Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework
Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework
复制标题
DOI：
10.1021/acs.jproteome.8b00717
复制
发表时间：
2019-02-01
影响因子：
4.4
通讯作者：
Gorshkov, Mikhail V.
中科院分区：
文献类型：
--
作者：
Levitsky, Lev I.;Klein, Joshua A.;Gorshkov, Mikhail V.
许多驱动当今蛋白质组学技术的新想法基本上集中在实验或数据处理工作流程上。后者以多种方式实现和发布，从自定义脚本和程序到使用通用或专用工作流引擎构建的项目;大部分例行数据处理是手动执行的，或者使用尚未发布的自定义脚本。促进可重复的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至关重要。为了帮助克服蛋白质组实验室日常实践中的生物信息学挑战，5年前我们开发并发布了Pyteomics，这是一个免费的开源库，为蛋白质组数据提供Python接口。我们总结了Pyteomics自推出以来开发的新功能。
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workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished.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.