+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: not found

      The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

      1 , 2 , 2 , 3 , 3 , 3 , 4 , 4 , 5 , 6 , 7 , 8 , 7 , 8 , 9 , 3 , 10 , 3 , 11 , 11 , 1 , 3 , 12 , 3 , 3 , 3 , 3 , 12 , 3 , 12 , 11 , 13 , 5 , 14 , 14 , 7 , 8 , 15 , 5 , 7 , 8 , 4 , 14 , 6 , 16 , 17 , 18 , 19 , 20 , 3 , 12 ,   1 , 2

      Nature methods

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants ( e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.

          Related collections

          Most cited references 40

          • Record: found
          • Abstract: not found
          • Article: not found

          TreeView: an application to display phylogenetic trees on personal computers.

           Roderic Page (1996)
            • Record: found
            • Abstract: found
            • Article: not found

            Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

            We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
              • Record: found
              • Abstract: found
              • Article: not found

              TANDEM: matching proteins with tandem mass spectra.

              Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to sequence the original peptide completely. Several proprietary software applications have been developed to attempt to match the spectra with a list of protein sequences that may contain the sequence of the peptide. The application TANDEM was written to provide the proteomics research community with a set of components that can be used to test new methods and algorithms for performing this type of sequence-to-data matching. The source code and binaries for this software are available at http://www.proteome.ca/opensource.html, for Windows, Linux and Macintosh OSX. The source code is made available under the Artistic License, from the authors.

                Author and article information

                Nat Methods
                Nat. Methods
                Nature methods
                20 July 2013
                07 July 2013
                August 2013
                01 February 2014
                : 10
                : 8
                : 730-736
                [1 ]Department of Pathology, University of Michigan, Ann Arbor, MI, USA
                [2 ]Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
                [3 ]Centre for Systems Biology, Samuel Lunenfeld Research Institute at Mount Sinai Hospital, Toronto, ON, Canada
                [4 ]Department of Molecular Biology, Princeton University, Princeton, NJ, USA
                [5 ]Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
                [6 ]Stowers Institute for Medical Research, Kansas City, MO, USA
                [7 ]Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
                [8 ]Netherlands Proteomics Center, Utrecht, The Netherlands
                [9 ]Division of Cell Biology, Netherlands Cancer Institute, Amsterdam, The Netherlands
                [10 ]Department of Molecular Biology; Faculty of Science; Nijmegen Centre for Molecular Life Sciences; Radboud University; Nijmegen, The Netherlands
                [11 ]Institut de recherches cliniques de Montréal (IRCM), Montréal, QC, Canada
                [12 ]Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
                [13 ]Department of Biochemistry, Université de Montréal, Montréal, QC, Canada
                [14 ]CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
                [15 ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore
                [16 ]Department of Pathology & Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
                [17 ]Ontario Cancer Institute, Toronto, ON, Canada
                [18 ]Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
                [19 ]Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
                [20 ]Department of Genetics and Genome Science, Case Western Reserve University School of Medicine, Cleveland, OH, USA
                Author notes
                [* ]To whom all correspondence should be addressed. gingras@ 123456lunenfeld.ca , nesvi@ 123456med.umich.edu

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM094231 || GM

                Life sciences


                Comment on this article