Pustovalova Yulia, Delaglio Frank, Craft D Levi, Arthanari Haribabu, Bax Ad, Billeter Martin, Bostock Mark J, Dashti Hesam, Hansen D Flemming, Hyberts Sven G, Johnson Bruce A, Kazimierczuk Krzysztof, Lu Hengfa, Maciejewski Mark, Miljenović Tomas M, Mobli Mehdi, Nietlispach Daniel, Orekhov Vladislav, Powers Robert, Qu Xiaobo, Robson Scott Anthony, Rovnyak David, Wagner Gerhard, Ying Jinfa, Zambrello Matthew, Hoch Jeffrey C, Donoho David L, Schuyler Adam D
Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA.
Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology and the University of Maryland, Rockville, MD 20850, USA.
Magn Reson (Gott). 2021 Nov 25;2(2):843-861. doi: 10.5194/mr-2-843-2021. eCollection 2021.
Although the concepts of nonuniform sampling (NUS) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago , it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., "resolution") or peaks of weak intensity (i.e., "sensitivity"). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the "Nonuniform Sampling Contest" (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform.
尽管多维核磁共振中的非均匀采样(NUS)和非傅里叶谱重建概念在40年前就已开始出现,但直到最近NUS才变得更为普遍。NUS的优势包括能够定制实验,以减少数据采集时间并提高谱图质量,无论是通过检测间距紧密的峰(即“分辨率”)还是强度较弱的峰(即“灵敏度”)。这些方法得到更广泛应用是计算性能提升、软件丰富性和灵活性不断增加、核磁共振光谱仪供应商支持以及更高磁场带来的数据采样需求增加的结果。然而,确定最佳实践仍然是一个重大且未解决的挑战。与离散傅里叶变换不同,用于从NUS数据重建谱图的非傅里叶方法是非线性的,取决于信号的复杂性和性质,并且缺乏描述其性能的定量或形式理论。看似细微的算法差异可能导致谱图质量和伪影的显著变化。已经发起了一项基于社区的对NUS挑战问题的关键评估,称为“非均匀采样竞赛”(NUScon),目的是确定处理和分析NUS实验的最佳实践。我们通过构建注入合成信号的核磁共振实验挑战来实现这一目标,并使用社区提交的工作流程来处理这些挑战。在NUScon的初始轮次中,我们的目标是建立评估谱图重建质量的客观标准。我们在此展示一个用于进行定量分析的软件包,并展示NUScon前两轮的结果。我们讨论仍然存在的挑战,并提出一个由社区驱动的持续发展路线图,最终目标是在这个快速发展的领域提供最佳实践。NUScon软件包以及评估挑战问题的所有数据都托管在NMRbox平台上。