About Elodie
Bioinformatics Engineer | Transcriptomics, Neuroscience & Multiomics Data & AI
I am passionate about computational biology, multi-omics data integration, and AI-driven insights. Open to collaborations, innovative projects, and professional opportunities in dynamic environments.
Skilled in integrating bioinformatics, data science, and machine learning approaches to extract meaningful biological insights.
Core expertise & technical skills
Omics data analysis: bulk RNA-seq, single-cell RNA-seq (scRNA-seq), single-nuclei RNA-seq (snRNA-seq), spatial transcriptomics, ATAC-seq, metabolomics
Programming & pipelines: R, Python, Nextflow, bash scripting, reproducible workflows
Data analysis & visualization: Seurat, Scanpy, Bioconductor, ggplot2, matplotlib, pandas, numpy and so on...
Machine learning & AI: supervised/unsupervised ML, deep learning (DL), predictive modeling for biological datasets (currently into it), LLMs
NGS technologies: Wet lab expertise for prep and sequencing. Bioinformatics skills on QC, alignment, differential expression, pathway analysis, cell chat and so on...
🧠 Current work
Contributing to transcriptomic research in neuroscience (Parkinson’s disease), other projects and optimization of R&D bioinformatics platforms.
🤖 Focus & interests
Currently expanding expertise in data science, machine learning (ML), and deep learning (DL) for genomics and transcriptomics applications, aiming to develop predictive models and integrative bioinformatic solutions.
📚 Research & publications
Co-author of publications in single-cell sequencing and molecular network analysis (including COVID-19-related research).
🔗 ORCID: 🔗 Projects & code GitHub:
French
Native or bilingual
English
Fluent
Can work on-site
Bordeaux (up to 50km), Bordeaux (up to 50km)
Experience
- Treefrog TherapeuticsBioinformaticianBIOTECHNovember 2024 - Today (1 year and 7 months)Bordeaux, FranceLed bulk RNA-Seq and single-nuclei RNA-Seq (~20k cells) characterization of a therapeutic product candidate in a QC- and GMP-like context, ensuring compliance with clinical data standards and full reproducibility through automated workflows and documented reporting. Supported R&D programs with exploratory multi-omics analyses (bulk RNA-Seq, snRNA-Seq, ATAC-Seq), including the active Parkinson's disease program; designed and deployed reproducible pipelines (Nextflow, Docker). Investigated technical variability through cross-functional analysis, bridging bioinformatics and process insights to support root-cause understanding.
- Self-EmployedBioinformatics ConsultantJanuary 2024 - November 2024 (10 months)Delivered 3 distinct bioinformatics projects across diverse biological contexts; provided hands-on training, comprehensive reports andfollow-up.
- SBRI Lab,Bioinformatics EngineerSeptember 2021 - December 2023 (2 years and 3 months)Lyon, FranceAuthored and shared the lab's core scRNA-Seq analysis codebase (R/Seurat); reused across multiple downstream projects and publications, becoming the lab's reference workflow. Adapted single-nuclei isolation protocols for downstream sequencing; supervised an intern on a single-cell project; presented results at internal and external events.
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Education
- Data Scientist - ML/DLLiora (ex Datascientest)400-hour curriculum (280h training + 120h applied capstone) covering Python, SQL, Data Visualization, Machine Learning (classification, regression, time series, text mining), Deep Learning (Keras, Computer Vision with OpenCV), Transformers & LLMs, Data Engineering and MLOps (Streamlit, MLflow). + project : Stem-Cell Pluripotency Prediction, Capstone Project with TreeFrog Therapeutics : Multi-modal Deep Learning model (CNN on microscopy images + classical ML on tabular features) predicting pluripotency and quality markers of stem-cell batches, aiming to replace costly wet-lab assays and accelerate R&D QC
- Master of Science in BioinformaticsUniversité de Rennes 12019Master Bioinformatique, Biologie, informatique, santé