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      Targeted protein quantification using sparse reference labeling.

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          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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          Author and article information

          Journal
          Nat. Methods
          Nature methods
          1548-7105
          1548-7091
          Mar 2014
          : 11
          : 3
          Affiliations
          [1 ] 1] Department of Statistics, Purdue University, West Lafayette, Indiana, USA. [2].
          [2 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2].
          [3 ] 1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, Zurich, Switzerland. [3] Faculty of Science, University of Zurich, Zurich, Switzerland.
          [4 ] 1] Department of Statistics, Purdue University, West Lafayette, Indiana, USA. [2] Department of Computer Science, Purdue University, West Lafayette, Indiana, USA.
          Article
          nmeth.2806
          10.1038/nmeth.2806
          24441934

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