SAINT: Probabilistic Scoring of Affinity Purification - Mass Spectrometry Data
By Hyungwon Choi, B. Larsen, Zhen-Yuan Lin, Ashton Breitkreutz, Dattatreya Mellacheruvu, Damian Fermin, Zhaohui S. Qin, Mike Tyers, A. Gingras, Alexey I. Nesvizhskii. Nature Methods, 2010
740 citations recorded by the source corpus.
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.
Subfields: Cell Biology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6b5376a4d481c4ede9cacf396cd49115d57b04b7
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In Molecular Biology
- Alexey I. Nesvizhskii
- Hyungwon Choi
- A. Gingras
- S. Artavanis-Tsakonas
- Steven P. Gygi
- R. Aebersold
- Matthias Mann
- Jimmy K. Eng
Citations in the corpus
- SAINTexpress: improvements and additional features in Significance Analysis of Interactome software (2013, methodology): “The method was later extended to analyze datasets with negative control purifications, which allows more robust removal of background noise interactions from the data [2, 3].”
- A Protein Complex Network of Drosophila melanogaster (2011, methodology): “The coAP-MS data set was also analyzed using published scoring methods (Breitkreutz et al., 2010; Choi et al., 2011; Gavin et al., 2006; Hart et al., 2007; Sowa et al., 2009).”