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Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs

Overview of attention for article published in BMC Bioinformatics, November 2012
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Title
Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs
Published in
BMC Bioinformatics, November 2012
DOI 10.1186/1471-2105-13-s16-s6
Pubmed ID
Authors

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek

Abstract

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 281 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
United Kingdom 2 <1%
South Africa 1 <1%
India 1 <1%
France 1 <1%
Belgium 1 <1%
Germany 1 <1%
Spain 1 <1%
Russia 1 <1%
Other 0 0%
Unknown 268 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 83 30%
Student > Ph. D. Student 72 26%
Student > Master 26 9%
Student > Bachelor 17 6%
Other 11 4%
Other 42 15%
Unknown 30 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 103 37%
Biochemistry, Genetics and Molecular Biology 60 21%
Medicine and Dentistry 20 7%
Computer Science 14 5%
Chemistry 11 4%
Other 36 13%
Unknown 37 13%