Literature DB >> 15966812

Current status of proteomic standards development.

Sandra Orchard1, Chris Taylor, Henning Hermjakob, Weimin Zhu, Randall Julian, Rolf Apweiler.   

Abstract

The generation of proteomic data is becoming ever more high throughput. Both the technologies and experimental designs used to generate and analyze data are becoming increasingly complex. The need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognized. Work by the Proteome Standards Initiative of the Human Proteome Organization has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The Minimum Information About a Proteomic Experiment data model describes both the scope and purpose of a proteomics experiment and encompasses the development of more specific interchange formats such as the mzData model of mass spectrometry. The eXtensible Mark-up Language-MI data interchange format, which allows exchange of molecular interaction data, has already been published and major databases within this field are supplying data downloads in this format.

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Year:  2004        PMID: 15966812     DOI: 10.1586/14789450.1.2.179

Source DB:  PubMed          Journal:  Expert Rev Proteomics        ISSN: 1478-9450            Impact factor:   3.940


  5 in total

1.  HTAPP: high-throughput autonomous proteomic pipeline.

Authors:  Kebing Yu; Arthur R Salomon
Journal:  Proteomics       Date:  2010-06       Impact factor: 3.984

2.  Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content.

Authors:  Vladislav A Petyuk; Navdeep Jaitly; Ronald J Moore; Jie Ding; Thomas O Metz; Keqi Tang; Matthew E Monroe; Aleksey V Tolmachev; Joshua N Adkins; Mikhail E Belov; Alan R Dabney; Wei-Jun Qian; David G Camp; Richard D Smith
Journal:  Anal Chem       Date:  2007-12-29       Impact factor: 6.986

3.  PeptideDepot: flexible relational database for visual analysis of quantitative proteomic data and integration of existing protein information.

Authors:  Kebing Yu; Arthur R Salomon
Journal:  Proteomics       Date:  2009-12       Impact factor: 3.984

4.  multiplierz: an extensible API based desktop environment for proteomics data analysis.

Authors:  Jignesh R Parikh; Manor Askenazi; Scott B Ficarro; Tanya Cashorali; James T Webber; Nathaniel C Blank; Yi Zhang; Jarrod A Marto
Journal:  BMC Bioinformatics       Date:  2009-10-29       Impact factor: 3.169

Review 5.  Data standards can boost metabolomics research, and if there is a will, there is a way.

Authors:  Philippe Rocca-Serra; Reza M Salek; Masanori Arita; Elon Correa; Saravanan Dayalan; Alejandra Gonzalez-Beltran; Tim Ebbels; Royston Goodacre; Janna Hastings; Kenneth Haug; Albert Koulman; Macha Nikolski; Matej Oresic; Susanna-Assunta Sansone; Daniel Schober; James Smith; Christoph Steinbeck; Mark R Viant; Steffen Neumann
Journal:  Metabolomics       Date:  2015-11-17       Impact factor: 4.290

  5 in total

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