Implementation and application of a versatile clustering tool for tandem mass spectrometry data.
High-throughput proteomics experiments typically generate large amounts of peptide fragmentation mass spectra during a single experiment. There is often a substantial amount of redundant fragmentation of the same precursors among these spectra, which is usually considered a nuisance. We here discuss the potential of clustering and merging redundant spectra to turn this redundancy into a useful property of the dataset. To this end, we have created the first general-purpose, freely available open-source software application for clustering and merging MS/MS spectra. The application also introduces a novel approach to calculating the similarity of fragmentation mass spectra that takes into account the increased precision of modern mass spectrometers, and we suggest a simple but effective improvement to single-linkage clustering. The application and the novel algorithms are applied to several real-life proteomic datasets and the results are discussed. An analysis of the influence of the different algorithms available and their parameters is given, as well as a number of important applications of the overall approach.
Flikka
Kristian
K
Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Bergen, Norway. flikka@ii.uib.no
Meukens
Jeroen
J
Helsens
Kenny
K
Vandekerckhove
Joël
J
Eidhammer
Ingvar
I
Gevaert
Kris
K
Martens
Lennart
L
D000465:Algorithms; D000595:Amino Acid Sequence; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D006801:Humans; D008969:Molecular Sequence Data; D040901:Proteomics; D053719:Tandem Mass Spectrometry / Q000379:methods*