These two sessions are cancelled due to Covid19 sanitary measures

We organize a 2 sessions training on Transposable annotation
 

From Nov 24, 2020 to Nov 27, 2020

• Venue : URGI - INRA, Bat 18 - Route de Saint-Cyr - RD10, 78026 Versailles Cedex

• 24-25/11    Session 1 : TE detection and annotation using REPET V3 package
• 26-27/11    Session 2 : Manual curation of transposable elements reference sequences obtained with REPET V3 pipelines. 
  • The session 2 will be on a BYOD-style (Bring Your Own Data), or using data provided by session 1

Prerequisite: Bioanalysis background using unix/linux command lines      

You can register for only session 1, only session 2 or both. Fees :

• INRAE: 300 € per session
• Academic :  340 € per session
• Not academic: 1100 € per session

Deadline for pre-registration October 1st, 2020  using the form at https://forms.gle/RhMVu8ojzg73ggMfA

A maximum of 8 participants for each session will be accepted, if the sanitary conditions of Covid-19 could allow it.

Preliminary program

Session 1:  TE detection and annotation using REPET V3 package  

• General introduction to Transposable Elements and their annotation
• Presentation of REPET V3 pipelines
• Running of pipelines for detection, classification and annotation-
• Outputs analysis

Session 2: Manual curation of transposable elements reference sequences obtained with REPET V3 pipelines.   

• Presentation of REPET outputs of TEdenovo and TEannot pipelines needed to curate the consensus library
• Presentation of tools used to assist curation/validation of TE consensus
• Presentation of examples- Practice on your data or a precomputed dataset

Prerequisite for Session 2

- You will attend the session 1 on this current course

OR

- You have already launched TEdenovo and TEannot pipelines using REPET V3 and the MySQL database is still alive and accessible

OR

- You have already participated to a TE annotation training (@URGI or in the frame of an Elixir  Genome assembly and annotation Course). In such a case we’ll provide you a precomputed dataset for the training