SILACanalyzer: a tool for differential quantitation of stable isotope derived data

  • Authors:

  • Lars Nilse;Marc Sturm;David Trudgian;Mogjiborahman Salek;Paul F. G. Sims;Kathleen M. Carroll;Simon J. Hubbard

  • Affiliations:

  • Faculty of Life Sciences, University of Manchester, Manchester, UK and Wilhelm Schickard Institute for Computer Science, Eberhard Karls University, Tübingen, Germany;Wilhelm Schickard Institute for Computer Science, Eberhard Karls University, Tübingen, Germany;Centre for Cellular and Molecular Physiology, University of Oxford, Oxford, UK and Sir William Dunn School of Pathology, University of Oxford, Oxford, UK;Sir William Dunn School of Pathology, University of Oxford, Oxford, UK;Faculty of Life Sciences, Manchester Interdisciplinary Biocentre, Manchester, UK;Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, Manchester, UK;Faculty of Life Sciences, University of Manchester, Manchester, UK

  • Venue:
  • CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Quantitative proteomics is a growing field where several experimental techniques such as those based around stable isotope labelling are reaching maturity. These advances require the parallel development of informatics tools to process and analyse the data, especially for high-throughput experiments seeking to quantify large numbers of proteins. We have developed a novel algorithm for the quantitative analysis of stable isotope-based proteomics data at the peptide level. Without prior formal identification of the peptides by MS/MS, the algorithm determines the mass charge ratio m/z and retention time t of stable isotope-labelled peptide pairs and calculates their relative ratios. It supports several nonproprietary XML input formats and requires only minimal parameter tuning and runs fully automated. We have tested its performance on a low complexity peptide sample in an initial study. In comparison to a manual analysis and an automated approach using MSQuant, it performs as well or better and therefore we believe it has utility for groups wishing to perform high-throughput experiments.