Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data.
Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data.
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DOI：
10.1007/s10858-018-0182-5
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发表时间：
2018-05
影响因子：
2.7
通讯作者：
Pons M
中科院分区：
文献类型：
--
作者：
Teixeira JMC;Skinner SP;Arbesú M;Breeze AL;Pons M
我们提出了Farseer-NMR（https：//git.io/vAueU），这是一个软件包，用于处理、评估和联合收割机结合来自蛋白质衍生峰列表的NMR光谱数据，这些峰列表涵盖了一系列实验条件。NMR和分子生物学的结合进步使得能够研究复杂的生物分子系统，例如柔性蛋白质或大型多体复合物，其显示出对其环境条件的强烈和功能相关的响应，例如配体的存在，定点突变，翻译后修饰，分子拥挤或溶液的化学组成。这些进展使得越来越需要分析这些系统对多种变量的反应。来自大型和多变量数据集的NMR峰列表的组合分析已经成为NMR分析管道中的新瓶颈，由此必须手动生成信息丰富的NMR衍生参数，这可能是繁琐的、重复的并且容易出现人为错误，或者甚至对于非常大的数据集是不可行的。在开发和分发侧重于峰值列表处理、分析和表示的软件方面，特别是能够处理越来越普遍的大型多变量数据集的软件方面，一直存在差距。在这方面，Farseer-NMR旨在缩小自动化NMR用户管道中的这一长期差距，并将大量峰列表分析的时间负担从数天/数周减少到数秒/分钟。我们已经实现了一些最常见的，以及新的，用于计算NMR参数的例程和几个出版质量绘图模板，以提高NMR数据表示。Farseer-NMR完全用Python编写，其模块化代码库支持轻松扩展。本文的在线版本（10.1007/s10858-018-0182-5）包含补充材料，可供授权用户使用。
We present Farseer-NMR (https://git.io/vAueU), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems’ responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension. The online version of this article (10.1007/s10858-018-0182-5) contains supplementary material, which is available to authorized users.
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