multiplierz: An Extensible API Based Desktop Environment for Proteomics Data Analysis
Parikh, Jignesh R
Webber, James T
Blank, Nathaniel C
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CitationParikh, Jignesh R., Manor Askenazi, Scott B. Ficarro, Tanya Cashorali, James T. Webber, Nathaniel C. Blank, Yi Zhang, and Jarrod A. Marto. 2009. multiplierz: an extensible API based desktop environment for proteomics data analysis. BMC Bioinformatics 10: 364.
AbstractBackground: Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge. Results: We describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines. Conclusion: Collectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4724757
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