Linked Life Data

20217861

Source:http://linkedlifedata.com/resource/pubmed/id/20217861

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2010-4-27
pubmed:abstractText
2-DE is a fundamental technology used in proteomics research. However, despite its high capacity to simultaneously separate several proteins for subsequent identification and quantitative comparison studies, a drawback for this technique is its limited reproducibility, especially when comparing data from different laboratories. 2-DE-related variability can be broadly divided into two categories: experimental and post-experimental. Experimental variability depends on physical and chemical parameters, whereas post-experimental variability arises when gels are analyzed by different software packages, particularly when different workflows are followed. In this paper, we compared the analysis performance of two software packages, Delta2D and Proteomweaver, using both standard and experimental gel images. Using standard gel images, the false negative spot count was 50% lower, the false positive count was 77% lower, the true positive count was 19% higher and spot matching was 4% higher in Delta2D when compared to Proteomeweaver. Using experimental gel images, we found that the total amount of time taken to complete the analysis with Delta2D was 30% that of the time needed with Proteomweaver and required fewer user interventions. The differences between ease of use and workflow strategy of these programs is discussed.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1522-2683
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1311-7
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Delta2D and Proteomweaver: Performance evaluation of two different approaches for 2-DE analysis.
pubmed:affiliation
Department of Clinical and Experimental Medicine, Division of Metabolism, University of Padua, Padua, Italy. millionirenato@gmail.com
pubmed:publicationType
Journal Article, Comparative Study