title: APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools.
abstract: BACKGROUND
  Mass spectrometry analyses of complex protein samples yield large amounts of data and specific expertise is needed for data analysis, in addition to a dedicated computer infrastructure. Furthermore, the identification of proteins and their specific properties require the use of multiple independent bioinformatics tools and several database search algorithms to process the same datasets. In order to facilitate and increase the speed of data analysis, there is a need for an integrated platform that would allow a comprehensive profiling of thousands of peptides and proteins in a single process through the simultaneous exploitation of multiple complementary algorithms.
  
  
  RESULTS
  We have established a new proteomics pipeline designated as APP that fulfills these objectives using a complete series of tools freely available from open sources. APP automates the processing of proteomics tasks such as peptide identification, validation and quantitation from LC-MS/MS data and allows easy integration of many separate proteomics tools. Distributed processing is at the core of APP, allowing the processing of very large datasets using any combination of Windows/Linux physical or virtual computing resources.
  
  
  CONCLUSIONS
  APP provides distributed computing nodes that are simple to set up, greatly relieving the need for separate IT competence when handling large datasets. The modular nature of APP allows complex workflows to be managed and distributed, speeding up throughput and setup. Additionally, APP logs execution information on all executed tasks and generated results, simplifying information management and validation.
authors.affiliation: Division of Glycoscience, School of Biotechnology, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, Sweden. emalm@kth.se.
authors.forename: Erik K
authors.initials: EK
authors.lastname: Malm
authors.affiliation: Division of Glycoscience, School of Biotechnology, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, Sweden. vaibhav.srivastava@biotech.kth.se.
authors.forename: Vaibhav
authors.initials: V
authors.lastname: Srivastava
authors.affiliation: Division of Glycoscience, School of Biotechnology, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, Sweden. gustavs@kth.se.
authors.forename: Gustav
authors.initials: G
authors.lastname: Sundqvist
authors.affiliation: Division of Glycoscience, School of Biotechnology, Royal Institute of Technology (KTH), AlbaNova University Centre, Stockholm, Sweden. bulone@kth.se.
authors.forename: Vincent
authors.initials: V
authors.lastname: Bulone
mesh_terms: D022126:Access to Information; D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D010446:Peptide Fragments / Q000032:analysis; D032107:Populus / Q000378:metabolism; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods*; D057188:Workflow
chemical_list: D010446:Peptide Fragments; D011506:Proteins