Training

Bioinformatics program for PhD students 2019-2020

A TARGETED CURRICULUM DEVELOPED BY THE COMPUTATIONAL BIOLOGY DEPARTMENT

In accordance with a request in 2016 from the former General Board of Directors, the Computational Biology Department has developed this training program dedicated to Institut Pasteur PhD students. Each and every student at the institute must attend at least 50 hours of training. At the minimum, he or she is required to validate the statistical modules (which, depending on their background, some students may skip), and possibly choose additional modules according to his or her background and field of research.  

A CORE FOUNDATION AND THE OPTION TO SPECIALIZE

This 50-hour training program required for all Institut Pasteur PhD students begins with a group of common core courses including a knowledge base in reproducible research, R Programming and Statistics. Each student then chooses a track of additional modules— Bioinformatics or Image Analysis—according to their background and field of research. Students already proficient in R programming and statistics can skip certain modules or the entire track.  

INSTITUTIONAL RECOGNITION

This PhD program has been acknowledged by the doctoral schools at the Sorbonne Bio, CDV, Ecole doctorale interdisciplinaire européenne Frontières du Vivant, and ED-SDSV. Check out the course’s flyer for more information and and visit the Pasteur course web page!
   

COURSE PROGRAM

Mandatory Common Core

This 6h course is mandatory for all PhD students. They will learn how to get help and assistance from the Department of Computational Biology and from the Hubs for questions in programming, experimental design, data analysis and image analysis. They will attend three short lectures in programming and reproducible resaerch. Finally, they will have the opportunity to assess their level in R programming and statistics and to register to the modules of their choice in the R programming and statistics, Bioinformatics and Image analysis tracks. Two sessions will be organized, on Nov 12th, 2019 and Jan 8th, 2020. Students already on the campus are strongly encouraged to attend the first session.  
Program of the mandatory common core module, session 2
   

Other modules

The figure below shows all the available modules. To attend these lectures you need to bring your own laptop. No special configuration is required. Just avoid to come with your grandma’s machine.  
Description of the Bioinformatics program
  A detailed description of each and every module can be found here.

UPDATE : New dates and additional sessions !!!

  These two modules were shifted by one week:  
  • • Introduction to R and Statistics, session 1: December 10-12
  • • Hypothesis testing, session 1: December 16-17, meeting room, 2nd floor, Yersin building
  Following remarks that were made during the first common core course, and to take into account the high number of registrations in all modules, a new session will be organized for most modules of the PhD program. New dates are given below :

R programming and statistics

A third session IN FRENCH will be organized in June :
  • • Introduction to R and stats : 9-11 june
  • • Hypothesis testing : 22-23 june
  • • Linear models : 25-26 june
  • • Multivariate analyses : 29-30 june
Students who have registered to the second session (in march) and wish to switch to the third one can send an email to c3bi-teaching@pasteur.fr    

Bioinformatics

The Basic concepts in NGS data analyses lecture – Feb 3rd – will be extended to 35 seats. A second session will be organized, dates will be communicated shortly.    

Image analysis

A second session will be organized end of April (28-30)
  • • Fiji : 28 April
  • • Icy : 29 April morning
  • • Advanced Icy : 29 April afternoon
  • • Super resolution images : 30 April morning
  • • Machine learning : 30 April afternoon
 

COURSE MATERIAL

  Mandatory common core:   R Programming and Statistics
  • • Introduction to R and Statistics – Course
  • • Hypothesis testing – Course – Lab
  • • Linear Models – Course – Lab
  • • Multivariate analysis – Course – Lab