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Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.

Abstract：

:Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.

journal_name

Plant Physiol

journal_title

Plant physiology

authors

Heazlewood JL,Tonti-Filippini J,Verboom RE,Millar AH

doi

10.1104/pp.105.065532

subject

Has Abstract

pub_date

2005-10-01 00:00:00

pages

598-609

issue

eissn

0032-0889

issn

1532-2548

pii

139/2/598

journal_volume

139

pub_type

杂志文章

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Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.