Mots-Clés

Bioinformatics, genomics, cancer, computational biology, computational oncology

Description

Project Supervisors: Andrey Yurchenko (PhD, CRCN INSERM, Institut Gustave Roussy)

Department: UMR981, Institut Gustave Roussy
Institution: Institut Gustave Roussy
Duration: 6 months
Start Date: January 2025

Background:

Gustave Roussy, Sanofi,  Inserm, Institut Polytechnique de Paris and the University of Paris-Saclay are committed to developing personalized medicine in France through a patient-centered oncology cluster – the Paris Saclay Cancer Cluster (PSCC). The PSCC, a center bringing together key players in oncology innovation. This project, which is unique in Europe, will bring together the best scientific, human and technological expertise to shape the future of personalized medicine and accelerate the discovery of new customized cancer treatments.

The candidate will work in multi-disciplinary environment under PSCC umbrella on the project focused on development of robust bioinformatics workflow for the analysis of Single-cell DNA sequencing (scDNA-seq) data.

scDNA-seq allows the high-resolution exploration of genetic heterogeneity within tumors, identifying subclonal mutations, low-frequency variants and understanding the evolutionary processes of cancer. Tapestri technology is a powerful platform for scDNA-seq analysis that captures detailed mutational information at the single-cell level across a targeted panel of genes. In parallel, whole exome sequencing (WES) analysis offers a comprehensive view of mutational landscapes by identifying all coding region mutations within a mixed population of cells.

However, the analysis and integration of scDNA-seq with WES data is still an emerging field, with the potential to provide a more complete view of tumor clonal architecture and evolution. This project aims to develop a robust bioinformatics workflow for the analysis, visualisation and integration of Tapestri scDNA-seq data with WES data based on real samples from cancer pateints.

Objectives:

1. Develop a Bioinformatics Workflow:
   • Design a reproducible and efficient pipeline for processing, analyzing, and interpreting scDNA-seq data from the Tapestri platform (R or Python).
2. Integrate scDNA-seq and WES/targeted panel Data:
   • Develop methods to integrate scDNA-seq with WES data to better guide the analysis and enhance the understanding of mutational dynamics.
3. Data Visualization and Interpretation:
   • Implement interactive data visualization tools to represent clonal architectures, mutation frequencies, and the integration of scDNA-seq with WES results.

Candidate Profile:

• Background in bioinformatics, computational biology, or related fields.
• Strong programming skills (e.g., R, Python).
• Familiarity with version control systems, preferably Git.
• Previous experience with NGS data analysis and variant calling is a plus.
• Interest in cancer genomics and multi-omics data integration.