Bioinformatic analyses
This portfolio presents a selection of bioinformatic methods I routinely use. While this list reflects my current expertise, I can also implement new analyses upon request, provided that relevant R packages or Python libraries are available.
For questions or collaboration inquiries, you can reach me using the links in the navigation bar of the Contact section.
Currently under construction, the various analyses will be progressively completed.
To support computationally intensive projects, I have access to the Mésocentre de Franche-Comté supercomputing facilities. This includes high-memory nodes (up to 1TB RAM) for large-scale data processing (e.g., expression matrices with millions of cells) and GPUs for machine learning and AI model training and inference.
With a strong background in cancer research and bioinformatics, I specialize in analyzing complex biological datasets to uncover insights into breast, lung, and colorectal cancers. Below are my key areas of expertise, ranging from well-established skills to exploratory techniques in genomics, transcriptomics, and computational biology.
Areas of Expertise
- Transcriptomics (Bulk RNA-seq): RNA-seq alignment (genome-based or pseudo-alignment), transcript or gene counting, and analysis. ref(1, 2, 3, 4, 5, 6)
- Differential Gene Expression Analysis ref(1, 2, 3, 4, 5, 6)
- Pathway Enrichment Analysis ref(1, 2, 3, 4, 5, 6)
- Immune Deconvolution ref(1, 5)
- Transcriptomics (Single-cell RNA-seq): Alignment and analysis (Seurat) ref(1)
- Transposable Elements (with a focus on Endogenous Retroviruses) ref(1)
- Biostatistics and Modeling (Machine Learning) ref(1)
- Metabam and IGV Visualization
- Genome Browser
- ATAQ-seq
- Bisulfite-seq
- Proteomics (PepQuery)
- DNA-seq: Variant calling, copy number variation, genomic signatures
- Fusion Transcripts
- TCRseq and BCRseq using MiXCR
- Digital Pathology (QuPath)
