1 research outputs found
Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology
Comprehensive
metabolomic data can be achieved using multiple orthogonal
separation and mass spectrometry (MS) analytical techniques. However,
drawing biologically relevant conclusions from this data and combining
it with additional layers of information collected by other omic technologies
present a significant bioinformatic challenge. To address this, a
data processing approach was designed to automate the comprehensive
prediction of dysregulated metabolic pathways/networks from multiple
data sources. The platform autonomously integrates multiple MS-based
metabolomics data types without constraints due to different sample
preparation/extraction, chromatographic separation, or MS detection
method. This multimodal analysis streamlines the extraction of biological
information from the metabolomics data as well as the contextualization
within proteomics and transcriptomics data sets. As a proof of concept,
this multimodal analysis approach was applied to a colorectal cancer
(CRC) study, in which complementary liquid chromatography–mass
spectrometry (LC–MS) data were combined with proteomic and
transcriptomic data. Our approach provided a highly resolved overview
of colon cancer metabolic dysregulation, with an average 17% increase
of detected dysregulated metabolites per pathway and an increase in
metabolic pathway prediction confidence. Moreover, 95% of the altered
metabolic pathways matched with the dysregulated genes and proteins,
providing additional validation at a systems level. The analysis platform
is currently available via the XCMS Online (XCMSOnline.scripps.edu)