ArticleNature methods2014
Targeted protein quantification using sparse reference labeling.
Article in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Ion mobility-enhanced MS(E)-based label-free analysis reveals effects of low-dose radiation post contextual fear conditioning training on the mouse hippocampal proteome.Journal of proteomics · 2016Article
- Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis.Molecular & cellular proteomics : MCP · 2016Article
- An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples.Clinical proteomics · 2015Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Targeted proteomics is a method of choice for accurate and high-throughput quantification of predefined sets of proteins. Many workflows use isotope-labeled reference peptides for every target protein, which is time consuming and costly. We report a statistical approach for quantifying full protein panels with a reduced set of reference peptides. This label-sparse approach achieves accurate quantification while reducing experimental cost and time. It is implemented in the software tool SparseQuant.
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Registered trials
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