IE en deep learning pour la génomique

 CDD · IE  · 12 mois (renouvelable)    Bac+5 / Master   Structure et Instabilité des génomes · Paris (France)


deep learning gene regulation epigenomics DNA sequences motifs


The improvement of DNA sequencing techniques lead to an explosion in the number and completeness of fully sequenced genomes. One of the major goals in the field of genomics is to make sense of this data and to annotate these DNA sequences by associating a biological function with sequence motifs located at different positions along the genomes. In parallel to these developments in genome reading and annotating, algorithmic advances and the use of graphical processing units (GPU) enabled the incredibly successful application of deep neural networks in many different contexts. This led to several breakthroughs in domains such as computer vision, speech recognition and machine translation. As a data driven domain, genomics followed this trend and pioneering studies demonstrated the efficiency of deep neural networks to annotate the genome with functional marks directly from the DNA sequence (1). A game changing advantage of these tools is their ability to predict a learned annotation on a variation of the genome, i.e. to predict the effect of mutations. As a first proof of concept of the ability to predict mutations on large scales, we (and others) developed the mutasome approach, in which all bp of a single genome are mutated individually to see the effect on a given genome annotation (2). If one can successfully predict the effect of mutations, it becomes also possible to design new sequences with controlled properties, a field now known as genome writing (3). In our team,we use different strategies to leverage the power of deep learning for interpreting genome sequencing data and for genome writing. The recruited ingeneer will use deep neural nets trained on nucleosome positioning, nucleosome modifications and/or gene expression data to design synthetic genomic sequences with tailored properties that can be experimentally tested in the lab or in the lab of our collaborators at the Institut Pasteur (R. Koszul and P. Navarro)

(1) Khodabandelou et al., PeerJ Computer science, 2020.           

(2) Routhier et al., Genome Research, 2020. 

(3) Routhier and Mozziconacci, PeerJ, 2022


Procédure : Envoyer un CV et une lettre de motivation par mail

Date limite : None


Julien Mozziconacci

Offre publiée le 15 novembre 2022, affichage jusqu'au 10 février 2023