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by claude@2026-06, 2026-06-24
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This paper describes LAMPrEY, an open-source Python-based automated quality control pipeline for large-scale quantitative proteomics datasets, including GUI-based file submission, automated processing using MaxQuant and RawTools, and an interactive analytics dashboard with an API for reproducible, programmatic analysis. The authors demonstrate the pipeline’s longitudinal monitoring and analytics capabilities using TMT11 quantitative proteomics data from 910 Enterococcus faecium isolates collected from bloodstream infection patients. A key limitation stated in the paper is not to provide competing proteins/biological findings beyond showcasing dataset monitoring and interpretation for this specific proteomics workflow. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
ABSTRACT Over the past years, proteomics has moved increasingly towards the analysis of large cohorts of biological specimens. This has been made possible by significant improvements in mass spectrometry technology, chromatographic separation methods, and improved data acquisition strategies. These technological advances now routinely enable experiments that yield vast datasets that substantially outstrip the capacity of existing proteomics data analysis approaches. Processing such large datasets requires purpose-built, quality control tools designed to organize and analyze the data while recording all processing parameters for reproducibility. To address this need, we developed an open-source, Python-based software platform, L arge-scale A utomated M ulti-level Pr oteomics E valuation by P y thon (LAMPrEY), a comprehensive quality-control pipeline for quantitative proteomics analyses of large cohorts of samples. LAMPrEY features GUI-based file submission, automated processing with MaxQuant and RawTools, an interactive analytics dashboard, and an application programming interface (API) for programmatic usage that collectively enable rapid, reproducible analysis and interpretation of proteomics data. We demonstrate the longitudinal monitoring and analytical capabilities of LAMPrEY using TMT11 quantitative proteomics data generated from 910 Enterococcus faecium isolates collected from bloodstream infection patients. LAMPrEY is an open-source software that can be accessed at www.lewisresearchgroup.org/software .
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ABSTRACT
Over the past years, proteomics has moved increasingly towards the analysis of large cohorts of biological specimens. This has been made possible by significant improvements in mass spectrometry technology, chromatographic separation methods, and improved data acquisition strategies. These technological advances now routinely enable experiments that yield vast datasets that substantially outstrip the capacity of existing proteomics data analysis approaches. Processing such large datasets requires purpose-built, quality control tools designed to organize and analyze the data while recording all processing parameters for reproducibility. To address this need, we developed an open-source, Python-based software platform, Large-scale Automated Multi-level Proteomics Evaluation by Python (LAMPrEY), a comprehensive quality-control pipeline for quantitative proteomics analyses of large cohorts of samples. LAMPrEY features GUI-based file submission, automated processing with MaxQuant and RawTools, an interactive analytics dashboard, and an application programming interface (API) for programmatic usage that collectively enable rapid, reproducible analysis and interpretation of proteomics data. We demonstrate the longitudinal monitoring and analytical capabilities of LAMPrEY using TMT11 quantitative proteomics data generated from 910 Enterococcus faecium isolates collected from bloodstream infection patients. LAMPrEY is an open-source software that can be accessed at www.lewisresearchgroup.org/software.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The figures included in the text have been uploaded as high-resolution figures. The TOC figure has been updated. Improvement of the Supplementary Information format.
ABBREVIATIONS
- API
- application programming interface
- TMT
- tandem mass tag
- QC
- quality control
- LC
- liquid chromatography
- MS
- mass spectrometry
- SHAP
- shapley additive explanations.
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