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
Contributed to the compbiozurich/UZH-BIO392 repository by establishing foundational project scaffolding and comprehensive documentation to support reproducibility and efficient onboarding. Developed and maintained a genetic analysis workflow using R and R Markdown, enabling population structure studies through PCA and Admixture visualizations. Implemented data preprocessing steps such as VCF to BED conversion, variant ID correction, and LD pruning, ensuring robust and repeatable analyses. Focused on documentation hygiene, naming consistency, and clear authorship attribution to facilitate collaboration and knowledge transfer. Leveraged skills in bioinformatics, data curation, and technical writing to deliver maintainable solutions without reported bugs over the two-month period.
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
27 peopleShared Repositories
27
Your NameawildbergerMember
anaispradoMember
anschumaMember
AnoukMember
BaBurriMember
strebelindaMember
strebelindaMember
Emily MosbergerMember
GarfieldfeiMember
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
Overview of all repositories you've contributed to across your timeline