Retention Time Standardization and Registration (RTStaR): An algorithm that matches corresponding and identifies unique species in nanoliquid chromatography-nanoelectrospray ionization-mass spectrometry lipidomic datasets
Blanchard, A. P.; Wang, Y.; Taylor, G. P.; Granger, M. W.; Fai, S.; Figeys, D.; Paus, T.; Pausova, Z.; Xu, H.; Bennett, S. A. L.
Show abstract
Bioinformatic tools capable of registering, rapidly and reproducibly, large numbers of nanoliquid chromatography-nanoelectrospray ionization-tandem mass spectrometry (nLC-nESI-MS/MS) lipidomic datasets are lacking. We provide here a freely available Retention Time Standardization and Registration (RTStaR) algorithm that aligns nLC-nESI-MS/MS spectra within a single dataset and compares these aligned retention times across multiple datasets. This two-step calibration matches corresponding and identifies unique lipid species in different lipidomes from different matrices and organisms. RTStaR was developed using a population-based study of 1001 human serum samples composed of 71 distinct glycerophosphocholine metabolites comprising a total of 68,572 analytes. Platform and matrix independence were validated using different MS instruments, nLC methodologies, and mammalian lipidomes. The complete algorithm is packaged in two modular ExcelTM workbook templates for easy implementation. RTStaR is freely available from the India Taylor Lipidomics Research Platform http://www.neurolipidomics.ca/rtstar/rtstar.html. Technical support is provided through ldomic@uottawa.ca
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry Based Workflow 96%
- rtmsEcho: An Open-Source R Package for Automated Analysis of Acoustic Ejection Mass Spectrometry Data 96%
- AutoTuner: High fidelity, robust, and rapid parameter selection for metabolomics data processing 96%
Similar papers in this journal
- Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments 98%
- Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls 95%
- MS2Lipid: a lipid subclass prediction program using machine learning and curated tandem mass spectral data 95%
Similar papers in this journal
- Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing 96%
- Hybrid Quadrupole Mass Filter Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics 96%
- Quantitative analysis of in vivo methionine oxidation of the human proteome 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.