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MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids.

Xia J, Bjorndahl TC, Tang P, Wishart DS - BMC Bioinformatics (2008)

Bottom Line: However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies.MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra.Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca

ABSTRACT

Background: One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies.

Results: We describe a standalone graphics software tool, called MetaboMiner, which can be used to automatically or semi-automatically identify metabolites in complex biofluids from 2D NMR spectra. MetaboMiner is able to handle both 1H-1H total correlation spectroscopy (TOCSY) and 1H-13C heteronuclear single quantum correlation (HSQC) data. It identifies compounds by comparing 2D spectral patterns in the NMR spectrum of the biofluid mixture with specially constructed libraries containing reference spectra of approximately 500 pure compounds. Tests using a variety of synthetic and real spectra of compound mixtures showed that MetaboMiner is able to identify >80% of detectable metabolites from good quality NMR spectra.

Conclusion: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra. Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.

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Comparative performance of different search strategies. Synthetic mixture query spectra were generated by pooling the peaks of 50 randomly selected compounds from MetaboMiner's reference spectral library. Different levels of spectral noise were added to these peaks and then compounds were identified with (*) and without using the adaptive threshold method. The Figure 5A, the query peaks were deleted at random with 0%, 10%, 20%, 30%, 40% and 50% probabilities; Figure 5B, the query peaks were subject to five levels of random chemical shift variations (± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift). The F scores were averaged over 50 iterations. (Abbreviations: PM, percentage match method; MS, minimal signature method).
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Figure 5: Comparative performance of different search strategies. Synthetic mixture query spectra were generated by pooling the peaks of 50 randomly selected compounds from MetaboMiner's reference spectral library. Different levels of spectral noise were added to these peaks and then compounds were identified with (*) and without using the adaptive threshold method. The Figure 5A, the query peaks were deleted at random with 0%, 10%, 20%, 30%, 40% and 50% probabilities; Figure 5B, the query peaks were subject to five levels of random chemical shift variations (± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift). The F scores were averaged over 50 iterations. (Abbreviations: PM, percentage match method; MS, minimal signature method).

Mentions: The performance of the minimal signature method and the adaptive threshold method were evaluated under two common types of spectral noise – missing peaks and "drifting" peaks (i.e. peaks that have drifted from their canonical positions due to temperature, pH or solvent effects). The missing peaks were simulated by deleting peaks of each compound at random with 0%, 10%, 20%, 30%, 40%, 50% probabilities. The chemical shift drift effects were simulated by adding random values of ± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift. The spectra of each synthetic query mixture were generated by first pooling the peaks from 50 compounds that were randomly selected from the MetaboMiner reference spectral library (162 compounds). After introducing this artificial spectral noise, the query mixtures were searched against the reference spectral library with and without using the adaptive threshold method. Two compound identification strategies were compared – the minimal signature method (MS) and the percentage match method (PM) with 75% as the cut-off value. The F-measure was used for performance evaluation, where F = 2 × (precision × recall)/(precision + recall) where recall is the proportion of true positives in the returned result (recall = TP/(TP+FN)) and precision is a measure of the percentage of positive or correct results (precision = TP/(TP+FP)). The values were obtained as the averages of TOCSY and HSQC search results over 50 iterations. Figure 5A summarizes MetaboMiner's performance using data with different fractions of missing peaks. Figure 5B shows the results using data with increasing chemical shift drift effects.


MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids.

Xia J, Bjorndahl TC, Tang P, Wishart DS - BMC Bioinformatics (2008)

Comparative performance of different search strategies. Synthetic mixture query spectra were generated by pooling the peaks of 50 randomly selected compounds from MetaboMiner's reference spectral library. Different levels of spectral noise were added to these peaks and then compounds were identified with (*) and without using the adaptive threshold method. The Figure 5A, the query peaks were deleted at random with 0%, 10%, 20%, 30%, 40% and 50% probabilities; Figure 5B, the query peaks were subject to five levels of random chemical shift variations (± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift). The F scores were averaged over 50 iterations. (Abbreviations: PM, percentage match method; MS, minimal signature method).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2612014&req=5

Figure 5: Comparative performance of different search strategies. Synthetic mixture query spectra were generated by pooling the peaks of 50 randomly selected compounds from MetaboMiner's reference spectral library. Different levels of spectral noise were added to these peaks and then compounds were identified with (*) and without using the adaptive threshold method. The Figure 5A, the query peaks were deleted at random with 0%, 10%, 20%, 30%, 40% and 50% probabilities; Figure 5B, the query peaks were subject to five levels of random chemical shift variations (± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift). The F scores were averaged over 50 iterations. (Abbreviations: PM, percentage match method; MS, minimal signature method).
Mentions: The performance of the minimal signature method and the adaptive threshold method were evaluated under two common types of spectral noise – missing peaks and "drifting" peaks (i.e. peaks that have drifted from their canonical positions due to temperature, pH or solvent effects). The missing peaks were simulated by deleting peaks of each compound at random with 0%, 10%, 20%, 30%, 40%, 50% probabilities. The chemical shift drift effects were simulated by adding random values of ± 0.01, ± 0.02, ± 0.03, ± 0.04, ± 0.05 ppm for each 1H chemical shift, and ± 0.05, ± 0.10, ± 0.15, ± 0.20, ± 0.25 ppm for each 13C chemical shift. The spectra of each synthetic query mixture were generated by first pooling the peaks from 50 compounds that were randomly selected from the MetaboMiner reference spectral library (162 compounds). After introducing this artificial spectral noise, the query mixtures were searched against the reference spectral library with and without using the adaptive threshold method. Two compound identification strategies were compared – the minimal signature method (MS) and the percentage match method (PM) with 75% as the cut-off value. The F-measure was used for performance evaluation, where F = 2 × (precision × recall)/(precision + recall) where recall is the proportion of true positives in the returned result (recall = TP/(TP+FN)) and precision is a measure of the percentage of positive or correct results (precision = TP/(TP+FP)). The values were obtained as the averages of TOCSY and HSQC search results over 50 iterations. Figure 5A summarizes MetaboMiner's performance using data with different fractions of missing peaks. Figure 5B shows the results using data with increasing chemical shift drift effects.

Bottom Line: However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies.MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra.Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca

ABSTRACT

Background: One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies.

Results: We describe a standalone graphics software tool, called MetaboMiner, which can be used to automatically or semi-automatically identify metabolites in complex biofluids from 2D NMR spectra. MetaboMiner is able to handle both 1H-1H total correlation spectroscopy (TOCSY) and 1H-13C heteronuclear single quantum correlation (HSQC) data. It identifies compounds by comparing 2D spectral patterns in the NMR spectrum of the biofluid mixture with specially constructed libraries containing reference spectra of approximately 500 pure compounds. Tests using a variety of synthetic and real spectra of compound mixtures showed that MetaboMiner is able to identify >80% of detectable metabolites from good quality NMR spectra.

Conclusion: MetaboMiner is a freely available, easy-to-use, NMR-based metabolomics tool that facilitates automatic peak processing, rapid compound identification, and facile spectrum annotation from either 2D TOCSY or HSQC spectra. Using comprehensive reference libraries coupled with robust algorithms for peak matching and compound identification, the program greatly simplifies the process of metabolite identification in complex 2D NMR spectra.

Show MeSH
Related in: MedlinePlus