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. 2008 Jan;7(1):113-22.
doi: 10.1021/pr070361e. Epub 2007 Dec 8.

Clustering millions of tandem mass spectra

Ari M Frank  1 , Nuno Bandeira, Zhouxin Shen, Stephen Tanner, Steven P Briggs, Richard D Smith, Pavel A Pevzner
Affiliations

Clustering millions of tandem mass spectra

Ari M Frank et al. J Proteome Res. 2008 Jan.

Abstract

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.

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Figures

Figure 1
Figure 1
A pseudocode description of the approximate hierarchical clustering algorithm used by MS-Clustering.
Figure 2
Figure 2
Illustration of cluster appending. The set Clusters is a linked list where each element is a list of spectra. When the algorithm merges cluster c with a preceding cluster c′ it appends the list of spectra in cluster c to the list of spectra in cluster c′ and then removes the entry for c from the linked list of clusters.
Figure 3
Figure 3
Example of cluster for the peptide TGSVDIIVTDLPFGK. A cluster of three spectra is shown along with the consensus spectrum that was created from them. For each spectrum the InsPecT score is shown, along with the number of identified b/y-ions and the percentage of the spectrum’s intensity that is explained by the peptide’s fragment ions. Only the consensus spectrum had a suffciently high score to be positively identified in the database search using InsPecT. All spectra have a precursor charge 2 with precursor m/z errors below 1 Da. The figures’ x-axes represents the fragments’ m/z values and the y-axes represents the intensities.
Figure 4
Figure 4
Fragmented clusters. Spectra of the peptide VDDPNAEDKR from two clusters that were not joined are shown (the figure contains 3 spectra from each cluster, originally cluster I contained 6 spectra and cluster II contained 4 spectra). The figures’ x-axes represents the fragments’ m/z values and the y-axes represents the intensities.
See this image and copyright information in PMC

References

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