![]() ![]() This problem may stem from the history of metabolomics, as its analytical techniques, such as nmr spectroscopy, gas-chromatography-mass spectrometry ( GC-MS) and liquid chromatography-mass spectrometry ( LC-MS), were originally developed for identifying and quantifying pure compounds, not complex mixtures. In other words, metabolomics is not yet automated. Compared to genomics, where it is now possible to automatically characterize 1000s of genes, 100s of thousands of transcripts and millions of SNPs in mere minutes, metabolomics only allows users to identify and measure a few dozen metabolites after many hours of manual effort. This rapid growth in interest and excitement surrounding metabolomics is also revealing its “Achilles heel”: Unlike proteomics, genomics or transcriptomics, which are high-throughput sciences, metabolomics is a relatively low-throughput science. It is also finding more applications in disease diagnosis, biomarker discovery and drug development/discovery. Because metabolomics provides a unique window on gene-environment interactions, it is playing an increasingly important role in many quantitative phenotyping and functional genomics studies. Metabolomics is often viewed as complementary to the other “omics” fields as it provides information about both an organism’s phenotype and its environment. Metabolomics is a relatively new branch of “omics” science that focuses on the system-wide characterization of small molecule metabolites and small molecule metabolism. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. 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. 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. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable 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. ![]() Given a 1D 1 H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person’s metabolic profile. ![]() 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 information can be extracted from a biofluids Nuclear Magnetic Resonance ( NMR) spectrum. 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. Many diseases cause significant changes to the concentrations of small molecules (a.k.a. ![]()
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