Peptide Sequence Tags for Fast Database Search in Mass-Spectrometry. Frank, A., Tanner, S., & Pevzner, P. In Proc. of Research in Computational Molecular Biology (RECOMB 2005), volume 3500, of Lect Notes Comput Sci, pages 326–341, 2005.
abstract   bibtex   
Filtration techniques, in the form of rapid elimination of candidate sequences while retaining the true one, are key ingredients of database searches in genomics. Although SEQUEST and Mascot are sometimes referred to as BLAST for mass-spectrometry, the key algorithmic idea of BLAST (filtration) was never implemented in these tools. As a result MS/MS protein identification tools are becoming too timeconsuming for many applications including search for post-translationally modified peptides. Moreover, matching millions of spectra against all known proteins will soon make these tools too slow in the same way that genome vs. genome comparisons instantly made BLAST too slow. We describe the development of filters for MS/MS database searches that dramatically reduce the running time and effectively remove the bottlenecks in searching the huge space of protein modifications. Our approach, based on a probability model for determining the accuracy of sequence tags, achieves superior results compared to GutenTag, a popular tag generation algorithm.
@InProceedings{frank05peptide,
  author    = {Ari Frank and Stephen Tanner and Pavel Pevzner},
  title     = {Peptide Sequence Tags for Fast Database Search in Mass-Spectrometry},
  booktitle = {Proc. of Research in Computational Molecular Biology (RECOMB 2005)},
  year      = {2005},
  volume    = {3500},
  series    = lncs,
  pages     = {326--341},
  publisher = Springer,
  abstract  = {Filtration techniques, in the form of rapid elimination of candidate sequences while retaining the true one, are key ingredients of database searches in genomics. Although SEQUEST and Mascot are sometimes referred to as BLAST for mass-spectrometry, the key algorithmic idea of BLAST (filtration) was never implemented in these tools. As a result MS/MS protein identification tools are becoming too timeconsuming for many applications including search for post-translationally modified peptides. Moreover, matching millions of spectra against all known proteins will soon make these tools too slow in the same way that genome vs. genome comparisons instantly made BLAST too slow. We describe the development of filters for MS/MS database searches that dramatically reduce the running time and effectively remove the bottlenecks in searching the huge space of protein modifications. Our approach, based on a probability model for determining the accuracy of sequence tags, achieves superior results compared to GutenTag, a popular tag generation algorithm.},
  file      = {FrankEtAl_PeptideSequenceTags_Recomb_2005.pdf:2005/FrankEtAl_PeptideSequenceTags_Recomb_2005.pdf:PDF},
  owner     = {Sebastian},
  timestamp = {2006.03.21},
}

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