Teixeira, JMC, Skinner, SP orcid.org/0000-0002-9349-6935, Arbesú, M et al. (2 more authors) (2018) Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data. Journal of Biomolecular NMR, 71 (1). pp. 1-9. ISSN 0925-2738
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) The Author(s) 2018, This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | NMR spectroscopy; Data Analysis; Intrinsically Disordered Proteins; Paramagnetic-NMR; Proteins; Chemical Shift Perturbations |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) > NMR (Leeds) |
Funding Information: | Funder Grant number MRC MR/P000355/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Apr 2018 10:24 |
Last Modified: | 25 Jun 2023 21:19 |
Status: | Published |
Publisher: | Springer Netherlands |
Identification Number: | 10.1007/s10858-018-0182-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130047 |