M2 internship: Analysis of gene regulatory variants and sequence involved in the immune system

 Stage · Stage M2  · 6 mois    Bac+5 / Master   TAGC/INSERM U1090 · Marseille (France)

 Date de prise de poste : 2 janvier 2023

Mots-Clés

immune system deep learning human diseases genetic variants gene regulation bioinformatics genetics genomics cis-regulatory elements

Description

The immune system plays an important role in a large number of physiological traits ranging from infection, auto-immune diseases and tumorigenesis. Many of these  physiological processes are driven by frequent genetic variants. Most of these variants fall in non-coding regulatory regions and are likely gene regulatory variants but their mechanism is unknown. We are developing a sequence-based deep learning model to predict the functional impact of genetic variants on gene regulatory regions in immune cells.

In this internship, we would like to use our deep learning model to annotate genetic variants involved in different physiological traits such infection, auto-immune diseases and tumorigenesis. We would also like to analyze this deep learning model to uncover the sequence features that define the specificity of different sequences for different immune cell types. This internship project might allow to better understand the role of the immune system for physiological traits.

References:

- Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. 2021. DOI:10.1016/j.cell.2021.03.056
- Landscape of stimulation-responsive chromatin across diverse human immune cells. 2019. DOI:10.1038/s41588-019-0505-9
- DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. 2022. DOI:10.1038/s41588-022-01048-5

Candidature

Procédure : Envoyer un email à: Aitor Gonzalez

Date limite : 1 mars 2023

Contacts

Aitor Gonzalez

 aiNOSPAMtor.gonzalez@univ-amu.fr

Offre publiée le 2 août 2022, affichage jusqu'au 1 mars 2023