ArticleJournal of proteome research2024
RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.
Article in Journal of proteome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Quinoa bioester application shifts human skin proteome toward molecular profiles associated with younger age.Communications biology · 2026Observational
- Breaking barriers in crosslinking mass spectrometry with enhanced throughput and sensitivity using Orbitrap Astral.Nature communications · 2025Article
- Focal adhesion in the tumour metastasis: from molecular mechanisms to therapeutic targets.Biomarker research · 2025Review
- A dataset for developing proteomic tools for pathogen detection via differential cell lysis of whole blood samples.Scientific data · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.
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