LAUSR

high resolution MS data directly onto pathways, (ii) cross-integration of genomic and proteomic data and (iii) metabolite identity verification via data-dependent MS/MS analysis, either separately or as part of the autonomous workflow5. Our multi-omic analysis tool uses embedded BioCyc4 and Uniprot6 databases to map user-uploaded gene and protein data onto the predicted metabolic pathways (Supplementary Fig. 1). Results can be viewed in table form or using the interactive Pathway Cloud plot (Fig. 1). Dysregulated pathways with greater percent overlap and statistical significance appear in the upper right of the cloud plot. Graph features can be clicked to view more information on overlapping gene, protein and metabolite data, with links to BioCyc, KEGG and METLIN. Important features can be readily identified, helping to decipher underlying biological mechanisms. Details on the pathway analysis and integrated omics workflow can be found in the Supplementary Methods. Data sharing is possible between collaborators and the public, and we encourage users to share their data in the XCMS Online community. To demonstrate metabolic pathway analysis and multiomic integration, we describe representative sample sets in the Supplementary Note, including metabolic pathway analysis using progenitor cell proliferation data and a bacterially induced corrosion study (Supplementary Fig. 2); proteomic integration with an aging study (Supplementary Fig. 3); transcriptomic and proteomic integration using a human colon cancer study (Supplementary Fig. 4 and Supplementary Table 1); a nitrate stress response study in sulfate-reducing bacteria (Supplementary Fig. 5) and a media stress response study in Escherichia coli (Supplementary Fig. 6 and Supplementary Table 2); and a cohort of 1,600 diabetes plasma samples (Supplementary Fig. 7), which helps illustrate the scalability of the cloud-based XCMS Online. Other notable tools providing pathway analysis and multi-omic integration include Galaxy-M7, Open MS from KNIME8 and MetaboAnalyst9. However, many of these tools still require separate preprocessing of tandem liquid chromatography—mass spectrometry data and are not fully integrated into a single program. Our workflow automatically maps metabolomic data directly onto pathways and integrates transcriptomics and proteomics for systems-wide interpretation in one cohesive platform. Additionally, metabolic network mapping is available based on the predictive activity network algorithm3 for analysis of metabolomic data only, with multi-omics networking in development. In the future, we will incorporate unique metabolic pathways and networks from other sources to provide more comprehensive biological resources.