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Open source libraries and frameworks for mass spectrometry based proteomics: A developers perspective

机译:基于质谱的蛋白质组学的开源库和框架:开发人员的观点

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摘要

Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
机译:数据处理,管理和可视化是基于高通量质谱(MS)的最新蛋白质组学实验的核心和关键组成部分,通常是一些最耗时的步骤,特别是对于没有太多生物信息学支持的实验室。蛋白质组学领域的兴趣日益浓厚,引发了新软件库(包括免费提供的开源软件)的开发增长。从数据库搜索分析到识别结果的后处理,即使这些库和程序包的目标可能有很大不同,但它们通常具有许多功能。常见的用例包括蛋白质和肽序列的处理,各种蛋白质组学搜索引擎输出文件的结果解析以及与MS相关的信息(包括质谱和色谱图)的可视化。在这篇评论中,我们提供了现有软件库,开源框架的概述,并且,我们提供了一些利用它们的免费应用程序的信息。本文是名为“后识别时代的计算蛋白质组学”的特刊的一部分。客座编辑:马丁·埃塞纳赫和克里斯蒂安·斯蒂芬。

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