Pyteomics 4.0: Five Years of Development of a Python']Python Proteomics Framework

被引:109
作者
Levitsky, Lev I. [1 ,2 ]
Klein, Joshua A. [3 ]
Ivanov, Mark V. [2 ]
Gorshkov, Mikhail V. [2 ]
机构
[1] Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Moscow Region, Russia
[2] Russian Acad Sci, VL Talrose Inst Energy Problems Chem Phys, Moscow 119334, Russia
[3] Boston Univ, Bioinformat Program, Boston, MA 02215 USA
基金
俄罗斯基础研究基金会;
关键词
proteomics; !text type='Python']Python[!/text; software libraries; MASS-SPECTROMETRY; PEPTIDE IDENTIFICATION; LIQUID-CHROMATOGRAPHY; DATA VISUALIZATION; SOFTWARE PLATFORM; STATISTICAL-MODEL; DATA STANDARD; TANDEM; MS/MS; TOOL;
D O I
10.1021/acs.jproteome.8b00717
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished.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.
引用
收藏
页码:709 / 714
页数:6
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