PINT, the Proteomics INTegrator, is an online experiment repository for final results coming from different qualitative and/or quantitative proteomics assays.
PINT is a new comprehensive system to store, visualize, and analyze data for proteomics results obtained under different experimental conditions.
PINT provides an extremely flexible and powerful query interface that allows data filtering based on numerous proteomics features such as confidence values, abundance levels or ratios, dataset overlaps, etc…
Furthermore, proteomics results can be combined with queries over the vast majority of the UniprotKB annotations, which are transparently incorporated into the system. For example, these queries can allow rapid identification of proteins with a confidence score above a given threshold that are known to be associated to diseases or they may highlight proteins with a least one phosphorylated site that are shared between a set of experimental conditions.
In addition, PINT allows the developers to incorporate data visualization and analysis tools, serving its role as a centralized hub of proteomics data analysis tools. One example is the recent integration of enrichment analysis with PSEA-Quant online tool.
PINT will thus facilitate interpretation of proteomics results and expedite biological conclusions and, by the same means, deal with the ‘big data’ paradigm in proteomics.
Source code: https://github.com/orgs/proteomicsyates
Use PINT: http://sealion.scripps.edu/pint
Functional enrichment analyses are often used to generate hypotheses regarding the underlying mechanisms revealed in MS-based proteomics datasets (refs). We have recently developed a functional enrichment analysis algorithm explicitly for MS-based proteomics data (PSEA-Quant, Lavallée-Adam, M., Rauniyar, N., McClatchy, D.B., Yates J.R. III: PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data. J. Proteome Res. 13(12), 5496–5509 (2014)). This web-based user-friendly algorithm, inspired by GSEA [ Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., Mesirov, J.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545–15550 (2005)], consists in a novel protein set enrichment analysis for label-free and labeled MS-based quantitative proteomics. Unlike GSEA, PSEA-Quant allows the analysis of proteomics samples originating from a single or multiple conditions. This Java program uses Census’ output, while supporting other file formats, to identify protein sets that are statistically significantly enriched among abundant proteins that are quantified with high reproducibility across a set of replicates.
Click to use [http://pseaquant.scripps.edu]