title: Chemical rule-based filtering of MS/MS spectra.
abstract: BACKGROUND
  Identification of proteins by mass spectrometry-based proteomics requires automated interpretation of peptide tandem mass spectrometry spectra. The effectiveness of peptide identification can be greatly improved by filtering out extraneous noise peaks before the subsequent database searching steps.
  
  
  RESULTS
  Here we present a novel chemical rule-based filtering algorithm, termed CRF, which makes use of the predictable patterns (rules) of collision-induced peptide fragmentation. The algorithm selects peak pairs that obey the common fragmentation rules within plausible limits of mass tolerance as well as peak intensity and produces spectra that can be subsequently submitted to any search engine. CRF increases the positive predictive value and decreases the number of random matches and thus improves performance by 15-20% in terms of peptide annotation using search engines, such as X!Tandem. Importantly, the algorithm also achieves data compression rates of ∼75%.
  
  
  BACKGROUND
  The MATLAB source code and a web server are available at http://hydrax.icgeb.trieste.it/CRFilter/.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, 34149 Trieste, Italy.
authors: Beáta
authors: B
authors: Reiz
authors: Attila
authors: A
authors: Kertész-Farkas
authors: Sándor
authors: S
authors: Pongor
authors: Michael P
authors: MP
authors: Myers
mesh_terms: D000465:Algorithms; D006801:Humans; D010455:Peptides / Q000032:analysis / Q000737:chemistry; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods*
chemical_list: D010455:Peptides; D011506:Proteins