Improving automatic peptide mass fingerprint protein identification by combining many peak sets.
An automated peak picking strategy is presented where several peak sets with different signal-to-noise levels are combined to form a more reliable statement on the protein identity. The strategy is compared against both manual peak picking and industry standard automated peak picking on a set of mass spectra obtained after tryptic in gel digestion of 2D-gel samples from human fetal fibroblasts. The set of spectra contain samples ranging from strong to weak spectra, and the proposed multiple-scale method is shown to be much better on weak spectra than the industry standard method and a human operator, and equal in performance to these on strong and medium strong spectra. It is also demonstrated that peak sets selected by a human operator display a considerable variability and that it is impossible to speak of a single "true" peak set for a given spectrum. The described multiple-scale strategy both avoids time-consuming parameter tuning and exceeds the human operator in protein identification efficiency. The strategy therefore promises reliable automated user-independent protein identification using peptide mass fingerprints.
Rögnvaldsson
Thorsteinn
T
School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, SE-301 18 Halmstad, Sweden. denni@ide.hh.se
Häkkinen
Jari
J
Lindberg
Claes
C
Marko-Varga
György
G
Potthast
Frank
F
Samuelsson
Jim
J
D002460:Cell Line; D015180:Electrophoresis, Gel, Two-Dimensional; D006801:Humans; D008970:Molecular Weight; D010449:Peptide Mapping; D010455:Peptides / Q000737:chemistry*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
D010455:Peptides