Accurate, fully-automated NMR spectral profiling for metabolomics
Siamak Ravanbakhsh, Philip Liu, Trent Bjorndahl, Rupasri Mandal, Jason, R. Grant, Michael Wilson, Roman Eisner, Igor Sinelnikov, Xiaoyu Hu, Claudio, Luchinat, Russell Greiner, David S. Wishart

TL;DR
Bayesil is an automated tool that rapidly and accurately derives metabolic profiles from NMR spectra, matching expert-level performance and enabling high-throughput metabolomics in clinical research.
Contribution
This paper introduces Bayesil, the first fully-automatic system for quantitative NMR spectral profiling with high accuracy and speed, surpassing manual expert analysis.
Findings
Achieves ~90% correct metabolite identification
Quantifies metabolites with ~10% error
Completes analysis in under 5 minutes on a single CPU
Abstract
Many diseases cause significant changes to the concentrations of small molecules (aka metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile". This information can be extracted from a biofluid's NMR spectrum. Today, this is often done manually by trained human experts, which means this process is relatively slow, expensive and error-prone. This paper presents a tool, Bayesil, that can quickly, accurately and autonomously produce a complex biofluid's (e.g., serum or CSF) metabolic profile from a 1D1H NMR spectrum. 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. Many of these steps are novel algorithms and our matching step views spectral matching as an…
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