Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data

Dan Bach Kristensen, Jan Christian Brønd, Peter Aagaard Nielsen, Jens Roswald Andersen, Ole Tang Sørensen, Vibeke Jørgensen, Kenneth Budin, Jesper Matthiesen, Peter Venø, Hans Mikael Jespersen, Christian H Ahrens, Soeren Schandorff, Peder Thusgaard Ruhoff, Jacek R Wisniewski, Keiryn L Bennett, Alexandre V Podtelejnikov

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.
Original languageEnglish
JournalMolecular and Cellular Proteomics
Volume3
Issue number10
Pages (from-to)1023-38
Number of pages15
ISSN1535-9476
DOIs
Publication statusPublished - 1. Oct 2004

Keywords

  • Breast Neoplasms
  • Chromatography, Liquid
  • Computer Graphics
  • Database Management Systems
  • Databases, Factual
  • Female
  • Humans
  • Information Storage and Retrieval
  • Mass Spectrometry
  • Peptides
  • Reproducibility of Results
  • Research Design
  • Software
  • Tumor Cells, Cultured

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