Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass spectrum/peptide sequence false match frequencies.
BACKGROUND
In proteomics, reverse database searching is used to control the false match frequency for tandem mass spectrum/peptide sequence matches, but reversal creates sequences devoid of patterns that usually challenge database-search software.


RESULTS
We designed an unsupervised pattern recognition algorithm for detecting patterns with various lengths from large sequence datasets. The patterns found in a protein sequence database were used to create decoy databases using a Monte Carlo sampling algorithm. Searching these decoy databases led to the prediction of false positive rates for spectrum/peptide sequence matches. We show examples where this method, independent of instrumentation, database-search software and samples, provides better estimation of false positive identification rates than a prevailing reverse database searching method. The pattern detection algorithm can also be used to analyze sequences for other purposes in biology or cryptology.


BACKGROUND
On request from the authors.


BACKGROUND
http://bioinformatics.psb.ugent.be/.
Feng
Jian
J
Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA.
Naiman
Daniel Q
DQ
Cooper
Bret
B
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated / Q000379:methods*; D010449:Peptide Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins