transcriptomics, epigenomics, genotyping, …).

- To master the different variants of the linear model (anova, regressions...)

- Be able to model RNA-seq data using a statistical approach and perform differential analysis

- Be able to implement statistical analyses and graphical representations with R software

- To master different methods of multivariate analysis for large and heteorgeneous datasets and interpret the resulting outcomes

- Choose and apply the right test to identify over- and under-represented functional categories among a list of genes using statistical and bioinformatic tools

- Develop autonomy in the analysis of large and complex omics datasets, particularly RNA-seq data

- To understand basic principles regarding experimental designs including the notion of statistical power

The course unit takes place over three days of review and refresher sessions in statistics (early September) and four weeks of workshops (November–December). The workshop consists of two weeks of bioinformatics followed by two weeks of statistics.

The statistics component includes theoretical lectures followed by supervised practical sessions using R. During the first week, based on a common dataset, each student identifies a different biological question she/he wants to explore and determines the analysis plan she/he will implement to address it. The teachers validate the question and the proposed approach at the end of the week.

The second week is dedicated to continuing the lectures and implementing each student’s analytical plan. It is organized as independent work sessions, partly in the presence of a teacher who can provide guidance, and partly in full autonomy.

The Scientific Project unit consists of a guided bibliographical study carried out in close collaboration with the internship supervisor(s). Its primary objective is to enable students to define and contextualize the research project that will be developed during their internship. Through an in-depth analysis of the relevant scientific literature, students are expected to produce a structured document that includes a general introduction to the research field, a clear presentation of the biological context, and a critical overview of current knowledge related to the host team’s research theme.

This work also aims to help students formulate the specific objectives of their internship project, identify the key biological questions to be addressed, and understand their significance within the broader scientific context. Particular attention is given to the description and justification of the experimental and/or computational methodologies that will be employed to tackle these questions. Students are encouraged to critically assess the advantages and limitations of these approaches, as well as to consider possible alternative strategies.

Overall, the Scientific Project serves as a preparatory step for the research internship, fostering autonomy, critical thinking, and the ability to integrate and synthesize complex scientific information, while ensuring a coherent alignment between the proposed project, its objectives, and the methods implemented.

The scientific project will be evaluated based on a short report written by the student

The research internship of the Master 2 GenE2 at Université Paris-Saclay is a six-month immersion within an academic or industrial laboratory, in France or abroad. It enables students to apply and deepen their skills in genetics, genomics, epigenetics, bioinformatics, and data analysis through an original research project addressing current scientific questions. Supervised by experienced researchers, students develop autonomy, critical thinking, and the ability to design, implement, and interpret experimental and/or computational approaches. The pedagogical framework is based on research-driven learning, with personalized mentoring, regular interactions with supervisors, and full integration into the host laboratory environment. This internship is a key step in professional development, providing strong preparation for PhD studies or careers in research and biotechnology sectors.

The research internship is preceded by the “Scientific Project” unit, during which students prepare their project through a supervised bibliographical study in collaboration with their future supervisor(s). A mid-term follow-up is organized in March, either in person or remotely, to assess progress and provide guidance. The internship is validated by the submission of a written report and an oral defense.