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CiteWeb id: 19990000021

CiteWeb score: 9510

Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

The publication "Probability-based protein identification by searching sequence databases using mass spectrometry data" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Chemistry it is included to the Top 100. Additionally, the publicaiton "Probability-based protein identification by searching sequence databases using mass spectrometry data" is placed in the Top 100 among other scientific works published in 1999.
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