RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.
Louise Ulrich KurtMilan Avila ClasenÍsis Venturi BiembengutMax RuwoltFan LiuFabio César GozzoDiogo Borges LimaPaulo Costa CarvalhoPublished in: Journal of proteome research (2024)
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.
Keyphrases
- mass spectrometry
- liquid chromatography
- ms ms
- high performance liquid chromatography
- gas chromatography
- quality control
- capillary electrophoresis
- electronic health record
- high resolution
- big data
- multiple sclerosis
- data analysis
- computed tomography
- high resolution mass spectrometry
- tandem mass spectrometry
- optical coherence tomography
- air pollution
- genome wide
- gene expression
- quality improvement
- machine learning
- high intensity
- network analysis
- quantum dots
- risk assessment
- magnetic resonance imaging
- dual energy