Computational tools for mass spectrometry and proteomic data analysis
Modern proteomic experiments can routinely profile several thousand proteins in a single assay. These results are driven in-part by a significant increase in the rate of data acquisition provided by state-of-the-art mass spectrometry platforms. Unfortunately, it is too often the case that scientists spend more time trying to analyze and build knowledge from these data than was required to perform the original discovery experiment. These efforts are complicated by the closed nature of commercial data systems and their proprietary data formats. We have developed multiple, open-source software tools, built around a robust Python scripting environment, that provide unfettered access to the underlying data files, and enable analysis of results in the context of biological pathways and networks. We have also established a rigorous computational framework for statistical evaluation of quantitative proteomic data. The overriding objective of these activities is to reduce barriers to entry for those researchers not formally trained in the underlying technologies. See papers below and our resources page for more information.

Publications related to informatics and computational tools
   13.  Ficarro SB, Alexander WM, Marto JA. mzStudio: a dynamic digital canvas for user-driven interrogation of mass spectrometry data. Proteomes
           2017;5:pii:20§.

    12.  Alexander WM, Ficarro SB, Adelmant G, Marto JA. Multiplierz v2.0: a python-based ecosystem for shared access and analysis of native mass
            spectrometry data. Proteomics 2017;17:1700091.

    11.  Webber JT, Askenazi M, Ficarro SB, Iglehart MA, Marto JA. Library dependent lc-ms/ms acquisition via mzapi/live. Proteomics 2013;13:1412-16.

    10.  Parikh JR, Xia Y, Marto JA. Multi-edge gene set networks reveal novel insights into global relationships between biological themes. PLoS ONE
           2012;7:e45211.

     9.    Mandel M, Askenazi M, Zhang Y, Marto JA. Variance function estimation in quantitative mass spectrometry with application to itraq labeling. Annals of
             Appl Stats 2013;7:1-24.

     8.    Askenazi M, Webber JT, Marto JA. mzserver: web-based programmatic access for mass spectrometry data analysis. Mol Cell Proteomics
            2011;10:M110.003988, 1-7.*

     7.    Webber JT, Askenazi M, Marto JA. mzresults: an interactive viewer for interrogation and distribution of proteomics results. Mol Cell Proteomics
             2011;10:M110.003970, 1-7.

     6.    Askenazi M, Marto JA, Linial M. The complete peptide dictionary – a meta-proteomics resource.  Proteomics 2010;10:4306-10.

     5.    Parikh JR, Klinger B, Xia Y, Marto JA, Blüthgen N. Discovering causal signaling pathways through gene expression patterns using SPEED. Nucleic Acids
            Res. 2010; 38 Suppl:W109-17.

     4.    Askenazi M, Li S, Singh S, Marto JA. Pathway Palette: A rich internet application for peptide-, protein- and network-oriented analysis of MS data.
            Proteomics 2010;10:1880-85*.

     3.    Zhang Y, Askenazi M, Jiang J, Luckey CJ, Griffin JD, Marto JA. A robust error model for iTRAQ quantification reveals divergent signaling between
            oncogenic FLT3 mutants in acute myeloid leukemia. Mol Cell Proteomics 2010;9:780-90.

     2.    Parikh JR, Askenazi M, Ficarro SB, Cashorali T, Webber JT, Blank NC, Zhang Y, Marto JA. multiplierz: an extensible API based desktop environment for
            proteomics data analysis. BMC Bioinformatics 2009;10:364.

     1.    Askenazi M, Parikh JR, Marto JA. mzAPI: a new strategy for efficiently sharing mass spectrometry data. Nat Methods 2009;6:240-1.