title: Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets.
abstract: BACKGROUND
  The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that coelute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pair-wise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result.
  
  
  RESULTS
  We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools.
  
  
  BACKGROUND
  The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment.
authors: School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
authors: Joe
authors: J
authors: Wandy
authors: School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
authors: Rónán
authors: R
authors: Daly
authors: School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
authors: Rainer
authors: R
authors: Breitling
authors: School of Computing Science, University of Glasgow, Glasgow, UK, School of Computing and Mathematical Sciences, Liverpool John Moores University, Merseyside, UK and Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
authors: Simon
authors: S
authors: Rogers
mesh_terms: D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D054794:Glycomics / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D010446:Peptide Fragments / Q000032:analysis*; D040901:Proteomics / Q000379:methods*
chemical_list: D010446:Peptide Fragments
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