MetNet: Metabolite Network Prediction from High-Resolution Mass Spectrometry Data in R Aiding Metabolite Annotation

A major
bottleneck of mass spectrometric metabolomic analysis is
still the rapid detection and annotation of unknown m/z features across biological matrices. This kind
of analysis is especially cumbersome for complex samples with hundreds
to thousands of unknown features. Traditionally, the annotation was
done manually imposing constraints in reproducibility and automatization.
Furthermore, different analysis tools are typically used at different
steps which requires parsing of data and changing of environments.
We present here MetNet, implemented in the R programming language
and available as an open-source package via the Bioconductor project.
MetNet, which is compatible with the output of the xcms/CAMERA suite,
uses the data-rich output of mass spectrometry metabolomics to putatively
link features on their relation to other features in the data set.
MetNet uses both structural and quantitative information on metabolomics
data for network inference and enables the annotation of unknown analytes.