Société Française de Bioinformatique

Mots-Clés

cryo-ET tomograms image processing benchmarking software integration hpc

Description

In the context of a FIL (https://fil.cnrs.fr/) funded collaborative project we are looking for an M2 student for the implementation and optimization of a pipeline for the reconstruction and denoising of tomograms from CryoEM data. Cryo electron tomography (cryo-ET) is a rapidly evolving imaging field for the determination of in situ (ultra-) structures. Acquired tilt series need to be aligned and corrected for technical aberrations to optimize the quality of the tomographic reconstruction. However, even under optimal conditions, cryo electron tomograms suffer from a low signal to noise ratio, resulting in significant challenges for subsequent data interpretation. Hence, optimized workflows and denoising approaches are needed to facilitate result interpretation. As part the AIsyCryo funded project, we will be developing new algorithms to improve the signal-to-noise ratio (SNR) after tomogram reconstruction for better data visualization. The internship aims are 1) preparing a dataset that will be used for benchmarking our new and state-of-the-art algorithms, 2) benchmarking the algorithms using several quality metrics and distributed computational resources and 3) integrating the selected algorithms in a user-friendly software environment.

We are looking for a team player with a strong interest in image processing, tools benchmarking and implementation of user-friendly interfaces.

The successful candidate will work under the supervision of Christophe Combet in the CIRI’s StruVir team (https://ciri.ens-lyon.fr/teams/evir/struvir) and co-supervision by Valentin Debarnot (CREATIS). This emerging team is headed by Christiane Riedel and interested in the host factor requirements of animal viruses and the structure of their interactions. The CIRI is an internationally renowned research centre focusing on viral and bacterial pathogen as well as the host immune system.

Expected starting date: January 2027.

References:
Advances in cryo-ET data processing: meeting the demands of visual proteomics. Watson AJI and Bartesaghi A. Curr Opin Struct Biol, 2024. https://doi.org/10.1016/j.sbi.2024.102861.
Cryo-electron tomography: A long journey to the inner space of cells. Baumeister W. Cell, 2022, https://doi.org/10.1016/j.cell.2022.06.034.
Current data processing strategies for cryo-electron tomography and subtomogram averaging. Pyle E and Zanetti G. Biochem J, 2021, https://doi.org/10.1042/BCJ20200715.