
Nina Nolte developed a reproducible allele-specific expression analysis workflow for potato genomics in the longTREC/summer_school repository. She implemented the workflow as a Jupyter Notebook using Python, focusing on structured analysis of transcript metadata and accurate extraction of allele-specific counts from mapped reads, with attention to avoiding UTR length bias. Her work included detailed documentation, setup guidance, and quality control visualizations, which improved onboarding and analysis readiness for new users. By enhancing usability and reproducibility through environment setup instructions and code refactoring, Nina delivered a robust, well-documented tool that supports efficient, data-driven research in bioinformatics and RNA-Seq analysis.

In July 2025, delivered substantial usability, documentation, and reproducibility improvements for the Allele-Specific Expression Analysis Notebook in the longTREC/summer_school project. The changes enhance analysis readiness for potato ASE data and reduce onboarding time for new users by clarifying setup, column definitions, and QC visualization.
In July 2025, delivered substantial usability, documentation, and reproducibility improvements for the Allele-Specific Expression Analysis Notebook in the longTREC/summer_school project. The changes enhance analysis readiness for potato ASE data and reduce onboarding time for new users by clarifying setup, column definitions, and QC visualization.
June 2025 monthly summary for longTREC/summer_school: Implemented a reproducible potato allele-specific expression analysis workflow via a Jupyter notebook, enabling structured analysis of transcript metadata and CDS-focused allele counts. This work enhances data-driven insights in potato genomics and improves research efficiency and onboarding for new analysts.
June 2025 monthly summary for longTREC/summer_school: Implemented a reproducible potato allele-specific expression analysis workflow via a Jupyter notebook, enabling structured analysis of transcript metadata and CDS-focused allele counts. This work enhances data-driven insights in potato genomics and improves research efficiency and onboarding for new analysts.
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