Société Française de Bioinformatique

Mots-Clés

scRNA-seq spatial transcriptomics cancer genomics single cell

Description

This is a 5 year contract position on Gustave Roussy terms and conditions of employment.

Summary

The Clinical Discovery Bioinformatics group within the L’Institut Hospitalo-Universitaire (IHU) de médecine de précision PRISM at Gustave Roussy Institute is seeking to recruit a collaborative and self-driven senior bioinformatics data scientist with a passion for analysing data in a clinical research environment. IHU PRISM is a multidisciplinary, cross-tumor program aimed at better understanding the biology of each patient’s cancer to reduce mortality. In this key role, you’ll bring your expertise in multiomic data analysis to tackle clinical research questions using datasets from large cancer patient cohorts. As part of our dynamic team, you’ll help provide a unified, clinically contextualized view of multiomic data generated by the institute’s ground breaking clinical discovery programs. You will play a key role in helping to answer the latest questions in cancer translational research and help foster collaborations with research teams to develop advanced analyses and AI tools for translational medicine. We achieve this by managing and developing an integrated data analysis platform that implements the latest bioinformatics and data science analysis approaches. The successful applicants will have a proven track record in collaborative academic research, including statistical analysis and interpretation of large, complex datasets. You should possess a PhD with significant experience in bioinformatics, mathematics, or statistics applied to biomedical-based research with a large computational component.

Project summary

In this role, you will be responsible for implementing and developing data analysis strategies to answer clinical research questions posed by the clinical program teams. You will apply the latest analysis methodologies to spatial-transcriptomic, single-cell, bulkRNA-seq, WGS, and exome modalities to identify key biological processes driving phenotypic differences in cancer across patients (e.g. response to treatment, tumour progression, clinical outcome, etc.). You will work closely with the program teams to develop clinical insights that will ultimately improve patient care. A key aspect of the role will be to analyse single-cell and spatial transcriptomic data associated with our clinical trial and cancer atlas programs. This will involve the application of established methods and the development of analysis approaches to characterise the spatial transcriptomic landscapes of cancer using Visium, Xenium, Merscope and GeoMx technologies. You will work closing with the clinical discovery team to implement components of this strategy at scale. Results delivery is a key component of the data analysis platform and you will have the opportunity to use the latest technologies to develop innovative ways of presenting results to the clinical groups. There is a rich bioinformatics and data science community at GR within which you will play an active role. You will work closely with this community to evaluate, implement and develop new analysis approaches. Analysis reproducibility is another key characteristic of the data analysis platform and you will have the opportunity to use the latest technologies and methodologies to ensure full reproducibility. You will have the opportunity to publish in collaboration with the clinical and research teams.

Key responsibilities

• Provide data analyse expertise to help the clinical program teams achieve their research objectives.
• Develop and implement analysis strategies for single-cell and spatial transcriptomic data sets by applying a wide range of methodologies and resources, in line with the clinical and scientific objectives of the programs.
• Work closely with the clinical teams throughout the discovery process, helping them to interpret results and make research decisions.
• Develop innovative ways to present clear and documented results to the teams using the latest reporting technologies.
• Work with the Cancer Discovery Bioinformatics team to introduce validated methods into the platforms workflows.
• Work within the group’s integrated data analysis platform to ensure robust and reproducible results.
• Provide expertise to inform on the experimental and analysis design process.
• Work independently throughout the data analysis workflow.
• Support the wider objectives of the clinical discovery bioinformatics group.
• Help to develop others by mentoring and sharing expertise and experience.
• Continue professional development through maintaining awareness of developments in the wider bioinformatics and research communities.
• Participate and contribute to Gustave Roussy meetings, workshops and seminars.
• Publish where appropriate.

Key experience and competencies

The post holder should be open, dynamic and collegial, in addition to:

Essential Qualifications, experience and competencies:

• A degree in a relevant subject with an extensive analytical component e.g. bioinformatics, statistics, molecular biology or mathematics.
• Experience of analysing different types of high-throughput sequencing applications within a research environment.
• Excellent R and/or Python skills with knowledge of available packages and resources.
• A good theoretic understanding of bioinformatics and data science methodologies applied to omics data.
• Experience of spatial and single-cell transcriptomic technologies.
• Good Linux and HPC skills.
• Experience in delivering and communicating analysis results to research groups.
• The ability to work independently across the analysis project workflow.
• The ability to organise and prioritise workload.

Desirable Qualifications, experience and competencies:

• Familiarity with cancer biology.
• Familiarity with the application of statistical techniques to biological data.
• Computational skills applied to HPC, big data, software development and web technologies.
• Experience in applying expertise to the experimental and analysis design process.
• Experience in building and deploying pipelines (e.g. Snakemake, Nextflow) for data science.
• Experience of software development processes and tools.