| Title |
“Lossless” compression of high resolution mass spectra of small molecules
|
|---|---|
| Published in |
Metabolomics, March 2010
|
| DOI | 10.1007/s11306-010-0202-2 |
| Pubmed ID | |
| Authors |
Bo Blanckenburg, Yuri E. M. van der Burgt, André M. Deelder, Magnus Palmblad |
| Abstract |
Fourier transform ion cyclotron resonance (FTICR) provides the highest resolving power of any commercially available mass spectrometer. This advantage is most significant for species of low mass-to-charge ratio (m/z), such as metabolites. Unfortunately, FTICR spectra contain a very large number of data points, most of which are noise. This is most pronounced at the low m/z end of spectra, where data point density is the highest but peak density low. We therefore developed a filter that offers lossless compression of FTICR mass spectra from singly charged metabolites. The filter relies on the high resolving power and mass measurement precision of FTICR and removes only those m/z channels that cannot contain signal from singly charged organic species. The resulting pseudospectra still contain the same signal as the original spectra but less uninformative background. The filter does not affect the outcome of standard downstream chemometric analysis methods, such as principal component analysis, but use of the filter significantly reduces memory requirements and CPU time for such analyses. We demonstrate the utility of the filter for urinary metabolite profiling using direct infusion electrospray ionization and a 15 tesla FTICR mass spectrometer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0202-2) contains supplementary material, which is available to authorized users. |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Russia | 1 | 4% |
| China | 1 | 4% |
| South Africa | 1 | 4% |
| Unknown | 19 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 30% |
| Professor > Associate Professor | 4 | 17% |
| Student > Ph. D. Student | 3 | 13% |
| Other | 2 | 9% |
| Student > Bachelor | 2 | 9% |
| Other | 4 | 17% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 30% |
| Chemistry | 7 | 30% |
| Biochemistry, Genetics and Molecular Biology | 3 | 13% |
| Nursing and Health Professions | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 1 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2013.
All research outputs
#7,551,483
of 23,036,991 outputs
Outputs from Metabolomics
#470
of 1,300 outputs
Outputs of similar age
#34,691
of 94,026 outputs
Outputs of similar age from Metabolomics
#3
of 6 outputs
Altmetric has tracked 23,036,991 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,300 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 57% of its peers.
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We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.