371 rue du Prof. J. Blayac
SANOFI R&D - INTERNSHIP
Develop Next Generation Sequencing bioinformatics and statistical analysis pipelines
Within the Biostatistics & Programming department of Sanofi, in the Biomarker Data Science team, the internship will focus on developing pipelines to analyze high throughput sequencing data.
Indeed, personalized medicine i.e. giving the right patient the right treatment, is a key objective of current clinical development projects. In this context, more and more omic data (e.g. genomics, transcriptomics, metabolomics, proteomics) are generated from subjects enrolled in clinical trials and with the arrival of new, ever more efficient technologies, the complexity of the data generated is increasing drastically. Efficient analysis of these omic datasets plays a crucial role in better understanding the molecular mechanisms of action of drugs and the variability of response to treatment among individuals, thus promoting the advent of precision medicine. In this context, the Department of Biostatistics is developing pipelines to perform quality control, preprocessing and statistical analysis of high throughput sequencing data. The aim of this internship is to further develop pipelines for RNA or whole exome sequencing data. In the course of his internship, the student will also be able to apply such pipelines to clinical datasets and thus discover or better understand the field of statistical analyses of clinical trial data.
• Bac + 5 (Master 2 in Bioinformatics or equivalent)
• Knowledge in genomics, NGS technologies
• Knowledge of linux environment
• Knowledge of R and python would be a plus
• Abilities : autonomous, rigorous, team player
Duration: 6 months
Start date: January-April 2021
Location: Montpellier (34)
• SANOFI :
o Annick Péléraux, Biostatistician - Biostatistics & Programming Department – Biomarker Data Science Group - Montpellier : firstname.lastname@example.org
Leipzig, Jeremy. (2016). A review of bioinformatic pipeline frameworks. Briefings in Bioinformatics. 18. bbw020. 10.1093/bib/bbw020.