SLAW: A scalable and self-optimizing processing workflow foruntargeted LC-MS

Open access
Date
2021-11-16Type
- Journal Article
Abstract
Metabolomics has been shown to be promising for diverse
applications in basic, applied, and clinical research. These
applications often require large-scale data, and while the
technology to perform such experiments exists, downstream
analysis remains challenging. Different tools exist in a variety
of ecosystems, but they often do not scale to large data and are
not integrated into a single coherent workflow. Moreover, the
outcome of processing is very sensitive to a multitude of
algorithmic parameters. Hence, parameter optimization is not
only critical but also challenging. We present SLAW, a scalable
and yet easy-to-use workflow for processing untargeted LC-MS
data in metabolomics and lipidomics. The capabilities of SLAW
include (1) state-of-the-art peak-picking algorithms, (2) a new
automated parameter optimization routine, (3) an efficient
sample alignment procedure, (4) gap filling by data recursion,
and (5) the extraction of consolidated MS2 and an isotopic
pattern across all samples. Importantly, both the workflow and
the parameter optimization were designed for robust analysis of
untargeted studies with thousands of individual LC-MSn runs. We
compared SLAW to two state-of-the-art workflows based on openMS
and XCMS. SLAW was able to detect and align more reproducible
features in all data sets considered. SLAW scaled well, and its
analysis of a data set with 2500 LC-MS files consumed 40% less
memory and was 6 times faster than that using the XCMS-based
workflow. SLAW also extracted 2-fold more isotopic patterns and
MS2 spectra, which in 60% of the cases led to positive matches
against a spectral library. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000526884Publication status
publishedExternal links
Journal / series
Analytical ChemistryVolume
Pages / Article No.
Publisher
American Chemical SocietyOrganisational unit
08839 - Zamboni, Nicola (Tit.-Prof.)
More
Show all metadata