Sandra Therrien-Laperriere

Lead bioinformatician responsible for designing, implementing, and maintaining pipelines for the analysis of clinical-grade Next-Generation Sequencing (NGS) data.

• Developed end-to-end pipelines in Python (NumPy, Pandas, TensorFlow) to automate genomic variant analysis for cardiovascular and rare genetic disorders.
• Validated and documented new analysis methods following IQOQPQ protocols to support clinical production and research-grade reliability.
• Collaborated with cross-functional teams—including clinicians, wet-lab scientists, IT, and QA—to deliver validated pipelines within deadlines and compliance constraints.
• Led feasibility assessments for new bioinformatics tools and statistical workflows prior to integration in routine diagnostics.
• Oversaw code refactoring, testing, and documentation, ensuring reproducibility, traceability, and compliance with regulatory standards.
• Integrated multi-modal datasets from diverse sequencing and metabolomic sources.

Led a research project combining genomics and metabolomics to interpret mGWAS data.

• Created PathQuant, a tool for annotating gene–metabolite relationships via metabolic pathway mapping
• Co-authored a publication in FASEB Journal (2017; 31(1)) — HAL-02622661
• Presented results at EB 2017 (Chicago) and Congrès R 2017 (Québec)
• Supported tool integration in downstream bioinformatics workflows and consortium studies

Short-term research internship contributing to computational biology projects in metabolomics and agrigenomics.

• Participated in data analysis workflows integrating metabolomic profiles and genomic markers
• Gained exposure to large-scale systems biology research in an academic R&D environment
• Collaborated with researchers on exploratory analysis and early-phase data modeling using R and scripting tools
• !!! Mention tools used (e.g., R, Python, Cytoscape, KEGG if applicable) !!!!

Supported instruction in a graduate-level metabolomics course focused on the analysis and interpretation of biological datasets.

• Assisted students with practical assignments involving R-based analysis of metabolomics data
• Provided guidance on data normalization, statistical interpretation, and visualization techniques
• Helped review course content and clarify bioinformatics concepts in a group setting

Supported instruction in a hands-on undergraduate course on mobile application development using Java and the Android SDK.

• Helped students troubleshoot development environments and debug application logic
• Provided lab assistance and reviewed programming assignments
• Introduced concepts in mobile UI design, Android activities, and event-driven architecture