OpenMS/OpenMS
Apr 2026 – Apr 2026
1 Month active
Languages Used
C++
Technical Skills
Algorithm DesignC++Software Developmentalgorithm designbioinformaticsdata analysis
Timo Sachsenberg
PROFILE
Timo Sachsenberg
Worked on the OpenMS/OpenMS repository to enhance proteomics data analysis by implementing an optional post-link retention-time based outlier filter for match-between-runs transfers in the ProteomicsLFQ module. This feature used robust statistics, including median and MAD, to identify and remove unreliable anchors from ConsensusMap data, recalculating centroids to ensure accurate downstream feature sets. Developed in C++ with a focus on algorithm design and bioinformatics, the work maintained compatibility with existing pipelines and downstream tools. Additionally, reverted a flawed filter implementation to restore correct retention-time filtering, preserving data integrity and stability across quantification and identification workflows in proteomics analysis.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
1,674
Activity Months1
Your Network
38 peopleWork History
April 2026
2 Commits • 1 Features
Apr 1, 2026OpenMS OpenMS - April 2026 monthly summary: Key features delivered: - Implemented an optional post-link retention-time (RT) based outlier filter for match-between-runs (MBR) transfers in ProteomicsLFQ. The filter is applied to ConsensusMap data after linking and uses per-pair RT residual statistics to remove unreliable anchors, recomputing affected ConsensusFeature centroids so downstream consumers see accurate feature sets. This is exposed via the -MBRFilter:enabled flag and integrates with existing map_index annotations. Major bugs fixed: - Reverted a flawed MBRFilter implementation to restore correct RT-based filtering behavior, ensuring filtering decisions align with established RT statistics and per-pair distributions. This rollback preserves data integrity and compatibility with downstream tools. Overall impact and accomplishments: - Increased reliability and accuracy of MBR-driven transfers in proteomics workflows, improving the trustworthiness of downstream quantification (PeptideAndProteinQuant, MSstatsFile, mzTab export) and ID resolution. - Maintained compatibility with existing ConsensusMap processing, minimizing disruption to current pipelines while delivering robust filtering. Technologies/skills demonstrated: - C++ OpenMS development, ProteomicsLFQ module extension, and robust statistics (median, MAD) for per-pair RT residuals. - Data model proficiency with ConsensusMap, map_index, and consensus feature recomputation; integration points with downstream exporters and analyzers. - Collaboration and code hygiene evidenced by structured feature introduction and rollback when necessary, ensuring stability.
2 Commits • 1 Features
Apr 1, 2026OpenMS OpenMS - April 2026 monthly summary: Key features delivered: - Implemented an optional post-link retention-time (RT) based outlier filter for match-between-runs (MBR) transfers in ProteomicsLFQ. The filter is applied to ConsensusMap data after linking and uses per-pair RT residual statistics to remove unreliable anchors, recomputing affected ConsensusFeature centroids so downstream consumers see accurate feature sets. This is exposed via the -MBRFilter:enabled flag and integrates with existing map_index annotations. Major bugs fixed: - Reverted a flawed MBRFilter implementation to restore correct RT-based filtering behavior, ensuring filtering decisions align with established RT statistics and per-pair distributions. This rollback preserves data integrity and compatibility with downstream tools. Overall impact and accomplishments: - Increased reliability and accuracy of MBR-driven transfers in proteomics workflows, improving the trustworthiness of downstream quantification (PeptideAndProteinQuant, MSstatsFile, mzTab export) and ID resolution. - Maintained compatibility with existing ConsensusMap processing, minimizing disruption to current pipelines while delivering robust filtering. Technologies/skills demonstrated: - C++ OpenMS development, ProteomicsLFQ module extension, and robust statistics (median, MAD) for per-pair RT residuals. - Data model proficiency with ConsensusMap, map_index, and consensus feature recomputation; integration points with downstream exporters and analyzers. - Collaboration and code hygiene evidenced by structured feature introduction and rollback when necessary, ensuring stability.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Algorithm DesignC++Software Developmentalgorithm designbioinformaticsdata analysis
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