SAINT: probabilistic scoring of affinity purification–mass spectrometry data

• Published in: Nature methods Vol. 8; no. 1; pp. 70 - 73
• Main Authors: Choi, Hyungwon, Larsen, Brett, Lin, Zhen-Yuan, Breitkreutz, Ashton, Mellacheruvu, Dattatreya, Fermin, Damian, Qin, Zhaohui S, Tyers, Mike, Gingras, Anne-Claude, Nesvizhskii, Alexey I
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2011; Nature Publishing Group
• Subjects: 631/1647/2230/2233; 631/1647/48; 631/1647/527/296; 631/61/475/2290; Applications software; Biochemistry; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; brief-communication; Chromatography, Affinity - methods; Computational Biology; Computational mathematics; Computer Simulation; Life Sciences; Mass Spectrometry; Probability; Protein Binding; Protein Interaction Mapping - methods; Protein-protein interactions; Proteins - isolation & purification; Proteins - metabolism; Proteomics; Research methodology; United States
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.1541

A statistical framework for assigning confidence scores for protein-protein interaction data generated via affinity purification–mass spectrometry, called significance analysis of interactome (SAINT) is described. 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.