ejh243/BrainFANS
Oct 2024 – Dec 2024
3 Months active
Languages Used
BashMarkdownRShellPythonYAML
Technical Skills
BioinformaticsConda Environment ManagementData AnalysisDocumentationPipeline ConfigurationPipeline Development
marinafloresp
PROFILE
Marinafloresp
Worked on the BrainFANS repository to enhance the ATAC-seq analysis pipeline by standardizing environments, improving reproducibility, and streamlining onboarding. Focused on Conda-based environment management and cross-system compatibility, the work included updating installation steps, clarifying documentation, and integrating MACS3 for peak calling. Addressed quality control by automating QC checks and fixing reporting bugs, while introducing standardized sample metadata and interactive Plotly visualizations for exploratory data analysis. Used Python, R, and Shell scripting to refactor workflows, remove deprecated components, and ensure robust, version-controlled pipelines, resulting in more reliable analyses and reduced technical debt for future contributors and users.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
17Total
Bugs
2
Commits
17
Features
6
Lines of code
2,074
Activity Months3
Your Network
22 peopleWork History
December 2024
6 Commits • 2 Features
Dec 1, 2024December 2024 BrainFANS monthly summary for ejh243/BrainFANS. Delivered standardized ATAC-seq input and enhanced QC interpretability, enabling reliable downstream analyses, improved data integrity, and faster data-driven decisions. Key changes focused on input standardization, QC accuracy, and enhanced visualization for exploration of results.
6 Commits • 2 Features
Dec 1, 2024December 2024 BrainFANS monthly summary for ejh243/BrainFANS. Delivered standardized ATAC-seq input and enhanced QC interpretability, enabling reliable downstream analyses, improved data integrity, and faster data-driven decisions. Key changes focused on input standardization, QC accuracy, and enhanced visualization for exploration of results.
December 2024
November 2024
6 Commits • 2 Features
Nov 1, 20242024-11 BrainFANS: Standardized ATAC-seq environment and tooling across systems, improved QC/peak calling robustness, and enhanced onboarding documentation. These changes reduce environment drift, fix critical Picard-related issues, improve data quality and reliability, and accelerate reproducible analyses across platforms.
November 2024
6 Commits • 2 Features
Nov 1, 20242024-11 BrainFANS: Standardized ATAC-seq environment and tooling across systems, improved QC/peak calling robustness, and enhanced onboarding documentation. These changes reduce environment drift, fix critical Picard-related issues, improve data quality and reliability, and accelerate reproducible analyses across platforms.
October 2024
5 Commits • 2 Features
Oct 1, 2024October 2024 performance for ejh243/BrainFANS: Delivered reproducibility-focused ATAC-seq workflow enhancements and essential codebase cleanup. Key features include standardized Conda environments and dependency management; updated installation/configuration steps with MACS3 compatibility; added sample selection and peak counting scripts; and README/documentation updates. Major cleanup involved removing deprecated ATAC-seq scripts and outdated job submission components to simplify workflows and reduce maintenance burden. Overall impact: more reliable analyses, smoother onboarding for new contributors, and reduced technical debt. Technologies demonstrated: Conda environment management, workflow standardization, Bash/script cleanup, documentation improvement, and MACS3 integration.
5 Commits • 2 Features
Oct 1, 2024October 2024 performance for ejh243/BrainFANS: Delivered reproducibility-focused ATAC-seq workflow enhancements and essential codebase cleanup. Key features include standardized Conda environments and dependency management; updated installation/configuration steps with MACS3 compatibility; added sample selection and peak counting scripts; and README/documentation updates. Major cleanup involved removing deprecated ATAC-seq scripts and outdated job submission components to simplify workflows and reduce maintenance burden. Overall impact: more reliable analyses, smoother onboarding for new contributors, and reduced technical debt. Technologies demonstrated: Conda environment management, workflow standardization, Bash/script cleanup, documentation improvement, and MACS3 integration.
October 2024
Activity
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Quality Metrics
Correctness87.6%
Maintainability88.4%
Architecture86.4%
Performance77.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashMarkdownPythonRShellYAML
Technical Skills
BioinformaticsConda Environment ManagementData AnalysisData VisualizationDocumentationEnvironment ManagementPipeline ConfigurationPipeline DevelopmentPipeline ManagementPython ScriptingQuality ControlRR MarkdownR ProgrammingR Scripting
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