License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.WABI.2017.26
URN: urn:nbn:de:0030-drops-76383
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7638/
Go to the corresponding LIPIcs Volume Portal

Frank, Yves ; Hruz, Tomas ; Tschager, Thomas ; Venzin, Valentin

Improved De Novo Peptide Sequencing using LC Retention Time Information

pdf-format:
LIPIcs-WABI-2017-26.pdf (0.6 MB)

Abstract

Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) is an important tool in proteomics for identifying the peptides in a sample. Liquid chromatography temporally separates the peptides and tandem mass spectrometry analyzes the peptides, that elute one after another, by measuring their mass-to-charge ratios and the mass-to-charge ratios of their prefix and suffix fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of the analyzed peptide from this measurement data. While previous approaches solely consider the mass spectrum of the fragments for reconstructing a sequence, we propose to also exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the retention time of the separation by liquid chromatography. We consider three models for predicting the retention time of a peptide and develop algorithms for de novo sequencing for each model. An evaluation on experimental data from synthesized peptides for two of these models shows an improved performance compared to not using the chromatographic information.

BibTeX - Entry

@InProceedings{frank_et_al:LIPIcs:2017:7638,
  author =	{Yves Frank and Tomas Hruz and Thomas Tschager and Valentin Venzin},
  title =	{{Improved De Novo Peptide Sequencing using LC Retention Time Information}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{26:1--26:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Russell Schwartz and Knut Reinert},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7638},
  URN =		{urn:nbn:de:0030-drops-76383},
  doi =		{10.4230/LIPIcs.WABI.2017.26},
  annote =	{Keywords: Computational proteomics, Peptide identification, Mass spectrometry, De novo peptide sequencing, Retention time prediction}
}

Keywords: Computational proteomics, Peptide identification, Mass spectrometry, De novo peptide sequencing, Retention time prediction
Collection: 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)
Issue Date: 2017
Date of publication: 11.08.2017

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI