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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Udgivelsesdato: 2004-Oct
OriginalsprogEngelsk
TidsskriftMolecular and Cellular Proteomics
Vol/bind3
Udgave nummer10
Sider (fra-til)1023-38
Antal sider15
ISSN1535-9476
DOI
StatusUdgivet - 1. okt. 2004

Fingeraftryk

Tandem Mass Spectrometry
Mass spectrometry
Peptides
Proteins
Search Engine
Data Mining
Search engines
Complex Mixtures
Cells
Databases
Ions

Citer dette

Kristensen, Dan Bach ; Brønd, Jan Christian ; Nielsen, Peter Aagaard ; Andersen, Jens Roswald ; Sørensen, Ole Tang ; Jørgensen, Vibeke ; Budin, Kenneth ; Matthiesen, Jesper ; Venø, Peter ; Jespersen, Hans Mikael ; Ahrens, Christian H ; Schandorff, Soeren ; Ruhoff, Peder Thusgaard ; Wisniewski, Jacek R ; Bennett, Keiryn L ; Podtelejnikov, Alexandre V. / Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data. I: Molecular and Cellular Proteomics. 2004 ; Bind 3, Nr. 10. s. 1023-38.
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title = "Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data",
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.",
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",
author = "Kristensen, {Dan Bach} and Br{\o}nd, {Jan Christian} and Nielsen, {Peter Aagaard} and Andersen, {Jens Roswald} and S{\o}rensen, {Ole Tang} and Vibeke J{\o}rgensen and Kenneth Budin and Jesper Matthiesen and Peter Ven{\o} and Jespersen, {Hans Mikael} and Ahrens, {Christian H} and Soeren Schandorff and Ruhoff, {Peder Thusgaard} and Wisniewski, {Jacek R} and Bennett, {Keiryn L} and Podtelejnikov, {Alexandre V}",
year = "2004",
month = "10",
day = "1",
doi = "10.1074/mcp.T400004-MCP200",
language = "English",
volume = "3",
pages = "1023--38",
journal = "Molecular and Cellular Proteomics",
issn = "1535-9476",
publisher = "American Society for Biochemistry and Molecular Biology",
number = "10",

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Kristensen, DB, Brønd, JC, Nielsen, PA, Andersen, JR, Sørensen, OT, Jørgensen, V, Budin, K, Matthiesen, J, Venø, P, Jespersen, HM, Ahrens, CH, Schandorff, S, Ruhoff, PT, Wisniewski, JR, Bennett, KL & Podtelejnikov, AV 2004, 'Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data', Molecular and Cellular Proteomics, bind 3, nr. 10, s. 1023-38. https://doi.org/10.1074/mcp.T400004-MCP200

Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data. / Kristensen, Dan Bach; Brønd, Jan Christian; Nielsen, Peter Aagaard; Andersen, Jens Roswald; Sørensen, Ole Tang; Jørgensen, Vibeke; Budin, Kenneth; Matthiesen, Jesper; Venø, Peter; Jespersen, Hans Mikael; Ahrens, Christian H; Schandorff, Soeren; Ruhoff, Peder Thusgaard; Wisniewski, Jacek R; Bennett, Keiryn L; Podtelejnikov, Alexandre V.

I: Molecular and Cellular Proteomics, Bind 3, Nr. 10, 01.10.2004, s. 1023-38.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Experimental Peptide Identification Repository (EPIR): an integrated peptide-centric platform for validation and mining of tandem mass spectrometry data

AU - Kristensen, Dan Bach

AU - Brønd, Jan Christian

AU - Nielsen, Peter Aagaard

AU - Andersen, Jens Roswald

AU - Sørensen, Ole Tang

AU - Jørgensen, Vibeke

AU - Budin, Kenneth

AU - Matthiesen, Jesper

AU - Venø, Peter

AU - Jespersen, Hans Mikael

AU - Ahrens, Christian H

AU - Schandorff, Soeren

AU - Ruhoff, Peder Thusgaard

AU - Wisniewski, Jacek R

AU - Bennett, Keiryn L

AU - Podtelejnikov, Alexandre V

PY - 2004/10/1

Y1 - 2004/10/1

N2 - 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.

AB - 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.

KW - Breast Neoplasms

KW - Chromatography, Liquid

KW - Computer Graphics

KW - Database Management Systems

KW - Databases, Factual

KW - Female

KW - Humans

KW - Information Storage and Retrieval

KW - Mass Spectrometry

KW - Peptides

KW - Reproducibility of Results

KW - Research Design

KW - Software

KW - Tumor Cells, Cultured

U2 - 10.1074/mcp.T400004-MCP200

DO - 10.1074/mcp.T400004-MCP200

M3 - Journal article

VL - 3

SP - 1023

EP - 1038

JO - Molecular and Cellular Proteomics

JF - Molecular and Cellular Proteomics

SN - 1535-9476

IS - 10

ER -