Moscow Institute of Physics and Technology , Dolgoprudny, Moscow Region 141701 , Russia.
V.L. Talrose Institute for Energy Problems of Chemical Physics , Russian Academy of Sciences , Moscow 119334 , Russia.
J Proteome Res. 2019 Feb 1;18(2):709-714. doi: 10.1021/acs.jproteome.8b00717. Epub 2019 Jan 8.
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workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished.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.
许多推动当今蛋白质组学技术发展的新颖思路主要集中在实验或数据处理工作流程上。这些工作流程以多种方式实现和发布，从定制脚本和程序到使用通用或专用工作流引擎构建的项目；很大一部分常规数据处理是手动完成的，或者使用未发布的自定义脚本完成的。促进可重复数据处理工作流程的开发对于提高蛋白质组学研究的效率变得至关重要。为了帮助克服蛋白质组学实验室日常实践中的生物信息学挑战，我们在 5 年前开发并发布了 Pyteomics，这是一个免费的开源库，提供了用于蛋白质组学数据的 Python 接口。我们总结了自推出以来 Pyteomics 所开发的新功能。
