MassIVE.quant
MassIVE.quant provides a repository for deposition and reproducible reanalysis of quantitative mass spectrometry-based proteomics data by organizing raw spectra, experimental metadata, analysis scripts, intermediate input and output files, and alternative reanalyses.
Key Features:
- Compatibility and Integration: Compatible with a wide range of mass spectrometry data acquisition methods and with multiple computational analysis tools.
- Branch Structure Organization: Employs a branch structure that organizes raw experimental data alongside comprehensive experimental-design metadata, analysis scripts, intermediate input and output files, and alternative reanalyses.
- Reproducibility and Data Sharing: Stores raw data and associated analytical processes to enable reproducible quantitative mass spectrometry-based proteomics experiments and facilitate validation.
Scientific Applications:
- Comparative Proteomics: Enables comparison of proteomic measurements across different experimental conditions or biological samples.
- Data Reanalysis and Validation: Supports independent reanalysis, validation, and verification by providing raw data and analysis scripts.
- Collaborative Research: Facilitates data sharing to support collaboration among researchers working on related proteomic projects or methodologies.
Methodology:
Organizes datasets using a branch structure that links raw spectral data with experimental-design metadata, quantitative analysis scripts, intermediate input/output files, and alternative reanalyses.
Topics
Details
- Tool Type:
- api, web application
- Added:
- 1/18/2021
- Last Updated:
- 2/20/2021
Operations
Publications
Choi M, Carver J, Chiva C, Tzouros M, Huang T, Tsai T, Pullman B, Bernhardt OM, Hüttenhain R, Teo GC, Perez-Riverol Y, Muntel J, Müller M, Goetze S, Pavlou M, Verschueren E, Wollscheid B, Nesvizhskii AI, Reiter L, Dunkley T, Sabidó E, Bandeira N, Vitek O. MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets. Nature Methods. 2020;17(10):981-984. doi:10.1038/s41592-020-0955-0. PMID:32929271. PMCID:PMC7541731.