4.3 Article

Nmrglue: an open source Python package for the analysis of multidimensional NMR data

期刊

JOURNAL OF BIOMOLECULAR NMR
卷 55, 期 4, 页码 355-367

出版社

SPRINGER
DOI: 10.1007/s10858-013-9718-x

关键词

Nuclear magnetic resonance; Solid-state NMR; Data processing; Data analysis; Data visualization; Python; Open source

资金

  1. National Science Foundation [MCB-0745754]
  2. National Institutes of Health [R01GM094357]
  3. Camille and Henry Dreyfus Foundation
  4. Eli Lilly and Company
  5. Direct For Biological Sciences
  6. Div Of Molecular and Cellular Bioscience [0745754] Funding Source: National Science Foundation

向作者/读者索取更多资源

Nmrglue, an open source Python package for working with multidimensional NMR data, is described. When used in combination with other Python scientific libraries, nmrglue provides a highly flexible and robust environment for spectral processing, analysis and visualization and includes a number of common utilities such as linear prediction, peak picking and lineshape fitting. The package also enables existing NMR software programs to be readily tied together, currently facilitating the reading, writing and conversion of data stored in Bruker, Agilent/Varian, NMRPipe, Sparky, SIMPSON, and Rowland NMR Toolkit file formats. In addition to standard applications, the versatility offered by nmrglue makes the package particularly suitable for tasks that include manipulating raw spectrometer data files, automated quantitative analysis of multidimensional NMR spectra with irregular lineshapes such as those frequently encountered in the context of biomacromolecular solid-state NMR, and rapid implementation and development of unconventional data processing methods such as covariance NMR and other non-Fourier approaches. Detailed documentation, install files and source code for nmrglue are freely available at http://nmrglue.com. The source code can be redistributed and modified under the New BSD license.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据