Significance Analysis of
INTeractome
 

Significance Analysis of INTeractome (SAINT) consists of a series of software tools for assigning confidence scores to protein-protein interactions based on quantitative proteomics data in AP-MS experiments. We posted the version used for spectral count data in the yeast kinase interactome work not incorporating control purification, as well as a generalized implementation for spectral count data with and without control purification.

SAINT versions
We currently maintain and distribute 2 versions of SAINT, SAINT v2 (refs 2, 3, 4) and SAINTexpress (ref 5).  Use SAINTexpress for rapid and robust scoring of datasets for which negative control runs quantitatively represent contaminant behavior.  Use SAINT 2.0 when flexibility is needed in the scoring (this is enabled via the use of “options” that enable tailoring the scoring to the dataset, e.g. normalize abundance across all purifications).  SAINT 2.0 is also available to use within the Contaminant Repository for Affinity Purification (CRAPome.org; ref 6).  Both SAINT 2.0 and SAINTexpress are integrated in the ProHits LIMS for interaction proteomics (refs 7-8).

Download
The software and vignette can be downloaded from
http://sourceforge.net/projects/saint-apms/files/

Compilation with GNU Scientific Library (GSL)
GSL is free from the web. Simply run the bash script ‘compile’ to install the program. Directory containing the executable should also be added to the PATH variable. Data preparation steps are described in the vignette.

References
[1] Breitkreutz, A., Choi, H., Sharon,  J., Boucher, L., Neduva, V., Larsen, B.G., Lin, Z.-Y., Breitkreutz, B.-J., Stark, C., Liu, G., Ahn, J., Dewar-Darch, D., Tang, X., Almeida, V., Qin, Z.S., Pawson, T., Gingras, A.-C, Nesvizhskii, A., Tyers, M. (2010) A global protein kinase and phosphatase network. Science, 328:1043-6
* This describes the use of the unsupervised SAINT model that does not require negative controls for scoring.  The unsupervised model should only be used for large scale projects that profile > xxx baits that share very few interactions.

[2] Choi, H., Larsen, B., Lin., Z.-Y., Breitkreutz, A., Mellacheruvu, D., Fermin, D., Qin, Z.S., Tyers, M., Gingras, A.-C. and Nesvizhskii, A.I. (2011) SAINT: probabilistic scoring of affinity purification - mass spectrometry data. Nature Methods, 8:70-3.
* This is the key reference for the SAINT series of algorithms.  We introduced in there the semi-supervised SAINT model, which is based on comparing the spectral count distribution across the negative control runs to the counts for the same prey in the purification of the bait.

[3] Choi, H., Liu, G., Tyers, M., Gingras, A.-C. and Nesvizhskii, A.I. (2012) Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINT. Cur Protoc Bioinformatics, Chapter 8:Unit8.15.
* This is a detailed protocol for the use of SAINT, which defines options (minFold, lowMode and Norm) that can be tailored to the dataset to be analyzed.

[4] Choi, H., Glatter, T., Gstaiger, M. and Nesvizhskii, A.I. (2012) SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments.  J Proteome Res, 11:2619-24.
* This describes the implementation of intensity data (by contrast to count data) for scoring in SAINT. 

[5] Teo, G., Liu, G., Zhang, J.P., Nesvizhskii, A.I., Gingras, A.-C., and Choi, H. (2013) SAINTexpress: improvements and additional features in Significance Analysis of INTeractome for AP-MS data.  J. Proteomics, 100:37-43.
* This manuscript will describe a computationally efficient version of the SAINT tool in which several improvements were made to more sensitively score interactions for prey proteins captured in different amounts across bait purifications.  This manuscript also presents an optional re-scoring based on externally acquired information.

[6] Mellacheruvu, D., Wright, Z., Couzens, A.L, Lambert, J.-P., St-Denis, N., Li, T., Miteva, Y.V., Hauri, S., Sardiu, M.E., Low, T.Y., Halim, V.A., Bagshaw, R., Hubner, N.C., al-Hakim, A., Bouchard, A., Faubert, D., Fermin, D., Dunham, W.H., Goudreault, M., Lin, Z.-Y., Gonzalez Badillo, B., Pawson, T., Durocher, D., Coulombe, B., Aebersold, R., Superti-Furga, G., Colinge, J., Heck, A.J.R., Choi, H., Gstaiger, M., Mohammed, S., Cristea, I.M., Bennett, K.L., Washburn, M.P., Raught, B., Ewing, R.M., Gingras, A.-C., and Nesvizhskii, A.I. (2013) The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data. In press, Nature Methods.
* SAINT 2.0 is used in this manuscript to help users score their interactions by making use of negative control purifications generated by the proteomics community.

[7] Liu, G., Zhang, J.P., Larsen, B, Stark, C., Breitkreutz, A., Lin, Z.-Y., Breitkreutz, B.-J., Ding, Y., Colwill, K., Pasculescu, A., Pawson, T, Wrana, J., Nesvizhskii, A.I., Raught, B, Tyers, M., and Gingras, A.-C. (2010) ProHits: an integrated software platform for mass spectrometry-based interaction proteomics. Nat Biotech, 28:1015-7
* Primary description of the ProHits LIMS which is integrated with SAINT.

[8] Liu, G., Zhang, J., Choi, H., Lambert, J.-P., Srikumar, T., Larsen, B., Nesvizhskii, A.I., Raught, B., Tyers, M., and Gingras, A.-C. (2012) Using ProHits to store, annotate and analyze affinity purification - mass spectrometry (AP-MS) data. Cur Protoc Bioinformatics, Chapter 8:Unit8.16
* This manuscript provides detailed protocols for the use of ProHits for managing interaction proteomics; SAINT 2.0 incorporation in ProHits is defined in this chapter.  Please also note that the current version of ProHits enables the user to run either SAINT 2.0 (with options) or SAINTexpress.

[9] Knight, J.D., Liu, G., Zhang, J., Pasculescu, A., Choi, H., and Gingras, A.-C. (2014) A web-tool for visualizing quantitative protein-protein interaction data. Proteomics, in press. PMID:25422071. Download link: http://prohitstools.mshri.on.ca.
* This manuscript describes a new visualization tool for quantitative interactome data. The web tool provides convenient visualization modules for the raw and SAINTexpress adjusted quantitative data and other statistical summaries such as fold changes and confidence scores.