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Raghav A, Jeong GB. Nanoquercetin and Extracellular Vesicles as Potential Anticancer Therapeutics in Hepatocellular Carcinoma. Cells 2024; 13:638. [PMID: 38607076 PMCID: PMC11011524 DOI: 10.3390/cells13070638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/01/2024] [Accepted: 04/04/2024] [Indexed: 04/13/2024] Open
Abstract
Despite world-class sophisticated technologies, robotics, artificial intelligence, and machine learning approaches, cancer-associated mortalities and morbidities have shown continuous increments posing a healthcare burden. Drug-based interventions were associated with systemic toxicities and several limitations. Natural bioactive compounds derived nanoformulations, especially nanoquercetin (nQ), are alternative options to overcome drug-associated limitations. Moreover, the EVs-based cargo targeted delivery of nQ can have enormous potential in treating hepatocellular carcinoma (HCC). EVs-based nQ delivery synergistically regulates and dysregulates several pathways, including NF-κB, p53, JAK/STAT, MAPK, Wnt/β-catenin, and PI3K/AKT, along with PBX3/ERK1/2/CDK2, and miRNAs intonation. Furthermore, discoveries on possible checkpoints of anticancer signaling pathways were studied, which might lead to the development of modified EVs infused with nQ for the development of innovative treatments for HCC. In this work, we abridged the control of such signaling systems using a synergetic strategy with EVs and nQ. The governing roles of extracellular vesicles controlling the expression of miRNAs were investigated, particularly in relation to HCC.
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Affiliation(s)
| | - Goo Bo Jeong
- Department of Anatomy and Cell Biology, College of Medicine, Gachon University, 155 Getbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea;
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2
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Balestra C, Maj C, Müller E, Mayr A. Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets. PLoS One 2023; 18:e0282699. [PMID: 36893181 PMCID: PMC9997904 DOI: 10.1371/journal.pone.0282699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 02/13/2023] [Indexed: 03/10/2023] Open
Abstract
In Genetics, gene sets are grouped in collections concerning their biological function. This often leads to high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation of their biological meaning. In Data Mining, it is often argued that techniques to reduce the dimensionality of data could increase the maneuverability and consequently the interpretability of large data. In the past years, moreover, we witnessed an increasing consciousness of the importance of understanding data and interpretable models in the machine learning and bioinformatics communities. On the one hand, there exist techniques aiming to aggregate overlapping gene sets to create larger pathways. While these methods could partly solve the large size of the collections' problem, modifying biological pathways is hardly justifiable in this biological context. On the other hand, the representation methods to increase interpretability of collections of gene sets that have been proposed so far have proved to be insufficient. Inspired by this Bioinformatics context, we propose a method to rank sets within a family of sets based on the distribution of the singletons and their size. We obtain sets' importance scores by computing Shapley values; Making use of microarray games, we do not incur the typical exponential computational complexity. Moreover, we address the challenge of constructing redundancy-aware rankings where, in our case, redundancy is a quantity proportional to the size of intersections among the sets in the collections. We use the obtained rankings to reduce the dimension of the families, therefore showing lower redundancy among sets while still preserving a high coverage of their elements. We finally evaluate our approach for collections of gene sets and apply Gene Sets Enrichment Analysis techniques to the now smaller collections: As expected, the unsupervised nature of the proposed rankings allows for unremarkable differences in the number of significant gene sets for specific phenotypic traits. In contrast, the number of performed statistical tests can be drastically reduced. The proposed rankings show a practical utility in bioinformatics to increase interpretability of the collections of gene sets and a step forward to include redundancy-awareness into Shapley values computations.
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Affiliation(s)
- Chiara Balestra
- Department of Computer Science, TU Dortmund, Dortmund, Germany
- Department of Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany
- * E-mail:
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics IGSB, University Hospital Bonn, Bonn, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Emmanuel Müller
- Department of Computer Science, TU Dortmund, Dortmund, Germany
| | - Andreas Mayr
- Department of Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Bonn, Germany
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Mubeen S, Hoyt CT, Gemünd A, Hofmann-Apitius M, Fröhlich H, Domingo-Fernández D. The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Front Genet 2019; 10:1203. [PMID: 31824580 PMCID: PMC6883970 DOI: 10.3389/fgene.2019.01203] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 10/30/2019] [Indexed: 02/04/2023] Open
Abstract
Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.
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Affiliation(s)
- Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - André Gemünd
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Holger Fröhlich
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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4
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Mubeen S, Hoyt CT, Gemünd A, Hofmann-Apitius M, Fröhlich H, Domingo-Fernández D. The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Front Genet 2019. [PMID: 31824580 DOI: 10.3389/fgene.2019.01203/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023] Open
Abstract
Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.
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Affiliation(s)
- Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - André Gemünd
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Holger Fröhlich
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Domingo-Fernández D, Hoyt CT, Bobis-Álvarez C, Marín-Llaó J, Hofmann-Apitius M. ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases. NPJ Syst Biol Appl 2018; 5:3. [PMID: 30564458 PMCID: PMC6292919 DOI: 10.1038/s41540-018-0078-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 10/31/2018] [Accepted: 11/02/2018] [Indexed: 11/09/2022] Open
Abstract
Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson’s disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.
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Affiliation(s)
- Daniel Domingo-Fernández
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Charles Tapley Hoyt
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Carlos Bobis-Álvarez
- 3Faculty of Medicine and Health Sciences, University of Oviedo, 33006 Oviedo, Spain
| | - Josep Marín-Llaó
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,4Rovira i Virgili University, 43003 Tarragona, Spain
| | - Martin Hofmann-Apitius
- 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, 53754 Sankt Augustin, Germany.,2Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
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Stoney RA, Schwartz JM, Robertson DL, Nenadic G. Using set theory to reduce redundancy in pathway sets. BMC Bioinformatics 2018; 19:386. [PMID: 30340461 PMCID: PMC6194563 DOI: 10.1186/s12859-018-2355-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 08/31/2018] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The consolidation of pathway databases, such as KEGG, Reactome and ConsensusPathDB, has generated widespread biological interest, however the issue of pathway redundancy impedes the use of these consolidated datasets. Attempts to reduce this redundancy have focused on visualizing pathway overlap or merging pathways, but the resulting pathways may be of heterogeneous sizes and cover multiple biological functions. Efforts have also been made to deal with redundancy in pathway data by consolidating enriched pathways into a number of clusters or concepts. We present an alternative approach, which generates pathway subsets capable of covering all of genes presented within either pathway databases or enrichment results, generating substantial reductions in redundancy. RESULTS We propose a method that uses set cover to reduce pathway redundancy, without merging pathways. The proposed approach considers three objectives: removal of pathway redundancy, controlling pathway size and coverage of the gene set. By applying set cover to the ConsensusPathDB dataset we were able to produce a reduced set of pathways, representing 100% of the genes in the original data set with 74% less redundancy, or 95% of the genes with 88% less redundancy. We also developed an algorithm to simplify enrichment data and applied it to a set of enriched osteoarthritis pathways, revealing that within the top ten pathways, five were redundant subsets of more enriched pathways. Applying set cover to the enrichment results removed these redundant pathways allowing more informative pathways to take their place. CONCLUSION Our method provides an alternative approach for handling pathway redundancy, while ensuring that the pathways are of homogeneous size and gene coverage is maximised. Pathways are not altered from their original form, allowing biological knowledge regarding the data set to be directly applicable. We demonstrate the ability of the algorithms to prioritise redundancy reduction, pathway size control or gene set coverage. The application of set cover to pathway enrichment results produces an optimised summary of the pathways that best represent the differentially regulated gene set.
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Affiliation(s)
| | - Jean-Marc Schwartz
- School of Biological Sciences, University of Manchester, Manchester, M13 9PT UK
| | - David L Robertson
- School of Biological Sciences, University of Manchester, Manchester, M13 9PT UK
- MRC-University of Glasgow Centre for Virus Research, Garscube Campus, Glasgow, G61 1QH UK
| | - Goran Nenadic
- School of Computer Science, University of Manchester, Manchester, M13 9PL UK
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN UK
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Van Laere S, Dirix L, Vermeulen P. Molecular profiles to biology and pathways: a systems biology approach. CHINESE JOURNAL OF CANCER 2016; 35:53. [PMID: 27311441 PMCID: PMC4910225 DOI: 10.1186/s40880-016-0112-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 05/25/2016] [Indexed: 01/02/2023]
Abstract
Interpreting molecular profiles in a biological context requires specialized analysis strategies. Initially, lists of relevant genes were screened to identify enriched concepts associated with pathways or specific molecular processes. However, the shortcoming of interpreting gene lists by using predefined sets of genes has resulted in the development of novel methods that heavily rely on network-based concepts. These algorithms have the advantage that they allow a more holistic view of the signaling properties of the condition under study as well as that they are suitable for integrating different data types like gene expression, gene mutation, and even histological parameters.
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Affiliation(s)
- Steven Van Laere
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Oosterveldlaan 24, Wilrijk, 2610 Antwerp, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Oosterveldlaan 24, Wilrijk, 2610 Antwerp, Belgium
| | - Peter Vermeulen
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Oosterveldlaan 24, Wilrijk, 2610 Antwerp, Belgium
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Belinky F, Nativ N, Stelzer G, Zimmerman S, Iny Stein T, Safran M, Lancet D. PathCards: multi-source consolidation of human biological pathways. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav006. [PMID: 25725062 PMCID: PMC4343183 DOI: 10.1093/database/bav006] [Citation(s) in RCA: 180] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths’ capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource.Database URL:http://pathcards.genecards.org/
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Affiliation(s)
- Frida Belinky
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noam Nativ
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Gil Stelzer
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Shahar Zimmerman
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tsippi Iny Stein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Marilyn Safran
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Doron Lancet
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
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Laukens K, Naulaerts S, Berghe WV. Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis. Proteomics 2015; 15:981-96. [PMID: 25430566 DOI: 10.1002/pmic.201400296] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 10/16/2014] [Accepted: 11/24/2014] [Indexed: 12/24/2022]
Abstract
The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.
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Affiliation(s)
- Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan, Antwerp, Belgium; Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp / Antwerp University Hospital, Antwerp, Belgium
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Chen YA, Tripathi LP, Dessailly BH, Nyström-Persson J, Ahmad S, Mizuguchi K. Integrated pathway clusters with coherent biological themes for target prioritisation. PLoS One 2014; 9:e99030. [PMID: 24918583 PMCID: PMC4053319 DOI: 10.1371/journal.pone.0099030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 05/07/2014] [Indexed: 12/15/2022] Open
Abstract
Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.
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Affiliation(s)
- Yi-An Chen
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
| | | | | | | | - Shandar Ahmad
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
| | - Kenji Mizuguchi
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
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