the Gingras Laboratory

The Lunenfeld-Tanenbaum Research Institute




James Knight developed interactive analysis visualization tools for interaction proteomics that enable organizing bait-prey data as heatmaps or dotplots, quantitatively comparing two baits, calculating the specificity of preys across baits jointly analyzed, and analyzing the prey-prey relationships (a feature that is most useful for proximity biotinylation studies. With a simple text-based input compatible with SAINT or CRAPome outputs, this web-based resource should facilitate interaction proteomics analysis and visualization.
- ProHits-viz
- publication
ProHits 4.0: Ready for DIA analysis
Guomin (Frank Liu) and colleagues in the Gingras, Nesvizhskii, Bandeira, Choi, Tyers and Raught labs release a major update to the ProHits LIMS. In ProHits 4.0, Data Independent Acquisition (DIA) is handled through DIA-Umpire, MSPLIT-DIA, mapDIA and SAINT-intensity. ProHits 4.0 also facilitates data deposition in public repositories such as MassIVE and the transfer of data to new visualization tools we have developed.
- software
- publication
DIA-Umpire: Integrated solution for Data Independent Acquisition
Chih-Chiang Tsou from the Nesvizhskii lab developed in collaboration with the Gingras lab DIA-Umpire, an integrated platform for untargeted and semi-targeted identification and quantification from Data Independent Acquisition. DIA-Umpire works on the basis of demultiplexing complex spectra through retention time alignment of MS1 and MS2 peaks and generation of pseudo-MS/MS spectra that can be searched using standard tools developed for shotgun proteomics. DIA-Umpire is integrated in the next public release of ProHits, in addition to being available as a stand-alone tool.
- software
- publication in Nature Methods
MSPLIT-DIA: spectral matching tool for Data Independent Acquisition
Jian Wang from the Bandeira lab developed in collaboration with the Gingras lab MSPLIT-DIA, a spectral library matching tool for identification of Data Independent Analysis data. MSPLIT-DIA demultiplexes complex spectra through spectral projection, leading to the sensitive identification of peptides in DIA data. Used as a stand-alone, MSPLIT-DIA performs sensitive spectral counting compatible with SAINT analysis, and can also generate assay-specific libraries for targeted re-extraction.
- software
- publication in Nature Methods
Visualization web-tool (dotplots)
"A web-tool for visualizing quantitative protein-protein interaction data". Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, confidence score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data and present this information to others in an intuitive way. (see publication in Proteomics).
- (website) designed by James Knight
SAINT series of software tools
Significance Analysis of INTeractome (SAINT) tools for interaction scoring were initially developed for unsupervised analysis of interactome data (Science, 2010), but rapidly adapted by Hyungwon Choi (Nesvizhskii lab; Nat Methods, 2011) for semi-supervised analysis of spectral-count based interaction proteomics data using negative controls. More recent advances include the extension to intensity data (JPR, 2012), the implementation of the computationally efficient SAINTexpress (J Proteomics, 2015) and in 2016, the extension of the SAINT-intensity framework to peptides and transitions, for adaptation to Data Independent Analysis (DIA) data (Proteomics, 2016).
- software maintained and further developed by Hyungwon Choi
- SAINT scoring is implemented within the ProHits LIMS, in the Contaminant Repository for Affinity Purification (CRAPome) and for the use of our visualization tools (published version at ProHitsTools and unpublished version in preparation at ProHits-viz).

ProHits-web: Web-accessible quantitative interaction repository
Interaction proteomics repository for the Gingras lab and our collaborators. The repository is meant to support publications by offering access to high quality quantitative interaction proteomics data and other Supplementary material through easy graphical user interfaces. We also use the resource to give password-protected access to specific datasets to collaborators and/or reviewers.
- Website developed by JianPian Zhang and Frank (Guomin) Liu (Gingras lab)
- First publication using the resource: Couzens et al., Sci Signal, 2013 (Hippo pathway signaling network).
- Major datasets currently in ProHits-web include: the HSP90 co-chaperone specificity dataset (Taipale et al., Cell, 2014), an interaction dataset for the myotubularin phosphatases (St-Denis et al., Mol Cell Proteomics, 2015) and a comprehensive map of the centrosome-cilium interface (Gupta et al., Cell, 2015).
CRAPome: Contaminant repository for Affinity Purification
Database of annotated negative controls contributed by the research community to help distinguishing real interactions from the non-specific background (see publication in Nature Methods).
- (website) designed by Datta Mellacheruvu and Zach Wright (Nesvizhskii lab).
ProHits - interaction proteomics LIMS
"ProHits: integrated software for mass spectrometry-based interaction proteomics", Liu et al., Nat Biotech, 2010.
This software helps manage interaction proteomics data - Distributed as open source from
-software developed by Guomin (Frank) Liu and JianPian Zhang
SAINT version 1 - unsupervised model
"A global protein kinase and phosphatase interaction network in yeast", Breitkreutz et al., Science, 2010.
SAINT version 1.0 allows to define the interaction significance based on label-free quantitative AP-MS data (optimized for large-scale datasets with no control runs)
-software developed by Hyungwon Choi (Nesvizhskii's lab)
Nested clusters
"Analysis of protein complexes via model-based biclustering of label-free quantitative AP-MS data", Choi et al., Mol Sys Biol, 2010
This software helps in the identification of protein complexes from AP-MS data
-software developed by Hyungwon Choi (Nesvizhskii's lab)

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