Ravanbakhsh Siamak, Liu Philip, Bjorndahl Trent C, Mandal Rupasri, Grant Jason R, Wilson Michael, Eisner Roman, Sinelnikov Igor, Hu Xiaoyu, Luchinat Claudio, Greiner Russell, Wishart David S
Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Alberta Innovates Center for Machine Learning, Edmonton, AB, Canada.
Department of Computing Science, University of Alberta, Edmonton, AB, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
PLoS One. 2015 May 27;10(5):e0124219. doi: 10.1371/journal.pone.0124219. eCollection 2015.
Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.
许多疾病会导致人体生物流体中出现的小分子(即代谢物)浓度发生显著变化,这意味着此类疾病通常可以从一个人的“代谢谱”(即这些代谢物的浓度列表)中轻易检测出来。该信息可从生物流体核磁共振(NMR)光谱中提取。然而,由于其复杂性,NMR光谱分析一直依赖人工操作,导致过程缓慢、成本高昂且容易出错,阻碍了通过NMR进行代谢组学在临床和工业领域的应用。本文介绍了一个名为BAYESIL的系统,它能够快速、准确且自主地生成一个人的代谢谱。给定复杂生物流体(具体为血清或脑脊液)的一维氢核磁共振光谱,BAYESIL可以自动确定代谢谱。这首先需要执行几个光谱处理步骤,然后将所得光谱与参考化合物库进行匹配,该库包含每种相关代谢物的“特征”。BAYESIL将光谱匹配视为概率图形模型中的一个推理问题,该模型能快速逼近最可能的代谢谱。我们对包括真实生物样本(血清和脑脊液)、特定混合物以及逼真的计算机生成光谱在内的各种复杂混合物进行了广泛研究;涉及超过50种化合物,结果表明BAYESIL能够在单个CPU上不到5分钟的时间内准确地自主找到NMR可检测代谢物的浓度(识别正确率约为90%,定量误差约为10%)。这些结果表明,BAYESIL是首个完全自动且可公开访问的系统,能够有效地提供定量NMR光谱分析——对这些生物流体的分析精度达到或超过了训练有素的专家的水平。我们预计该工具将引领高通量代谢组学的发展,并使NMR在临床环境中有大量新的应用。可通过http://www.bayesil.ca访问BAYESIL。