PEDRo: a database for storing, searching and disseminating experimental proteomics data.
BACKGROUND
Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets.


RESULTS
This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available.


CONCLUSIONS
The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.
Garwood
Kevin
K
Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. garwood@cs.man.ac.uk
McLaughlin
Thomas
T
Garwood
Chris
C
Joens
Scott
S
Morrison
Norman
N
Taylor
Christopher F
CF
Carroll
Kathleen
K
Evans
Caroline
C
Whetton
Anthony D
AD
Hart
Sarah
S
Stead
David
D
Yin
Zhikang
Z
Brown
Alistair J P
AJ
Hesketh
Andrew
A
Chater
Keith
K
Hansson
Lena
L
Mewissen
Muriel
M
Ghazal
Peter
P
Howard
Julie
J
Lilley
Kathryn S
KS
Gaskell
Simon J
SJ
Brass
Andy
A
Hubbard
Simon J
SJ
Oliver
Stephen G
SG
Paton
Norman W
NW
D000818:Animals; D029681:Arabidopsis Proteins / Q000737:chemistry; D001426:Bacterial Proteins / Q000737:chemistry; D002176:Candida albicans / Q000737:chemistry / Q000472:pathogenicity; D041221:Candida glabrata / Q000737:chemistry / Q000472:pathogenicity; D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems / Q000639:trends*; D030562:Databases, Protein / Q000639:trends*; D005656:Fungal Proteins / Q000737:chemistry; D015801:Helminth Proteins / Q000737:chemistry; D007579:Jejunal Diseases / Q000469:parasitology; D051379:Mice; D011506:Proteins / Q000737:chemistry; D040901:Proteomics; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry; D012985:Software Design; D048372:Streptomyces coelicolor / Q000737:chemistry; D017160:Trichinella spiralis / Q000472:pathogenicity; D014235:Trichinellosis / Q000473:pathology
D029681:Arabidopsis Proteins; D001426:Bacterial Proteins; D005656:Fungal Proteins; D015801:Helminth Proteins; D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins