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Bruker MALDI-TOF bacteria species identification scoring algorithm
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+ Bacteria
+ Bioinformatics
+ Biochemistry
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Karen L. Pringle
Bruker MALDI-TOF bacteria species identification scoring algorithm
For identification scoring, the software correlates signal
intensities of matched signals of mass spectra. The three scores
obtained from such a procedure are multiplied and normalized to a
value of 1,000 and its common logarithm. Log scores over 2 are
considered as reliable identification of a bacterial species (Table
4), whereas log scores over 1.7 generally indicate reliable
identification of bacterial genera. Log scores of 3 are obtained when
spectra are matched with themselves.
Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., & Geider, K. (2008). Classification and identification of bacteria by mass spectrometry and
computational analysis. PloS one, 3(7), e2843.
Going further, from a random thread on ResearchGate:
Number of peaks matched in your unknown sample divided by the number
of peaks in the unknown sample, this is given a score (out of 10)
Number of peaks matched in your unknown sample divided by the number
of peaks in the reference sample, this is given a score (out of 10)
Score of relative intensities of matching peaks (out of 10)
Total score out of 1000
Log10 score out of 3
There is no relationship of the score with a p-value nor you can
calculate the statistics because the bruker database is not open and
you can not know the values for what the score was calculated to do
the statistical analysis
For identification scoring, the software correlates signalintensities of matched signals of mass spectra. The three scoresobtained from such a procedure are multiplied and normalized to avalue of 1,000 and its common logarithm. Log scores over 2 areconsidered as reliable identification of a bacterial species (Table4), whereas log scores over 1.7 generally indicate reliableidentification of bacterial genera. Log scores of 3 are obtained whenspectra are matched with themselves.
Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., & Geider, K. (2008). Classification and identification of bacteria by mass spectrometry andcomputational analysis. PloS one, 3(7), e2843.
Going further, from a random thread on ResearchGate:
Number of peaks matched in your unknown sample divided by the numberof peaks in the unknown sample, this is given a score (out of 10)
Number of peaks matched in your unknown sample divided by the numberof peaks in the reference sample, this is given a score (out of 10)
Score of relative intensities of matching peaks (out of 10)
Total score out of 1000
Log10 score out of 3
There is no relationship of the score with a p-value nor you cancalculate the statistics because the bruker database is not open andyou can not know the values for what the score was calculated to dothe statistical analysis
Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., & Geider, K. (2008). Classification and identification of bacteria by mass spectrometry and computational analysis. PloS one, 3(7), e2843.
Going further, from a random thread on ResearchGate:
https://www.researchgate.net/post/Does_anyone_know_how_is_the_p-value_calculated_by_Bruker_Biotyper_during_MALDI-MS_fingerprinting_of_bacteria
Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., & Geider, K. (2008). Classification and identification of bacteria by mass spectrometry andcomputational analysis. PloS one, 3(7), e2843.
Going further, from a random thread on ResearchGate:
https://www.researchgate.net/post/Does_anyone_know_how_is_the_p-value_calculated_by_Bruker_Biotyper_during_MALDI-MS_fingerprinting_of_bacteria
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