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Relevant bibliographies by topics / Metabolite set enrichment analysis

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Academic literature on the topic 'Metabolite set enrichment analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Metabolite set enrichment analysis"

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Deng, Lingli, Lei Ma, Kian-Kai Cheng, Xiangnan Xu, Daniel Raftery, and Jiyang Dong. "Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis." Journal of Proteome Research 20, no. 6 (May 18, 2021): 3204–13. http://dx.doi.org/10.1021/acs.jproteome.1c00064.

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Martins, Raquel G., Luís G. Gonçalves, Nuno Cunha, and Maria João Bugalho. "Metabolomic Urine Profile: Searching for New Biomarkers of SDHx-Associated Pheochromocytomas and Paragangliomas." Journal of Clinical Endocrinology & Metabolism 104, no. 11 (July 23, 2019): 5467–77. http://dx.doi.org/10.1210/jc.2019-01101.

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Abstract Context Metabolomic studies of pheochromocytoma and paraganglioma tissue showed a correlation between metabolomic profile and presence of SDHx mutations, especially a pronounced increase of succinate. Objective To compare the metabolomic profile of 24-hour urine samples of SDHx mutation carriers with tumors (affected mutation carriers), without tumors (asymptomatic mutation carriers), and patients with sporadic pheochromocytomas and paragangliomas. Methods Proton nuclear magnetic resonance spectroscopic profiling of urine samples and metabolomic analysis using pairwise comparisons were complemented by metabolite set enrichment analysis to identify meaningful patterns. Results The urine of the affected SDHx carriers showed substantially lower levels of seven metabolites than the urine of asymptomatic mutation carriers (including, succinate and N-acetylaspartate). The urine of patients with SDHx-associated tumors presented substantially higher levels of three metabolites compared with the urine of patients without mutation; the metabolite set enrichment analysis identified gluconeogenesis, pyruvate, and aspartate metabolism as the pathways that most probably explained the differences found. N-acetylaspartate was the only metabolite the urinary levels of which were significantly different between the three groups. Conclusions The metabolomic urine profile of the SDHx mutation carriers with tumors is different from that of asymptomatic carriers and from that of patients with sporadic neoplasms. Differences are likely to reflect the altered mitochondria energy production and pseudohypoxia signature of these tumors. The urinary levels of N-acetylaspartate and succinate contrast with those reported in tumor tissue, suggesting a defective washout process of oncometabolites in association with tumorigenesis. The role of N-acetylaspartate as a tumor marker for these tumors merits further investigation.

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Chandler, Paulette D., Raji Balasubramanian, Nina Paynter, Franco Giulianini, Teresa Fung, Lesley F. Tinker, Linda Snetselaar, et al. "Metabolic signatures associated with Western and Prudent dietary patterns in women." American Journal of Clinical Nutrition 112, no. 2 (June 10, 2020): 268–83. http://dx.doi.org/10.1093/ajcn/nqaa131.

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ABSTRACT Background The Western dietary pattern (WD) is positively associated with risk of coronary artery disease (CAD) and cancer, whereas the Prudent dietary pattern (PD) may be protective. Foods may influence metabolite concentrations as well as oxidative stress and lipid dysregulation, biological mechanisms associated with CAD and cancer. Objective The aim was to assess the association of 2 derived dietary pattern scores with serum metabolites and identify metabolic pathways associated with the metabolites. Methods We evaluated the cross-sectional association between each dietary pattern (WD, PD) and metabolites in 2199 Women's Health Initiative (WHI) participants. With FFQ and factor analysis, we determined 2 dietary patterns consistent with WD and PD. Metabolites were measured with LC–tandem MS. Metabolite discovery among 904 WHI Observational Study (WHI-OS) participants was replicated among 1295 WHI Hormone Therapy Trial (WHI-HT) participants. We analyzed each of 495 metabolites with each dietary score (WD, PD) in linear regression models. Results The PD included higher vegetables and fruit intake compared with the WD with higher saturated fat and meat intake. Independent of energy intake, BMI, physical activity, and other confounding variables, 45 overlapping metabolites were identified (WHI-OS) and replicated (WHI-HT) with an opposite direction of associations for the WD compared with the PD [false discovery rate (FDR) P < 0.05]. In metabolite set enrichment analyses, phosphatidylethanolamine (PE) plasmalogens were positively enriched for association with WD [normalized enrichment score (NES) = 2.01, P = 0.001, FDR P = 0.005], and cholesteryl esters (NES = −1.77, P = 0.005, FDR P = 0.02), and phosphatidylcholines (NES = −1.72, P = 0.01, P = 0.03) were negatively enriched for WD. PE plasmalogens were positively correlated with saturated fat and red meat. Phosphatidylcholines and cholesteryl esters were positively correlated with fatty fish. Conclusions Distinct metabolite signatures associated with Western and Prudent dietary patterns highlight the positive association of mitochondrial oxidative stress and lipid dysregulation with a WD and the inverse association with a PD.

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Fan, Sili, Muhammad Shahid, Peng Jin, Arash Asher, and Jayoung Kim. "Identification of Metabolic Alterations in Breast Cancer Using Mass Spectrometry-Based Metabolomic Analysis." Metabolites 10, no. 4 (April 24, 2020): 170. http://dx.doi.org/10.3390/metabo10040170.

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Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were further analyzed to identify differentially expressed metabolites in BC and their associated metabolic networks. Additionally, Chemical Similarity Enrichment Analysis (ChemRICH), MetaMapp, and Metabolite Set Enrichment Analysis (MSEA) identified significantly enriched clusters and networks in BC tissues. Since metabolomic signatures hold significant promise in the clinical setting, more effort should be placed on validating potential BC biomarkers based on identifying altered metabolomes.

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Isserlin, Ruth, Daniele Merico, Veronique Voisin, and Gary D. Bader. "Enrichment Map – a Cytoscape app to visualize and explore OMICs pathway enrichment results." F1000Research 3 (July 1, 2014): 141. http://dx.doi.org/10.12688/f1000research.4536.1.

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High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.

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McLuskey, Karen, Joe Wandy, Isabel Vincent, Justin J. J. van der Hooft, Simon Rogers, Karl Burgess, and Rónán Daly. "Ranking Metabolite Sets by Their Activity Levels." Metabolites 11, no. 2 (February 11, 2021): 103. http://dx.doi.org/10.3390/metabo11020103.

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Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.

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Lokhov, Petr G., Elena E. Balashova, Oxana P. Trifonova, Dmitry L. Maslov, Elena A. Ponomarenko, and Alexander I. Archakov. "Mass Spectrometry-Based Metabolomics Analysis of Obese Patients’ Blood Plasma." International Journal of Molecular Sciences 21, no. 2 (January 15, 2020): 568. http://dx.doi.org/10.3390/ijms21020568.

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Scientists currently use only a small portion of the information contained in the blood metabolome. The identification of metabolites is a huge challenge because only highly abundant and well-separated compounds can be easily identified in complex samples. However, new approaches that enhance the identification of compounds have emerged; among them, the identification of compounds based on their involvement in a particular biological context is a recent development. In this work, this approach was first applied to identify metabolites in complex samples and, together with metabolite set enrichment analysis, was used for the evaluation of blood plasma from obese patients. The proposed approach was found to provide a statistically sound overview of the biochemical pathways, thus presenting additional information on obesity. Obesity progression was demonstrated to be accompanied by marked alterations in steroidogenesis, androstenedione metabolism, and androgen and estrogen metabolism. The findings of this study suggest that the workflow used for blood analysis is sufficient to demonstrate obesity at the biochemical pathway level as well as to monitor the response to treatment. This workflow is also expected to be suitable for studying other metabolic diseases.

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Tietz-Bogert, Pamela, Minsuk Kim, Angela Cheung, James Tabibian, Julie Heimbach, Charles Rosen, Madhumitha Nandakumar, et al. "Metabolomic Profiling of Portal Blood and Bile Reveals Metabolic Signatures of Primary Sclerosing Cholangitis." International Journal of Molecular Sciences 19, no. 10 (October 16, 2018): 3188. http://dx.doi.org/10.3390/ijms19103188.

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Primary sclerosing cholangitis (PSC) is a pathogenically complex, chronic, fibroinflammatory disorder of the bile ducts without known etiology or effective pharmacotherapy. Emerging in vitro and in vivo evidence support fundamental pathophysiologic mechanisms in PSC centered on enterohepatic circulation. To date, no studies have specifically interrogated the chemical footprint of enterohepatic circulation in PSC. Herein, we evaluated the metabolome and lipidome of portal venous blood and bile obtained at the time of liver transplantation in patients with PSC (n = 7) as compared to individuals with noncholestatic, end-stage liver disease (viral, metabolic, etc. (disease control, DC, n = 19)) and to nondisease controls (NC, living donors, n = 12). Global metabolomic and lipidomic profiling was performed on serum derived from portal venous blood (portal serum) and bile using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and differential mobility spectroscopy-mass spectroscopy (DMS-MS; complex lipid platform). The Mann–Whitney U test was used to identify metabolites that significantly differed between groups. Principal-component analysis (PCA) showed significant separation of both PSC and DC from NC for both portal serum and bile. Metabolite set enrichment analysis of portal serum and bile demonstrated that the liver-disease cohorts (PSC and DC) exhibited similar enrichment in several metabolite categories compared to NC. Interestingly, the bile in PSC was uniquely enriched for dipeptide and polyamine metabolites. Finally, analysis of patient-matched portal serum and biliary metabolome revealed that these biological fluids were more homogeneous in PSC than in DC or NC, suggesting aberrant bile formation and enterohepatic circulation. In summary, PSC and DC patients exhibited alterations in several metabolites in portal serum and bile, while PSC patients exhibited a unique bile metabolome. These specific alterations in PSC are amenable to hypothesis testing and, potentially, therapeutic pharmacologic manipulation.

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Persicke, Marcus, Christian Rückert, Jens Plassmeier, Leonhardt Jonathan Stutz, Nikolas Kessler, Jörn Kalinowski, Alexander Goesmann, and Heiko Neuweger. "MSEA: metabolite set enrichment analysis in the MeltDB metabolomics software platform: metabolic profiling of Corynebacterium glutamicum as an example." Metabolomics 8, no. 2 (May 1, 2011): 310–22. http://dx.doi.org/10.1007/s11306-011-0311-6.

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Dwivedi, Prarambh SR, V. P. Rasal, Ekta Kotharkar, Shailaja Nare, and Pukar Khanal. "Gene set enrichment analysis of PPAR-γ regulators from Murraya odorata Blanco." Journal of Diabetes & Metabolic Disorders 20, no. 1 (February 17, 2021): 369–75. http://dx.doi.org/10.1007/s40200-021-00754-x.

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Dissertations / Theses on the topic "Metabolite set enrichment analysis"

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Ried, Janina S. "Phenotype set enrichment analysis." Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-158079.

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Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." BioMed Central, 2010. https://tud.qucosa.de/id/qucosa%3A28875.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

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Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-176848.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

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Li, Wei. "Analyzing Gene Expression Data in Terms of Gene Sets: Gene Set Enrichment Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/79.

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The DNA microarray biotechnology simultaneously monitors the expression of thousands of genes and aims to identify genes that are differently expressed under different conditions. From the statistical point of view, it can be restated as identify genes strongly associated with the response or covariant of interest. The Gene Set Enrichment Analysis (GSEA) method is one method which focuses the analysis at the functional related gene sets level instead of single genes. It helps biologists to interpret the DNA microarray data by their previous biological knowledge of the genes in a gene set. GSEA has been shown to efficiently identify gene sets containing known disease-related genes in the real experiments. Here we want to evaluate the statistical power of this method by simulation studies. The results show that the the power of GSEA is good enough to identify the gene sets highly associated with the response or covariant of interest.

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Kodysh, Yuliya. "Using co-expression to redefine functional gene sets for gene set enrichment analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41661.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 89-90).
Manually curated gene sets related to a biological function often contain genes that are not tightly co-regulated transcriptionally. which obscures the evidence of coordinated differential expression of these gene sets in relevant experiments. To address this problem, we explored strategies to refine the manually curated subcollection of the Molecular Signatures Database (MSigDB) for use with Gene Set Enrichment Analysis (GSEA). We examined the manually curated gene sets in context of an atlas of gene expression of many normal human tissues. To refine gene sets, we clustered the genes in each set based on co-expression across the tissues to produce more tightly co-regulated children gene sets that are also likely more accurate representations of the biological process or processes described by the gene set. We evaluated the performance of the clustering algorithms by refining gene sets in the context of several published GSEA analyses and verifying that the children gene sets score higher with GSEA than do the parents. We created and annotated a new, refined version of a large portion of the manually curated component of MSigDB, which we hope will be a resource for the GSEA community.
by Yuliya Kodysh.
M.Eng.

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Jadhav, Trishul. "Knowledge Based Gene Set analysis (KB-GSA) : A novel method for gene expression analysis." Thesis, University of Skövde, School of Life Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4352.

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Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

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Ried, Janina S. [Verfasser], and H. Erich [Akademischer Betreuer] Wichmann. "Phenotype set enrichment analysis : genome wide analysis of multiple phenotypes / Janina S. Ried. Betreuer: H.-Erich Wichmann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2013. http://d-nb.info/1036836894/34.

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SARTOR, MAUREEN A. "TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

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Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.

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Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term.
Master of Science

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Hänzelmann, Sonja 1981. "Pathway-centric approaches to the analysis of high-throughput genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/108337.

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In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodologies focused on extracting information at the pathway rather than the single gene level. First, we developed Gene Set Variation Analysis (GSVA), a method that facilitates the organization and condensation of gene expression proﬁles into gene sets. GSVA enables pathway-centric downstream analyses of microarray and RNA-seq gene expression data. The method estimates sample-wise pathway variation over a population and allows for the integration of heterogeneous biological data sources with pathway-level expression measurements. To illustrate the features of GSVA, we applied it to several use-cases employing diﬀerent data types and addressing biological questions. GSVA is made available as an R package within the Bioconductor project. Secondly, we developed a pathway-centric genome-based strategy to reposition drugs in type 2 diabetes (T2D). This strategy consists of two steps, ﬁrst a regulatory network is constructed that is used to identify disease driving modules and then these modules are searched for compounds that might target them. Our strategy is motivated by the observation that disease genes tend to group together in the same neighborhood forming disease modules and that multiple genes might have to be targeted simultaneously to attain an eﬀect on the pathophenotype. To ﬁnd potential compounds, we used compound exposed genomics data deposited in public databases. We collected about 20,000 samples that have been exposed to about 1,800 compounds. Gene expression can be seen as an intermediate phenotype reﬂecting underlying dysregulatory pathways in a disease. Hence, genes contained in the disease modules that elicit similar transcriptional responses upon compound exposure are assumed to have a potential therapeutic eﬀect. We applied the strategy to gene expression data of human islets from diabetic and healthy individuals and identiﬁed four potential compounds, methimazole, pantoprazole, bitter orange extract and torcetrapib that might have a positive eﬀect on insulin secretion. This is the ﬁrst time a regulatory network of human islets has been used to reposition compounds for T2D. In conclusion, this thesis contributes with two pathway-centric approaches to important bioinformatic problems, such as the assessment of biological function and in silico drug repositioning. These contributions demonstrate the central role of pathway-based analyses in interpreting high-throughput genomics data.
En l'última dècada, la biologia molecular ha evolucionat des d'una perspectiva reduccionista cap a una perspectiva a nivell de sistemes que intenta desxifrar les complexes interaccions entre els components cel•lulars. Amb l'aparició de les tecnologies d'alt rendiment actualment és possible interrogar genomes sencers amb una resolució sense precedents. La dimensió i la naturalesa desestructurada d'aquestes dades ha posat de manifest la necessitat de desenvolupar noves eines i metodologies per a convertir aquestes dades en coneixement biològic. Per contribuir a aquest repte hem explotat l'abundància de dades genòmiques procedents d'instruments d'alt rendiment i disponibles públicament, i hem desenvolupat mètodes bioinformàtics focalitzats en l'extracció d'informació a nivell de via molecular en comptes de fer-ho al nivell individual de cada gen. En primer lloc, hem desenvolupat GSVA (Gene Set Variation Analysis), un mètode que facilita l'organització i la condensació de perfils d'expressió dels gens en conjunts. GSVA possibilita anàlisis posteriors en termes de vies moleculars amb dades d'expressió gènica provinents de microarrays i RNA-seq. Aquest mètode estima la variació de les vies moleculars a través d'una població de mostres i permet la integració de fonts heterogènies de dades biològiques amb mesures d'expressió a nivell de via molecular. Per il•lustrar les característiques de GSVA, l'hem aplicat a diversos casos usant diferents tipus de dades i adreçant qüestions biològiques. GSVA està disponible com a paquet de programari lliure per R dins el projecte Bioconductor. En segon lloc, hem desenvolupat una estratègia centrada en vies moleculars basada en el genoma per reposicionar fàrmacs per la diabetis tipus 2 (T2D). Aquesta estratègia consisteix en dues fases: primer es construeix una xarxa reguladora que s'utilitza per identificar mòduls de regulació gènica que condueixen a la malaltia; després, a partir d'aquests mòduls es busquen compostos que els podrien afectar. La nostra estratègia ve motivada per l'observació que els gens que provoquen una malaltia tendeixen a agrupar-se, formant mòduls patogènics, i pel fet que podria caldre una actuació simultània sobre múltiples gens per assolir un efecte en el fenotipus de la malaltia. Per trobar compostos potencials, hem usat dades genòmiques exposades a compostos dipositades en bases de dades públiques. Hem recollit unes 20.000 mostres que han estat exposades a uns 1.800 compostos. L'expressió gènica es pot interpretar com un fenotip intermedi que reflecteix les vies moleculars desregulades subjacents a una malaltia. Per tant, considerem que els gens d'un mòdul patològic que responen, a nivell transcripcional, d'una manera similar a l'exposició del medicament tenen potencialment un efecte terapèutic. Hem aplicat aquesta estratègia a dades d'expressió gènica en illots pancreàtics humans corresponents a individus sans i diabètics, i hem identificat quatre compostos potencials (methimazole, pantoprazole, extracte de taronja amarga i torcetrapib) que podrien tenir un efecte positiu sobre la secreció de la insulina. Aquest és el primer cop que una xarxa reguladora d'illots pancreàtics humans s'ha utilitzat per reposicionar compostos per a T2D. En conclusió, aquesta tesi aporta dos enfocaments diferents en termes de vies moleculars a problemes bioinformàtics importants, com ho son el contrast de la funció biològica i el reposicionament de fàrmacs "in silico". Aquestes contribucions demostren el paper central de les anàlisis basades en vies moleculars a l'hora d'interpretar dades genòmiques procedents d'instruments d'alt rendiment.

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Book chapters on the topic "Metabolite set enrichment analysis"

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Gentleman, R., M. Morgan, and W. Huber. "Gene Set Enrichment Analysis." In Bioconductor Case Studies, 193–205. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77240-0_13.

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Tilford, Charles A., and Nathan O. Siemers. "Gene Set Enrichment Analysis." In Methods in Molecular Biology, 99–121. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-175-2_6.

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Hung, Jui-Hung. "Gene Set/Pathway Enrichment Analysis." In Methods in Molecular Biology, 201–13. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-107-3_13.

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Stiglic, Gregor. "Gene Set Enrichment Meta-Learning Analysis." In Encyclopedia of the Sciences of Learning, 1344–46. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1755.

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Falcon, S., and R. Gentleman. "Hypergeometric Testing Used for Gene Set Enrichment Analysis." In Bioconductor Case Studies, 207–20. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77240-0_14.

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Bayá, Ariel E., Mónica G. Larese, Pablo M. Granitto, Juan Carlos Gómez, and Elizabeth Tapia. "Gene Set Enrichment Analysis Using Non-parametric Scores." In Advances in Bioinformatics and Computational Biology, 12–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73731-5_2.

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Zhu, Min, Xiaolai Li, Shujie Wang, Wei Guo, and Xueling Li. "Characterization of Radiotherapy Sensitivity Genes by Comparative Gene Set Enrichment Analysis." In Intelligent Computing Theories and Application, 205–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_25.

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Karagiannaki, Ioulia, Yannis Pantazis, Ekaterini Chatzaki, and Ioannis Tsamardinos. "Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data." In Discovery Science, 246–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_17.

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Abstract:

Abstract Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://github.com/mensxmachina/PASL.

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"Gene Set Enrichment Analysis." In Encyclopedia of Systems Biology, 806. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_100552.

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"GSEA (gene set enrichment analysis)." In Encyclopedia of Genetics, Genomics, Proteomics and Informatics, 827. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6754-9_7187.

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Conference papers on the topic "Metabolite set enrichment analysis"

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Clark, Neil R., Maciej Szymkiewicz, Zichen Wang, Caroline D. Monteiro, Matthew R. Jones, and Avi Ma'ayan. "Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359689.

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Yidong Chen, Fan Yang, and Paul S. Meltzer. "Application of gene set enrichment method to ChIP-chip data analysis." In 2008 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2008. http://dx.doi.org/10.1109/gensips.2008.4555684.

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WANG, YONGJIA, STANLEY J. WATSON, and FAN MENG. "EXPLORING IMPORTANT ISSUES IN THE IMPLEMENTATION OF GENE SET ENRICHMENT ANALYSIS." In Proceedings of the International Conference. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702098_0007.

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Kim, Jaeyoung, Hyungmin Lee, and Miyoung Shin. "Identifying Biologically Significant Pathways by Gene Set Enrichment Analysis Using Fisher's Criterion." In 2008 Second International Conference on Future Generation Communication and Networking (FGCN). IEEE, 2008. http://dx.doi.org/10.1109/fgcn.2008.212.

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Praveen Kumar, A., AJ Kovatich, A. Biancotto, F. Cheung, JK Davidson-Moncada, L. Kvecher, J. Liu, et al. "Abstract P4-09-14: Analysis of breast cancer recurrence using gene set enrichment analysis." In Abstracts: 2017 San Antonio Breast Cancer Symposium; December 5-9, 2017; San Antonio, Texas. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.sabcs17-p4-09-14.

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Jones, Matthew R. "Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool." In Abstracts: AACR Special Conference: Computational and Systems Biology of Cancer; February 8-11, 2015; San Francisco, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.compsysbio-b1-35.

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Kumar, Ashwani, and Tiratha Raj Singh. "Systems biology approach for gene set enrichment and topological analysis of Alzheimer's disease pathway." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552132.

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Mpindi, John Patrick, Dimitry Bychkov, Yadav Bhagwan, Disha Malani, Hirasawa Akira, Khalid Saeed, Susanne Hultsch, et al. "Abstract 4184: Drug set enrichment analysis : A computational approach to identify functional drug sets." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-4184.

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Gebczynska, Magdalena. "LEADERSHIP STYLE, ORGANIZATIONAL COMMITMENT, WORK FAMILY ENRICHMENT AND AUTONOMY AS PREDICTORS OF EMPLOYEE JOB SATISFACTION. A FUZZY � SET ANALYSIS." In 5th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/1.5/s05.077.

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Wenzel, Alexander T., Devora Champa, Stephen B. Howell, Jill P. Mesirov, and Olivier Harismendy. "Abstract 4411: A gene set enrichment analysis approach in single-cells along pseudotime trajectories reveals the dynamic activity of oncogenic pathways." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-4411.

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