Predicting Network Activity from High Throughput Metabolomics

By Shuzhao Li, Youngja Park, Sai Duraisingham, Frederick H. Strobel, Nooruddin Khan, Quinlyn A. Soltow, Dean P. Jones, Bali Pulendran. PLoS Comput. Biol., 2013

846 citations recorded by the source corpus.

The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3fc36b24cd25cbd7b17f43c0cfb7b5c945b8f40c

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In Molecular Biology

Citations in the corpus

Metabolomics for Investigating Physiological and Pathophysiological Processes. (2019, methodology): “When combined with additional biological knowledge found in pathway databases (143, 219, 225) or when processed using tools such as metabolite set enrichment analysis (523, 524) and biological network modeling (272), these data can be used to gain key insights into how certain metabolites contribute”
MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics (2020, methodology): “The R implementation of mummichog [21] was described in the previous version [5].”
MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation (2024, methodology): “Functional analysis of untargeted metabolomics was initially established based on mummichog and Gene Set Enrichment Analysis (GSEA) since MetaboAnalyst 4.0 ( 58 ).”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, methodology): “The mummichog algo-rithm was an elegant and efficient implementation of this concept that enables direct prediction of pathway activities from high-resolution MS peaks, without performing accurate peak annotation upfront (15).”
Untargeted metabolomics strategies – Challenges and Emerging Directions (2016, methodology): “Levels 2 and 1 (Putative identification or Validation) Integration with known biology Pathway and network analyses MetaboAnalyst [40] Kyoto Encyclopedia of Genes and Genomes (KEGG) [41] Mbrole (Metabolite Biological Role) [42] MetaCyc/BioCyc [43]”
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights (2021, methodology): “0 further improved on integrative and reproducible analysis (15), and began to support functional interpretation of global metabolomics data (16).”
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis (2019, methodology): “Keep the pathway library selected to Homo sapiens (human) [MFN] library, which was manually curated by the original authors from a number of sources (KEGG, BiGG, and Edinburgh Model; Li et al., 2013).”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, methodology): “New pathway-based methods can incorporate the uncertainty of the metabolite annotation into the statistical analysis to identify the pathway modules associated with a complex disease (S. Li et al., 2013).”
MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data (2023, neutral): “Tw o well-established algorithms - mum-michog ( 33 ) and globaltest ( 34 ) are used to perform enrichment analysis for LC-MS peaks and other featur es, r e-specti v ely.”