Journal
JOURNAL OF PROTEOME RESEARCH
Volume 18, Issue 2, Pages 709-714Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.8b00717
Keywords
proteomics; Python; software libraries
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Funding
- Russian Foundation for Basic Research [18-29-13015]
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Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.
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