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MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

https://doi.org/10.1016/j.mcpro.2022.100477

Kohler, Devon; Tsai, Tsung-Heng; Verschueren, Erik; Huang, Ting; Hinkle, Trent; Phu, Lilian; Choi, Meena; Vitek, Olga (January 2023, Molecular & Cellular Proteomics)

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MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

https://doi.org/10.1074/mcp.RA120.002105

Huang, Ting; Choi, Meena; Tzouros, Manuel; Golling, Sabrina; Pandya, Nikhil Janak; Banfai, Balazs; Dunkley, Tom; Vitek, Olga (October 2020, Molecular & Cellular Proteomics)

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MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

https://doi.org/10.1038/s41592-020-0955-0

Choi, Meena; Carver, Jeremy; Chiva, Cristina; Tzouros, Manuel; Huang, Ting; Tsai, Tsung-Heng; Pullman, Benjamin; Bernhardt, Oliver M.; Hüttenhain, Ruth; Teo, Guo Ci; et al (October 2020, Nature Methods)
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A proteomics sample metadata representation for multiomics integration and big data analysis

https://doi.org/10.1038/s41467-021-26111-3

Dai, Chengxin; Füllgrabe, Anja; Pfeuffer, Julianus; Solovyeva, Elizaveta M.; Deng, Jingwen; Moreno, Pablo; Kamatchinathan, Selvakumar; Kundu, Deepti Jaiswal; George, Nancy; Fexova, Silvie; et al (December 2021, Nature Communications)

Abstract The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.
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