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J Proteomics. 2014 Apr 4;100:37-43. doi: 10.1016/j.jprot.2013.10.023. Epub 2013 Oct 26.

SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software.

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Department of Statistics and Applied Probability, National University of Singapore, Singapore.
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.
Departments of Pathology and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Centre for Systems Biology, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, ON M5G 1X5, Canada; Department of Molecular Genetics, University of Toronto, ON M5S 1A8, Canada.
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore. Electronic address:


Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with simpler statistical model and quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute supplemental topology-based scores to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.


We present SAINTexpress, an upgraded implementation of Significance Analysis of INTeractome (SAINT) for filtering high confidence interaction data from affinity purification-mass spectrometry (AP-MS) experiments. SAINTexpress features faster computation and incorporation of external data sources into the scoring, improving the performance and user experience of SAINT across various types of datasets. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?


Affinity-purification; Probabilistic scoring; Protein–protein interaction

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