Skills overview

• Programming:
  Python, C, C++, Bash, R, SQL (PostgreSQL, mySQL)
  Git, Jupyter Notebook
  Answer-Set-Programming
  LaTeX, markdown, HTML/CSS
• Machine learning:
  Regression (lin., polyn., logistic, GLM)
  Classification (decision tree, kNN)
  Clustering (hierarchical, k-means)
  Ensemble methods (random forest)
  Dimensionality reduction (PCA, MDS)
  Cross-validation
• NGS analysis:
  • tertiary:
    Differential Expression analysis (edgeR, DESeq)
    Gene Set Enrichment Analysis (Metascape)
    Single-cell data biases
  • secondary:
    Quality Control (FastQC)
    Read cleaning (trimmomatic, cutadapt)
    Mapping (genome: STAR; transcriptome: Kallisto) or de novo assembly (Trinity)
    Gene expression quantification (FeatureCounts)
• Data analysis tools:
  Data mining (pandas, numpy, scipy, sklearn, networkx)
  Visualisation (matplotlib, seaborn, graphviz, cytoscape)
• Public biological databases:
  NCBI/EMBL/DDBJ, Gene Ontology, UniProt, DoRothEA, Signor, Reactome, PDB…
• Theoretical computer science:
  Algorithmics, graphs, complexity
• Teaching skills:
  Design of teaching materials, exercises, exams
• Operating Systems:
  Linux (Debian-based mostly), Windows
• Languages:
  French (native language), English