Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant
22 de junio de 2023
Te invitamos a leer el artículo "Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant" publicado en Rapid Communications in Mass Spectrometry, a cargo del profesor investigador Dr. Robert Winkler y su equipo de trabajo de la UGA-Langebio.
Autores: Héctor Guillén-Alonso, Nancy Shyrley García-Rojas, Robert Winkler
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, 36824 Irapuato, Guanajuato, Mexico.
- Department of Biochemical Engineering, National Technological Institute, Celaya, Mexico
Felicitamos al estudiantado y profesorado que contribuyeron en esta investigación por su arduo trabajo.
Abstract:
Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation.
We developed an interactive R script for guided mass spectrometry (MS) data processing. The “MQ_Assistant” is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and preview the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst.
Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of three biological replicates of a plant–microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data.
The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The stepwise operation with visual feedback also suggests the use of the MQ_Assistant in education.
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