compbiozurich/UZH-BIO392
Apr 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPythonRR MarkdownShell
Technical Skills
BioinformaticsData AnalysisData CurationData StorageData VisualizationDocumentation
Ayesha
PROFILE
Ayesha
Ayesha Gerber developed foundational infrastructure and reproducible workflows for the compbiozurich/UZH-BIO392 repository, focusing on bioinformatics and population genetics. She established project scaffolding and comprehensive documentation, standardizing file organization and naming to streamline onboarding and knowledge transfer. Using R, R Markdown, and shell scripting, Ayesha implemented a genetic analysis pipeline that included data preprocessing steps such as VCF to BED conversion, variant ID correction, and LD pruning, culminating in PCA and Admixture visualizations. Her work emphasized maintainability and transparency, enabling repeatable analyses and supporting collaborative research. The depth of documentation and workflow design improved project traceability and usability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
49Total
Bugs
0
Commits
49
Features
15
Lines of code
1,537
Activity Months2
Your Network
10 people
Work History
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for compbiozurich/UZH-BIO392. Focused on strengthening project documentation and delivering a reproducible genetic analysis workflow. Key outcomes: improved onboarding and collaboration transparency via new README and authorship updates; introduced an R Markdown-based genetic analysis pipeline for PCA and Admixture plots, with end-to-end data preprocessing (VCF to BED conversion, variant ID fixes, LD pruning) and visualization. No major bugs reported; no critical incidents. Impact: faster project ramp-up, repeatable analyses, and a foundation for population-structure studies. Technologies: R, R Markdown, genetic data preprocessing, PCA, Admixture, version control, documentation tooling. Commits include: ec369f1d639b088f9bb0567c87998ba6eebfd17f, 75a427e5048f6a9d4a5c1c495fba12bd024853d6, 2d44bf267d3840f410c44994c444c88c84e2eebf.
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for compbiozurich/UZH-BIO392. Focused on strengthening project documentation and delivering a reproducible genetic analysis workflow. Key outcomes: improved onboarding and collaboration transparency via new README and authorship updates; introduced an R Markdown-based genetic analysis pipeline for PCA and Admixture plots, with end-to-end data preprocessing (VCF to BED conversion, variant ID fixes, LD pruning) and visualization. No major bugs reported; no critical incidents. Impact: faster project ramp-up, repeatable analyses, and a foundation for population-structure studies. Technologies: R, R Markdown, genetic data preprocessing, PCA, Admixture, version control, documentation tooling. Commits include: ec369f1d639b088f9bb0567c87998ba6eebfd17f, 75a427e5048f6a9d4a5c1c495fba12bd024853d6, 2d44bf267d3840f410c44994c444c88c84e2eebf.
May 2025
April 2025
46 Commits • 13 Features
Apr 1, 2025April 2025 (2025-04) – UZH-BIO392 monthly summary: established a solid baseline for project onboarding, reproducibility, and long-term maintainability through foundational scaffolding and comprehensive documentation work. This groundwork enables faster feature delivery in future sprints and clearer knowledge transfer to new contributors. Key deliverables focused on scaffolding, documentation hygiene, and naming consistency, driving business value by reducing onboarding time and improving traceability across the repository.
April 2025
46 Commits • 13 Features
Apr 1, 2025April 2025 (2025-04) – UZH-BIO392 monthly summary: established a solid baseline for project onboarding, reproducibility, and long-term maintainability through foundational scaffolding and comprehensive documentation work. This groundwork enables faster feature delivery in future sprints and clearer knowledge transfer to new contributors. Key deliverables focused on scaffolding, documentation hygiene, and naming consistency, driving business value by reducing onboarding time and improving traceability across the repository.
Activity
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Quality Metrics
Correctness98.2%
Maintainability98.8%
Architecture98.8%
Performance97.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonRR MarkdownShell
Technical Skills
BioinformaticsData AnalysisData CurationData StorageData VisualizationDocumentationFile ManagementGeneticsGenomic Data AnalysisGenomicsPandasPopulation GeneticsR MarkdownR ProgrammingScientific Writing
Repositories Contributed To
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