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A Computational Framework for High-Throughput Isotopic Natural Abundance Correction of Omics-Level Ultra-High Resolution FT-MS Datasets

Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY 40536, USA
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Metabolites 2013, 3(4), 853-866; https://doi.org/10.3390/metabo3040853
Received: 21 July 2013 / Revised: 26 August 2013 / Accepted: 10 September 2013 / Published: 25 September 2013
(This article belongs to the Special Issue Data Processing in Metabolomics)
New metabolomics applications of ultra-high resolution and accuracy mass spectrometry can provide thousands of detectable isotopologues, with the number of potentially detectable isotopologues increasing exponentially with the number of stable isotopes used in newer isotope tracing methods like stable isotope-resolved metabolomics (SIRM) experiments. This huge increase in usable data requires software capable of correcting the large number of isotopologue peaks resulting from SIRM experiments in a timely manner. We describe the design of a new algorithm and software system capable of handling these high volumes of data, while including quality control methods for maintaining data quality. We validate this new algorithm against a previous single isotope correction algorithm in a two-step cross-validation. Next, we demonstrate the algorithm and correct for the effects of natural abundance for both 13C and 15N isotopes on a set of raw isotopologue intensities of UDP-N-acetyl-D-glucosamine derived from a 13C/15N-tracing experiment. Finally, we demonstrate the algorithm on a full omics-level dataset. View Full-Text
Keywords: stable isotope tracing; stable isotope-resolved metabolomics; Fourier transform mass spectrometry; multi-isotope natural abundance correction; analytical derivation; parallelization stable isotope tracing; stable isotope-resolved metabolomics; Fourier transform mass spectrometry; multi-isotope natural abundance correction; analytical derivation; parallelization
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Carreer, W.J.; Flight, R.M.; Moseley, H.N.B. A Computational Framework for High-Throughput Isotopic Natural Abundance Correction of Omics-Level Ultra-High Resolution FT-MS Datasets. Metabolites 2013, 3, 853-866.

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