2011
DOI: 10.1021/ac200536b
|View full text |Cite
|
Sign up to set email alerts
|

Deconvolution of Two-Dimensional NMR Spectra by Fast Maximum Likelihood Reconstruction: Application to Quantitative Metabolomics

Abstract: We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D–2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demon… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
106
0
1

Year Published

2011
2011
2024
2024

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 86 publications
(107 citation statements)
references
References 21 publications
0
106
0
1
Order By: Relevance
“…Salt was added to the samples used for the H/D exchange measurements because it stabilizes the S state of apo-IscU and made it easier to measure the exchange rates. We used fast maximum-likelihood reconstruction as implemented in the Newton software package (37) in analyzing the hydrogen exchange and 2D NMR exchange data. More experimental details are provided in SI Methods.…”
Section: Methodsmentioning
confidence: 99%
“…Salt was added to the samples used for the H/D exchange measurements because it stabilizes the S state of apo-IscU and made it easier to measure the exchange rates. We used fast maximum-likelihood reconstruction as implemented in the Newton software package (37) in analyzing the hydrogen exchange and 2D NMR exchange data. More experimental details are provided in SI Methods.…”
Section: Methodsmentioning
confidence: 99%
“…The quantification portion of targeted profiling consists of measurement of the intensity of each signal and its profile across the ensemble. Although the "identify" task is usually thought of as a prerequisite to the "quantify" task, a modeling approach such as deconvolution [17][18][19] may quantify signals and modulations of their intensity prior to the signals being assigned to an NMR transition. Fig.…”
Section: Metabolomics Analysis Softwarementioning
confidence: 99%
“…As with ROI-based integrals, ROIbased amplitudes are much more useful for 2D than 1D analysis of complex biological mixtures. The Newton software package for fast maximum likelihood reconstruction of 1D and 2D NMR spectra [19], for example (Fig. 4), makes extensive use of ROI-based amplitudes in performing quantitative analysis of "time-zero" extrapolated 2D HSQC spectra [26].…”
Section: Feature Generationmentioning
confidence: 99%
See 1 more Smart Citation
“…Two important aspects of spectral deconvolution [29][30][31][32][33] are estimation of the number of signals and proper model function to describe the lineshape. There is overfitting risk to allow a deconvolution algorithm to determine the number of peaks and carry out the fit because increasing the number of peaks will always appear to perfectly fit any spectrum.…”
Section: Nmr Signal Deconvolutionmentioning
confidence: 99%