PlasmoGenEpi/PGEcore
Feb 2026 – Mar 2026
2 Months active
Languages Used
R
Technical Skills
R programmingbioinformaticsdata analysisdata manipulationgenetic sequencingscript optimization
Nicholas Hathaway
PROFILE
Nicholas Hathaway
Worked on the PlasmoGenEpi/PGEcore repository to enhance bioinformatics workflows for genetic sequencing analysis. Delivered new features in R that expanded allele frequency estimation, including multilocus and single-allele calculations with weighted and presence/absence modes. Improved data structures by adding amino acid translation outputs and streamlined data accessibility through TSV exports. Addressed translation accuracy for non-standard start codons and resolved script warnings, library dependencies, and argument parsing issues. Refactored scripts for maintainability, enforced stricter input validation, and enabled MCMC results export for quality assurance. Maintained a clean, reliable codebase by removing obsolete files and updating workflow documentation for reproducibility.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
4
Lines of code
1,346
Activity Months2
Your Network
3 peopleShared Repositories
3
Work History
March 2026
2 Commits • 2 Features
Mar 1, 2026Monthly summary for 2026-03: Key features delivered and codebase optimizations in PlasmoGenEpi/PGEcore focused on reliability, debugging support, and repository cleanliness. Implemented MCMC results export option and stricter input validation to improve QA visibility and data integrity; reorganized scripts for easier reuse; removed obsolete example files to reduce clutter. No critical bugs reported; overall stability improved due to validation and cleanup. Business impact includes enhanced traceability for MCMC runs, faster QA cycles, and a cleaner, more maintainable codebase.
2 Commits • 2 Features
Mar 1, 2026Monthly summary for 2026-03: Key features delivered and codebase optimizations in PlasmoGenEpi/PGEcore focused on reliability, debugging support, and repository cleanliness. Implemented MCMC results export option and stricter input validation to improve QA visibility and data integrity; reorganized scripts for easier reuse; removed obsolete example files to reduce clutter. No critical bugs reported; overall stability improved due to validation and cleanup. Business impact includes enhanced traceability for MCMC runs, faster QA cycles, and a cleaner, more maintainable codebase.
March 2026
February 2026
9 Commits • 2 Features
Feb 1, 2026February 2026 highlights for PlasmoGenEpi/PGEcore focused on delivering robust data processing, expanding allele frequency analysis, and stabilizing the workflow for reliable downstream analyses. Key features delivered: - Data structure enhancements and loci outputs: added aa_locus column for amino acid translations, introduced a new TSV to write loci of interest, and fixed data table references to improve multilocus frequency processing and data accessibility. - Advanced allele frequency estimation: released a new script to calculate single-allele frequencies and a multilocus estimator with options for weighted sample allele frequency or presence/absence, broadening analysis capabilities. Major bugs fixed: - Bug fixes and usability/documentation improvements: silenced R script warnings, updated workflow documentation for SLAF/Microhaplotype analyses, added missing library support, and corrected joins and argument parsing to improve reliability. - Translation accuracy fix for non-standard start codons: ensured correct amino acid translation by enabling no.init.codon = TRUE, preventing Leucine from being mis-translated as Methionine. Overall impact and accomplishments: - Expanded analytic capabilities with more accurate multilocus frequency estimates, improved data accessibility, and greater pipeline reliability, accelerating end-to-end analysis and decision making. - Strengthened reproducibility through updated docs and clearer data outputs, easing onboarding and collaboration. Technologies/skills demonstrated: - R scripting, data wrangling, TSV I/O, inner joins, and robust argument handling; workflow documentation; versioned commits across feature, bug, and reliability improvements.
February 2026
9 Commits • 2 Features
Feb 1, 2026February 2026 highlights for PlasmoGenEpi/PGEcore focused on delivering robust data processing, expanding allele frequency analysis, and stabilizing the workflow for reliable downstream analyses. Key features delivered: - Data structure enhancements and loci outputs: added aa_locus column for amino acid translations, introduced a new TSV to write loci of interest, and fixed data table references to improve multilocus frequency processing and data accessibility. - Advanced allele frequency estimation: released a new script to calculate single-allele frequencies and a multilocus estimator with options for weighted sample allele frequency or presence/absence, broadening analysis capabilities. Major bugs fixed: - Bug fixes and usability/documentation improvements: silenced R script warnings, updated workflow documentation for SLAF/Microhaplotype analyses, added missing library support, and corrected joins and argument parsing to improve reliability. - Translation accuracy fix for non-standard start codons: ensured correct amino acid translation by enabling no.init.codon = TRUE, preventing Leucine from being mis-translated as Methionine. Overall impact and accomplishments: - Expanded analytic capabilities with more accurate multilocus frequency estimates, improved data accessibility, and greater pipeline reliability, accelerating end-to-end analysis and decision making. - Strengthened reproducibility through updated docs and clearer data outputs, easing onboarding and collaboration. Technologies/skills demonstrated: - R scripting, data wrangling, TSV I/O, inner joins, and robust argument handling; workflow documentation; versioned commits across feature, bug, and reliability improvements.
Activity
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Quality Metrics
Correctness89.0%
Maintainability85.4%
Architecture85.4%
Performance85.4%
AI Usage23.6%
Skills & Technologies
Programming Languages
R
Technical Skills
R programmingbioinformaticsdata analysisdata manipulationgenetic sequencingscript optimizationscriptingstatistical modeling
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