Mots-Clés

TnSeq AntiMicrobial Resistance (AMR) bioinformatics bacteria genomics

Description

Evotec is a drug discovery and development partnership organization collaborating with numerous partners across the world to develop new anti-infectives products. We efficiently move programs from target identification to investigational new drug (“IND”) status and have established a proven track record of contributing to the identification and characterization of multiple anti-infective agents from pre-clinical candidates through to marketed drugs.

At Evotec ID Lyon we are applying advanced method in Genomics, Transcriptomics and Phenomics to generate large volumes of biological data with the objective to characterize the mechanism of action and/or resistance (MoA / MoR) of candidate antimicrobial products. This knowledge is generated by a close collaboration between wet-lab (microbiology; molecular biology, NGS), project scientists and the bioinformatics team. As integral part of the data analysis we develop a web-based platform for microbiomics.

The Evotec Lyon bioinformatics team is looking for a highly motivated and dedicated:

                                             BIOINFORMATICS INTERN M2 (M/W)

Evotec Lyon bioinformatics team endeavors to enhance the technological AMR offer by integrating new omics approaches for deciphering new antibacterials mode of action and mechanism of resistance. One of the most promising and advanced projects is the use of transposon sequencing. TnSeq combines large scale (genome-wide) transposon mutagenesis with NGS and allows identifying, for a tested growth condition, essential genes, but also genes which inactivation enhances or reduces the bacterium fitness. Thanks to an internal innovate project, TnSeq profiles were generates for two ESKAPE species (a third species TnSeq data is in acquisition) with approximately 50 reference antibiotics compounds. The proposed internship project involves:

• Benchmarking our current method for identifying essential genes with the published statistical approaches,
• Propose, test and implement alternative approaches,
• Identify optimal TnSeq settings (sequencing depth and replicate count)
• Implement a shiny web application for TnSeq processed results interactive exploring
• Analyze the complete dataset and assess TnSeq data suitability for machine learning MoA classification (currently the data was analyzed for each species and product independently)

This is an ambitious project, but the bioinformatics team has all the expertise to ensure the success of this project.

Your Qualifications

• Preparing the Master’s degree in Computer Science, Statistics or Bioinformatics
• Demonstrated programming skills in R and at least one additional programming language (e.g. Python, Perl)
• Knowledge in applying machine learning approaches for bioinformatics problems
• Experience in NGS data analysis would be highly appreciated
• Knowledge in the field of microbiology or microbial genomics would be highly appreciated
• Excellent interpersonal and communication skills and the ability to work independently in a fast-paced work environment
• Fluent in English