  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 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, show that Bayesil can autonomously find the concentration of
all NMR-detectable metabolites accurately (~90% correct identification and ~10%
quantification error), in <5minutes 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
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. Available at http://www.bayesil.ca.
