Keywords: nucleus, genome, chromatin, epigenetic, 3D organization, transcription, DNA replication, DNA repair 

Prerequisites for the course: For each session, the speaker will provide a review to read beforehand, and do a brief introduction at the beginning with what is needed to understand his/her lecture. Hence no prerequisite is required as long as the student is motivated enough by the topic to read the bibliography provided by the speaker before the session. 

Course objectives and description: 

The course aims to provide a comprehensive overview of the latest research on nuclear processes, including gene transcription, silencing, DNA replication and DNA repair, with a focus on chromatin and epigenetic-based regulations and spatial genome organization and their relevance for developmental and environmental responses in diverse eukaryotic organisms (mammals, insects, plants, paramecium, and yeast). The lectures will be given by top researchers in their field, who have shined by their innovative and /or interdisciplinary approaches, such as single cell, state-of-the-art imaging or genomic approaches. 

The course will cover a range of themes, including DNA methylation, histone variants and modifications, non-coding RNA, transposable elements biology, meiosis, telomere biology, genome compartmentalization, single molecule analysis, developmental and environmental response. 


Aims:

The objective of the course is to initiate young life-science scientists to the bases of machine learning, and how to use it in Python with the scikit-learn package.

Organization:

The course will include twelve classes (two per week), each two-hours long, over a period of six weeks (with a one-week break), in April-May 2024.

A large part of each class will be devoted to practical coding exercises, further requiring a few hours of homework per week.

Assessment:

  • The participants will be regularly asked to explain their code during the classes.
  • Coding exercises and quizzes will be proposed over the duration of the course.

Course material:

The course will be based on the INRIA open online course (https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/), adapted towards biology.

Course pre-requisites:

A little bit of Linux and good bases in Python (being able to handle numpy arrays, ideally pandas dataframes, and knowing how to make plots).

If you have no Linux/Unix background, you can check the first sections of an online course such as https://www.tutorialspoint.com/unix/index.htm