SAINT: probabilistic scoring of affinity purification–mass spectrometry data
H Choi, B Larsen, ZY Lin, A Breitkreutz… - Nature …, 2011 - nature.com
Nature methods, 2011•nature.com
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.
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.
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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