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SAINT: probabilistic scoring of affinity purification–mass spectrometry data

Nature Methods volume 8, pages 70–73 (2011)Cite this article

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Abstract

We present 'significance analysis of interactome' (SAINT), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity purification–mass spectrometry (AP-MS). The method uses label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We show that SAINT is applicable to data of different scales and protein connectivity and allows transparent analysis of AP-MS data.

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Figure 1: Probability model in SAINT.
Figure 2: Analysis of TIP49 and DUB datasets.

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Acknowledgements

Supported by grants from the Canadian Institute of Health Research to A.-C.G. (MOP-84314) and M.T. (MOP-12246); the US National Institutes of Health to M.T. (5R01RR024031), A.I.N. and A.-C.G. (R01-GM094231), and A.I.N. (R01-CA126239); a Royal Society Wolfson Research Merit Award and a Scottish Universities Life Sciences Alliance Research Chair to M.T.; a Canada Research Chair in Functional Proteomics to A.-C.G.; and the Lea Reichmann Chair in Cancer Proteomics to A.-C.G. We thank M. Sardiu, M. Washburn and M. Sowa for providing additional information regarding the datasets used in this work, G. Bader for discussions and W. Dunham for reading the manuscript.

Author information

Author notes

  1. Zhaohui S Qin

    Present address: Present address: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA.,

Authors and Affiliations

  1. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Hyungwon Choi, Dattatreya Mellacheruvu, Damian Fermin & Alexey I Nesvizhskii

  2. Centre for Systems Biology, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada

    Brett Larsen, Zhen-Yuan Lin, Ashton Breitkreutz, Mike Tyers & Anne-Claude Gingras

  3. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA

    Zhaohui S Qin

  4. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Mike Tyers & Anne-Claude Gingras

  5. Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK

    Mike Tyers

  6. Centre for Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK

    Mike Tyers

  7. Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Alexey I Nesvizhskii

Authors
  1. Hyungwon Choi
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  10. Alexey I Nesvizhskii
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Contributions

H.C. and A.I.N. developed, implemented and tested the SAINT method; H.C. wrote the software; B.L., A.B., Z.-Y.L., A.-C.G. and M.T. generated data for the initial SAINT modeling and provided feedback on the model performance; D.M. and D.F. assisted with data analysis and processing; Z.S.Q. contributed to statistical model development; H.C., A.-C.G. and A.I.N. wrote the manuscript; A.I.N. and A.-C.G. conceived the study; A.I.N. directed the project with input from A.-C.G.

Corresponding authors

Correspondence to Anne-Claude Gingras or Alexey I Nesvizhskii.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 239 kb)

Supplementary Table 1

Data for the TIP49 dataset. (a) List of all detected interactions and scores from PP-NSAF, CompPASS and SAINT. (b) All interactions in control purifications were included in a separate table after merging of 35 technical replicate purifications into 9 purifications. (c) Table of technical replicates of control purifications. (d) GO terms enrichment in top scoring interactions for each scoring method. (XLS 1006 kb)

Supplementary Table 2

Data for the DUB dataset. (a) List of all detected interactions and scores from CompPASS and SAINT. (b–d) GO terms enrichment in top scoring interactions for each scoring method. (XLS 3104 kb)

Supplementary Table 3

Data for the CDC23 dataset. List of all detected interactions with SAINT scores and results reported by t-test. (XLS 98 kb)

Supplementary Software (ZIP 2169 kb)

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Choi, H., Larsen, B., Lin, ZY. et al. SAINT: probabilistic scoring of affinity purification–mass spectrometry data. Nat Methods 8, 70–73 (2011). https://doi.org/10.1038/nmeth.1541

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