  Motivation: Untargeted metabolomics comprehensively characterizes small
molecules and elucidates activities of biochemical pathways within a biological
sample. Despite computational advances, interpreting collected measurements and
determining their biological role remains a challenge. Results: To interpret
measurements, we present an inference-based approach, termed Probabilistic
modeling for Untargeted Metabolomics Analysis (PUMA). Our approach captures
measurements and known information about the sample under study in a generative
model and uses stochastic sampling to compute posterior probability
distributions. PUMA predicts the likelihood of pathways being active, and then
derives a probabilistic annotation, which assigns chemical identities to the
measurements. PUMA is validated on synthetic datasets. When applied to test
cases, the resulting pathway activities are biologically meaningful and
distinctly different from those obtained using statistical pathway enrichment
techniques. Annotation results are in agreement to those obtained using other
tools that utilize additional information in the form of spectral signatures.
Importantly, PUMA annotates many additional measurements.
