EBI-Metagenomics/nf-modules
Dec 2025 – Feb 2026
3 Months active
Languages Used
GroovyNextflowYAMLPython
Technical Skills
Nextflowbioinformaticsdata integrationdata processingsoftware testingworkflow automation
Alejandra Escobar
PROFILE
Alejandra Escobar
Ales developed and enhanced metagenomics workflows in the EBI-Metagenomics/nf-modules repository, focusing on automated antimicrobial resistance annotation, virulence factor prediction, and toxin detection. Using Nextflow and Python, Ales unified outputs from multiple bioinformatics tools into standardized GFF formats, streamlining downstream analysis and reporting. The work included integrating PathoFact2 for protein sequence analysis, implementing machine learning–based toxin prediction, and automating data acquisition from sources like Zenodo. Emphasis on robust testing, error handling, and reproducibility ensured high integration quality. Over three months, Ales delivered modular, maintainable workflow components that reduced manual intervention and improved the reliability of pathogen detection pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
5
Lines of code
5,646
Activity Months3
Your Network
33 people
Work History
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a unified virulence factor and toxin prediction workflow within the EBI-Metagenomics nf-modules, integrating Pathofact2 into a single GFF output and adding a protein-sequence–driven subworkflow to enhance virulence factor/toxin predictions. This work standardizes downstream annotation, reduces manual rework, and accelerates pathogen detection in metagenomic analyses. No major bugs fixed captured for this repo this month.
2 Commits • 1 Features
Feb 1, 2026February 2026: Delivered a unified virulence factor and toxin prediction workflow within the EBI-Metagenomics nf-modules, integrating Pathofact2 into a single GFF output and adding a protein-sequence–driven subworkflow to enhance virulence factor/toxin predictions. This work standardizes downstream annotation, reduces manual rework, and accelerates pathogen detection in metagenomic analyses. No major bugs fixed captured for this repo this month.
February 2026
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for EBI-Metagenomics/nf-modules focusing on delivering automated data acquisition, virulence factor analysis, and toxin prediction capabilities with robust testing and reproducibility.
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for EBI-Metagenomics/nf-modules focusing on delivering automated data acquisition, virulence factor analysis, and toxin prediction capabilities with robust testing and reproducibility.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025: Delivered key AMR workflow enhancements in EBI-Metagenomics/nf-modules. Implemented a dedicated AMR annotation subworkflow, unified outputs from multiple tools into a standardized GFF format, and added a streamlined DeepARG database downloader to improve reliability and efficiency. These changes reduce manual steps, improve reproducibility, and enable faster, more accurate antimicrobial resistance analyses within metagenomics pipelines. The work reinforces tight coupling between workflow automation and data standardization, boosting downstream reporting, collaboration, and business value.
2 Commits • 1 Features
Dec 1, 2025December 2025: Delivered key AMR workflow enhancements in EBI-Metagenomics/nf-modules. Implemented a dedicated AMR annotation subworkflow, unified outputs from multiple tools into a standardized GFF format, and added a streamlined DeepARG database downloader to improve reliability and efficiency. These changes reduce manual steps, improve reproducibility, and enable faster, more accurate antimicrobial resistance analyses within metagenomics pipelines. The work reinforces tight coupling between workflow automation and data standardization, boosting downstream reporting, collaboration, and business value.
December 2025
Activity
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Quality Metrics
Correctness87.6%
Maintainability80.0%
Architecture85.0%
Performance82.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
GroovyNextflowPythonYAML
Technical Skills
NextflowPython scriptingbioinformaticsdata analysisdata downloadingdata integrationdata parsingdata processingdata sciencemachine learningsoftware testingworkflow automationworkflow developmentworkflow management
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