compbiozurich/UZH-BIO392
Apr 2026 – May 2026
2 Months active
Languages Used
MarkdownNonePDFPythonR
Technical Skills
BWAFastQCGATKJupyterJupyter NotebookR programming
Amira Wildberger
PROFILE
Amira Wildberger
Developed and consolidated end-to-end genomic data analysis resources for the compbiozurich/UZH-BIO392 repository, focusing on workflow reproducibility and educational clarity. Delivered a Genomic Data Analysis Toolkit that streamlines CNV data download, quality control, variant calling, and population genetics analysis within Jupyter notebooks. Enhanced resource organization through systematic file renaming, cleanup, and removal of outdated materials, while introducing new R-based data exploration scripts and clinical genomics resources. Leveraged Python, R, and Pandas to enable robust data inspection, classification, and variant filtering. Maintained strong documentation and version control practices, ensuring traceability and accelerating data turnaround for research and teaching.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
4
Lines of code
11,880
Activity Months2
Your Network
38 peopleShared Repositories
27
Work History
May 2026
3 Commits • 1 Features
May 1, 2026May 2026 — Compbiozurich/UZH-BIO392: Delivered end-to-end Genomic Data Analysis Toolkit enabling CNV download/interpretation, data inspection/classification, QC and variant calling via a Jupyter notebook, and chromosome 7 analysis improvements (variant filtering and PCA). This work establishes an end-to-end genomic analysis workflow from data acquisition to interpretation, enabling researchers to obtain insights faster and with higher reproducibility. No major bugs reported in May; focus was on feature development and pipeline reliability. Key commits reorganized across three clusters to reflect completed milestones: ff0a153b07c96645c74d24beea92524e4f102739 (d3 exercises); cb8784224bfdaa8c374d6369144c155a4e97c186 (d6, d7, d9, d10); 3f926884e3713faf17632961417a1e6b5ec759e0 (d8_sampleB). Business value includes accelerated data turnaround, streamlined QC, and clearer data interpretation. Technologies demonstrated include Python tooling, Jupyter notebooks for QC and variant calling, CNV data processing, data inspection/classification pipelines, PCA, and robust variant filtering.
3 Commits • 1 Features
May 1, 2026May 2026 — Compbiozurich/UZH-BIO392: Delivered end-to-end Genomic Data Analysis Toolkit enabling CNV download/interpretation, data inspection/classification, QC and variant calling via a Jupyter notebook, and chromosome 7 analysis improvements (variant filtering and PCA). This work establishes an end-to-end genomic analysis workflow from data acquisition to interpretation, enabling researchers to obtain insights faster and with higher reproducibility. No major bugs reported in May; focus was on feature development and pipeline reliability. Key commits reorganized across three clusters to reflect completed milestones: ff0a153b07c96645c74d24beea92524e4f102739 (d3 exercises); cb8784224bfdaa8c374d6369144c155a4e97c186 (d6, d7, d9, d10); 3f926884e3713faf17632961417a1e6b5ec759e0 (d8_sampleB). Business value includes accelerated data turnaround, streamlined QC, and clearer data interpretation. Technologies demonstrated include Python tooling, Jupyter notebooks for QC and variant calling, CNV data processing, data inspection/classification pipelines, PCA, and robust variant filtering.
May 2026
April 2026
10 Commits • 3 Features
Apr 1, 2026April 2026 monthly performance summary for compbiozurich/UZH-BIO392. Focused on delivering end-to-end genomic analysis resources, maintaining course materials, and improving resource management. Key outcomes include notebook consolidation, data/clinical education resources, and targeted cleanup to enhance reproducibility and accessibility.
April 2026
10 Commits • 3 Features
Apr 1, 2026April 2026 monthly performance summary for compbiozurich/UZH-BIO392. Focused on delivering end-to-end genomic analysis resources, maintaining course materials, and improving resource management. Key outcomes include notebook consolidation, data/clinical education resources, and targeted cleanup to enhance reproducibility and accessibility.
Activity
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Quality Metrics
Correctness98.4%
Maintainability89.2%
Architecture93.8%
Performance89.2%
AI Usage23.0%
Skills & Technologies
Programming Languages
MarkdownNonePDFPythonR
Technical Skills
BWAFastQCGATKJupyterJupyter NotebookPLINKPandasPythonPython scriptingR programmingbioinformaticsdata analysisdata visualizationdocumentationfastp
Repositories Contributed To
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