Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network

Yao, Qianlan; Xu, Yanjun; Yang, Haixiu; Shang, Desi; Zhang, Chunlong; Zhang, Yunpeng; Sun, Zeguo; Shi, Xinrui; Li, Feng; Han, Junwei; Su, Fei; Li, Chunquan; Li, Xia

doi:10.1038/srep17201

Cited by 42 publications

(43 citation statements)

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“…(Kuhn, von Mering, Campillos, Jensen, & Bork, 2007), using only highly confident interactions. Finally, the metabolite-genedisease network is an integration of the gene-metabolite, metabolite-disease, and genedisease associations (Yao et al, 2015).…”

Section: Of 128mentioning

confidence: 99%

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Chong

Wishart

Xia

2019

CP in Bioinformatics

1,799

1,449

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MetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time‐series and two‐factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta‐analysis Basic Protocol 11: Knowledge‐based network exploration of multi‐omics data Basic Protocol 12: MetaboAnalystR introduction

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Section: Of 128mentioning

confidence: 99%

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Chong

Wishart

Xia

2019

CP in Bioinformatics

1,799

1,449

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“…The de novo prediction system MetabolitePredict is different from existing computation-based metabolite prediction systems [5,6], which identify disease metabolites based on known disease-associated metabolites and cannot perform predictions for diseases without known metabolites. Though we demonstrated that MetabolitePredict performs better than PROFANCY in prioritizing RA-associated metabolites, the de novo prediction system has its inherent limitation since it ignores our existing knowlege of disease-associated metabolites.…”

Section: Discussionmentioning

confidence: 99%

“…MetabolitePredict complements current clinical sample-based metabolomics studies: current human metabolomics characterize clinically significant metabolite profiles from patient samples; MetabolitePredict contextualizes disease metabolite biomarker discovery with vast amounts of existing system-level genetic and molecular data. MetabolitePredict is also different from existing computation-based metabolite prediction systems, including PROFANCY [5] and MetPriCNet [6], which identify additional disease metabolites based on known disease-associated metabolites, therefore cannot perform predictions for diseases without known metabolites. MetabolitePredict is a de novo prediction system that can predict metabolite biomarkers for any diseases without the need of known disease-associated metabolites.…”

Section: Introductionmentioning

confidence: 99%

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Wang¹,

2017

Journal of Biomedical Informatics

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Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved.MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known Ethics approval and consent to participate Not applicable.

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“…Yao et al integrated multi-omics data to construct a threelayered network model MetPriCNet, which consists of metabolite network, gene network, phenotype network, metabolite-phenotype network, metabolite-gene network, and gene-phenotype network (Yao et al, 2015). Afterwards, an RWR algorithm is applied to prioritize metabolites associated with diseases.…”

Section: Application Of Hmln In Omics Data Integration and Analysismentioning

confidence: 99%

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

et al. 2020

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Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-ofthe-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

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Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network

Cited by 42 publications

References 50 publications

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis

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