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Chaudhuri R, Krycer JR, Fazakerley DJ, Fisher-Wellman KH, Su Z, Hoehn KL, Yang JYH, Kuncic Z, Vafaee F, James DE. The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes. Sci Rep 2018; 8:1774. [PMID: 29379070 PMCID: PMC5789081 DOI: 10.1038/s41598-018-20104-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 01/12/2018] [Indexed: 02/06/2023] Open
Abstract
Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found > 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level ‘trans-omic’ approach when studying time-resolved responses to cellular perturbations.
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Affiliation(s)
- Rima Chaudhuri
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - James R Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel J Fazakerley
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Zhiduan Su
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Kyle L Hoehn
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Jean Yee Hwa Yang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Zdenka Kuncic
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.,School of Physics and Australian Institute for Nanoscale Science and Technology, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW, 2052, Australia.
| | - David E James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. .,School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia. .,Sydney Medical School, The University of Sydney, Sydney, NSW, 2006, Australia.
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52
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A Two-Stage Whole-Genome Gene Expression Association Study of Young-Onset Hypertension in Han Chinese Population of Taiwan. Sci Rep 2018; 8:1800. [PMID: 29379041 PMCID: PMC5789005 DOI: 10.1038/s41598-018-19520-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 01/03/2018] [Indexed: 12/31/2022] Open
Abstract
Hypertension is an important public health problem in the world. Since the intermediate position of the gene expression between genotype and phenotype makes it suitable to link genotype to phenotype, we carried out a two-stage whole-genome gene expression association study to find differentially expressed genes and pathways for hypertension. In the first stage, 126 cases and 149 controls were used to find out the differentially expressed genes. In the second stage, an independent set of samples (127 cases and 150 controls) was used to validate the results. Additionally, we conducted a gene set enrichment analysis (GSEA) to search for differentially affected pathways. A total of nine genes were implicated in the first stage (Bonferroni-corrected p-value < 0.05). Among these genes, ZRANB1, FAM110A, PREP, ANKRD9 and LAMB2 were also differentially expressed in an existing database of hypertensive mouse model (GSE19817). A total of 16 pathways were identified by the GSEA. ZRANB1 and six pathways identified are related to TNF-α. Three pathways are related to interleukin, one to metabolic syndrome, and one to Hedgehog signaling. Identification of these genes and pathways suggest the importance of 1. inflammation, 2. visceral fat metabolism, and 3. adipocytes and osteocytes homeostasis in hypertension mechanisms and complications.
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53
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Csabai L, Ölbei M, Budd A, Korcsmáros T, Fazekas D. SignaLink: Multilayered Regulatory Networks. Methods Mol Biol 2018; 1819:53-73. [PMID: 30421399 DOI: 10.1007/978-1-4939-8618-7_3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.
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Affiliation(s)
| | - Márton Ölbei
- Earlham Institute, Norwich Research Park, Norwich, UK.,Quadram Institute, Norwich Research Park, Norwich, UK
| | - Aidan Budd
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Tamás Korcsmáros
- Eötvös Loránd University, Budapest, Hungary. .,Earlham Institute, Norwich Research Park, Norwich, UK. .,Quadram Institute, Norwich Research Park, Norwich, UK.
| | - Dávid Fazekas
- Eötvös Loránd University, Budapest, Hungary.,Earlham Institute, Norwich Research Park, Norwich, UK
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54
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Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [PMID: 28526529 DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
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55
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Sun Y, Ma C, Halgamuge S. The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways. BMC Bioinformatics 2017; 18:551. [PMID: 29297291 PMCID: PMC5751691 DOI: 10.1186/s12859-017-1958-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer. Results We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences. Conclusions Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.
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Affiliation(s)
- Yahui Sun
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, 3010, Australia.
| | - Chenkai Ma
- Department of Surgery, The University of Melbourne, Melbourne, 3010, Australia
| | - Saman Halgamuge
- Research School of Engineering, College of Engineering & Computer Science, The Australian National University, Canberra, 2601, ACT, Australia
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Talikka M, Bukharov N, Hayes WS, Hofmann-Apitius M, Alexopoulos L, Peitsch MC, Hoeng J. Novel approaches to develop community-built biological network models for potential drug discovery. Expert Opin Drug Discov 2017; 12:849-857. [PMID: 28585481 DOI: 10.1080/17460441.2017.1335302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
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Affiliation(s)
- Marja Talikka
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Natalia Bukharov
- b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA
| | - William S Hayes
- c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA
| | - Martin Hofmann-Apitius
- d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany
| | - Leonidas Alexopoulos
- e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.,f Protavio Ltd , Stevenage , UK
| | - Manuel C Peitsch
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Julia Hoeng
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
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Dorel M, Viara E, Barillot E, Zinovyev A, Kuperstein I. NaviCom: a web application to create interactive molecular network portraits using multi-level omics data. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2017; 2017:3098441. [PMID: 28415074 PMCID: PMC5467574 DOI: 10.1093/database/bax026] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 01/08/2017] [Indexed: 12/21/2022]
Abstract
Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease. Database URL: NaviCom is available at https://navicom.curie.fr
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Affiliation(s)
- Mathurin Dorel
- Institut Curie, 26 rue d'Ulm, F-75005 Paris, France.,Inserm, U900 F-75005, Paris France.,Mines Paris Tech, F-77305 Cedex Fontainebleau, France.,PSL Research University, Paris F-75005, France.,Ecole Normale Supérieure, 46 rue d'Ulm, Paris, France.,Institute of Pathology and Institute for Theoretical Biology, Charite - Universitätsmedizin Berlin, Chariteplatz 1, Berlin 10117, Germany
| | | | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, F-75005 Paris, France.,Inserm, U900 F-75005, Paris France.,Mines Paris Tech, F-77305 Cedex Fontainebleau, France.,PSL Research University, Paris F-75005, France
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, F-75005 Paris, France.,Inserm, U900 F-75005, Paris France.,Mines Paris Tech, F-77305 Cedex Fontainebleau, France.,PSL Research University, Paris F-75005, France
| | - Inna Kuperstein
- Institut Curie, 26 rue d'Ulm, F-75005 Paris, France.,Inserm, U900 F-75005, Paris France.,Mines Paris Tech, F-77305 Cedex Fontainebleau, France.,PSL Research University, Paris F-75005, France
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58
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Delineating the Common Biological Pathways Perturbed by ASD's Genetic Etiology: Lessons from Network-Based Studies. Int J Mol Sci 2017; 18:ijms18040828. [PMID: 28420080 PMCID: PMC5412412 DOI: 10.3390/ijms18040828] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 04/03/2017] [Accepted: 04/06/2017] [Indexed: 12/26/2022] Open
Abstract
In recent decades it has become clear that Autism Spectrum Disorder (ASD) possesses a diverse and heterogeneous genetic etiology. Aberrations in hundreds of genes have been associated with ASD so far, which include both rare and common variations. While one may expect that these genes converge on specific common molecular pathways, which drive the development of the core ASD characteristics, the task of elucidating these common molecular pathways has been proven to be challenging. Several studies have combined genetic analysis with bioinformatical techniques to uncover molecular mechanisms that are specifically targeted by autism-associated genetic aberrations. Recently, several analysis have suggested that particular signaling mechanisms, including the Wnt and Ca2+/Calmodulin-signaling pathways are often targeted by autism-associated mutations. In this review, we discuss several studies that determine specific molecular pathways affected by autism-associated mutations, and then discuss more in-depth into the biological roles of a few of these pathways, and how they may be involved in the development of ASD. Considering that these pathways may be targeted by specific pharmacological intervention, they may prove to be important therapeutic targets for the treatment of ASD.
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59
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Chiappino-Pepe A, Pandey V, Ataman M, Hatzimanikatis V. Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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60
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Kong FY, Zhu T, Li N, Cai YF, Zhou K, Wei X, Kou YB, You HJ, Zheng KY, Tang RX. Bioinformatics analysis of the proteins interacting with LASP-1 and their association with HBV-related hepatocellular carcinoma. Sci Rep 2017; 7:44017. [PMID: 28266596 PMCID: PMC5339786 DOI: 10.1038/srep44017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 02/02/2017] [Indexed: 12/11/2022] Open
Abstract
LIM and SH3 domain protein (LASP-1) is responsible for the development of several types of human cancers via the interaction with other proteins; however, the precise biological functions of proteins interacting with LASP-1 are not fully clarified. Although the role of LASP-1 in hepatocarcinogenesis has been reported, the implication of LASP-1 interactors in HBV-related hepatocellular carcinoma (HCC) is not clearly evaluated. We obtained information regarding LASP-1 interactors from public databases and published studies. Via bioinformatics analysis, we found that LASP-1 interactors were related to distinct molecular functions and associated with various biological processes. Through an integrated network analysis of the interaction and pathways of LASP-1 interactors, cross-talk between different proteins and associated pathways was found. In addition, LASP-1 and several its interactors are significantly altered in HBV-related HCC through microarray analysis and could form a complex co-expression network. In the disease, LASP-1 and its interactors were further predicted to be regulated by a complex interaction network composed of different transcription factors. Besides, numerous LASP-1 interactors were associated with various clinical factors and related to the survival and recurrence of HBV-related HCC. Taken together, these results could help enrich our understanding of LASP-1 interactors and their relationships with HBV-related HCC.
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Affiliation(s)
- Fan-Yun Kong
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ting Zhu
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Nan Li
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yun-Fei Cai
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kai Zhou
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiao Wei
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yan-Bo Kou
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Hong-Juan You
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kui-Yang Zheng
- Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Ren-Xian Tang
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Pathogenic Biology and Immunology, Jiangsu Key Laboratory of Immunity and Metabolism, Xuzhou Medical University, Xuzhou, Jiangsu, China
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61
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Fabregat A, Sidiropoulos K, Viteri G, Forner O, Marin-Garcia P, Arnau V, D'Eustachio P, Stein L, Hermjakob H. Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinformatics 2017; 18:142. [PMID: 28249561 PMCID: PMC5333408 DOI: 10.1186/s12859-017-1559-2] [Citation(s) in RCA: 561] [Impact Index Per Article: 70.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 02/22/2017] [Indexed: 11/10/2022] Open
Abstract
Background Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Results Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Conclusion Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub (https://github.com/reactome/).
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Affiliation(s)
- Antonio Fabregat
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK.,Open Targets, Wellcome Genome Campus, Hinxton, UK
| | - Konstantinos Sidiropoulos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Guilherme Viteri
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Oscar Forner
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Pablo Marin-Garcia
- Fundación Investigación INCLIVA, Universitat de València, Valencia, Spain.,Instituto de Medicina Genomica, Valencia, Spain
| | - Vicente Arnau
- Escuela Técnica Superior de Ingenierías, Universitat de València, Valencia, Spain.,Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, Valencia, Spain
| | | | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK. .,State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine; National Center for Protein Sciences, 102206, Beijing, China.
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62
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Korla K, Chandra N. A Systems Perspective of Signalling Networks in Host–Pathogen Interactions. J Indian Inst Sci 2017. [DOI: 10.1007/s41745-016-0017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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63
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Lateef Z, Gimenez G, Baker ES, Ward VK. Transcriptomic analysis of human norovirus NS1-2 protein highlights a multifunctional role in murine monocytes. BMC Genomics 2017; 18:39. [PMID: 28056773 PMCID: PMC5217272 DOI: 10.1186/s12864-016-3417-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 12/12/2016] [Indexed: 12/22/2022] Open
Abstract
Background The GII.4 Sydney 2012 strain of human norovirus (HuNoV) is a pandemic strain that is responsible for the majority of norovirus outbreaks in healthcare settings. The function of the non-structural (NS)1-2 protein from HuNoV is unknown. Results In silico analysis of human norovirus NS1-2 protein showed that it shares features with the murine NS1-2 protein, including a disordered region, a transmembrane domain and H-box and NC sequence motifs. The proteins also contain caspase cleavage and phosphorylation sites, indicating that processing and phosphorylation may be a conserved feature of norovirus NS1-2 proteins. In this study, RNA transcripts of human and murine norovirus full-length and the disordered region of NS1-2 were transfected into monocytes, and next generation sequencing was used to analyse the transcriptomic profile of cells expressing virus proteins. The profiles were then compared to the transcriptomic profile of MNV-infected cells. Conclusions RNAseq analysis showed that NS1-2 proteins from human and murine noroviruses affect multiple immune systems (chemokine, cytokine, and Toll-like receptor signaling) and intracellular pathways (NFκB, MAPK, PI3K-Akt signaling) in murine monocytes. Comparison to the transcriptomic profile of MNV-infected cells indicated the pathways that NS1-2 may affect during norovirus infection. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3417-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zabeen Lateef
- Department of Microbiology and Immunology, Otago School of Medical Sciences, University of Otago, 720 Cumberland St, Dunedin, 9054, New Zealand.
| | - Gregory Gimenez
- Otago Genomics and Bioinformatics Facility, University of Otago, Dunedin, 9054, New Zealand
| | - Estelle S Baker
- Department of Microbiology and Immunology, Otago School of Medical Sciences, University of Otago, 720 Cumberland St, Dunedin, 9054, New Zealand
| | - Vernon K Ward
- Department of Microbiology and Immunology, Otago School of Medical Sciences, University of Otago, 720 Cumberland St, Dunedin, 9054, New Zealand
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64
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Lee J, Jo K, Lee S, Kang J, Kim S. Prioritizing biological pathways by recognizing context in time-series gene expression data. BMC Bioinformatics 2016; 17:477. [PMID: 28155707 PMCID: PMC5259824 DOI: 10.1186/s12859-016-1335-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Background The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. Results In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. Conclusions Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jusang Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Kyuri Jo
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sunwon Lee
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea. .,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. .,Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
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65
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Lan Z, Zhao Y, Kang J, Yu T. Bayesian network feature finder (BANFF): an R package for gene network feature selection. Bioinformatics 2016; 32:3685-3687. [PMID: 27503223 DOI: 10.1093/bioinformatics/btw522] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 07/12/2016] [Accepted: 08/03/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand. RESULTS We extended the method and developed an R package, the Bayesian network feature finder (BANFF), providing a package of posterior inference, model comparison and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo -based posterior inference and an expectation maximization-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network. AVAILABILITY https://cran.r-project.org/web/packages/BANFF/index.html CONTACT: jiankang@umich.edu, tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhou Lan
- Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA
| | - Yize Zhao
- Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY 10065, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
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66
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Yan Y, Qiu S, Jin Z, Gong S, Bai Y, Lu J, Yu T. Detecting subnetwork-level dynamic correlations. Bioinformatics 2016; 33:256-265. [PMID: 27667792 DOI: 10.1093/bioinformatics/btw616] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 09/07/2016] [Accepted: 09/21/2016] [Indexed: 01/11/2023] Open
Abstract
MOTIVATION The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. RESULTS Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. AVAILABILITY AND IMPLEMENTATION The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Yan
- School of Software Engineering, Tongji University, Shanghai, China
| | - Shangzhao Qiu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Zhuxuan Jin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Sihong Gong
- School of Software Engineering, Tongji University, Shanghai, China
| | - Yun Bai
- Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA, USA
| | - Jianwei Lu
- School of Software Engineering, Tongji University, Shanghai, China
- Institute of Advanced Translational Medicine, Tongji University, Shanghai, China
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
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67
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Abhinand CS, Raju R, Soumya SJ, Arya PS, Sudhakaran PR. VEGF-A/VEGFR2 signaling network in endothelial cells relevant to angiogenesis. J Cell Commun Signal 2016; 10:347-354. [PMID: 27619687 DOI: 10.1007/s12079-016-0352-8] [Citation(s) in RCA: 332] [Impact Index Per Article: 36.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 08/30/2016] [Indexed: 12/20/2022] Open
Abstract
Vascular endothelial growth factor-A (VEGF-A) is essential for endothelial cell functions associated with angiogenesis. Signal transduction networks initiated by VEGFA/VEGFR2, the most prominent ligand-receptor complex in the VEGF system, leads to endothelial cell proliferation, migration, survival and new vessel formation involved in angiogenesis. Considering its biomedical importance, we have developed the first comprehensive map of endothelial cell-specific signaling events of VEGFA/VEGFR2 system pertaining to angiogenesis. Screening over 20,000 published research articles and following the post-translational modification (PTM) and site specificity of VEGFR2, we have documented 240 proteins and their diverse PTM-dependent reactions involved in VEGFA/VEGFR2 signal transduction. From the ligand-receptor complex, this map has been extended to the level of major transcriptionally regulated genes for which the signaling cascades leading to their transcription factors are reported. We believe that this map would serve as a novel platform for reference, integration, and representation and more significantly, the progressive analysis of dynamic features of VEGF signaling in endothelial cells including their cross-talks with other ligand-receptor systems involved in angiogenesis.
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Affiliation(s)
- Chandran S Abhinand
- Department of Computational Biology and Bioinformatics, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695 581, India
| | - Rajesh Raju
- Computational Biology Group, Cancer Research Program-9, Rajiv Gandhi Centre for Biotechnology, KINFRA Campus, Thiruvananthapuram, Kerala, -695 585, India
| | - Sasikumar J Soumya
- Inter-University Centre for Genomics and Gene Technology, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695 581, India
| | - Prabha S Arya
- Department of Computational Biology and Bioinformatics, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695 581, India
| | - Perumana R Sudhakaran
- Department of Computational Biology and Bioinformatics, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, 695 581, India.
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68
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Louhimo R, Laakso M, Belitskin D, Klefström J, Lehtonen R, Hautaniemi S. Data integration to prioritize drugs using genomics and curated data. BioData Min 2016; 9:21. [PMID: 27231484 PMCID: PMC4881054 DOI: 10.1186/s13040-016-0097-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Accepted: 04/30/2016] [Indexed: 03/13/2025] Open
Abstract
Background Genomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature. Results We have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3. Conclusions Our results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0097-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Riku Louhimo
- Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
| | - Marko Laakso
- Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
| | - Denis Belitskin
- Translational Cancer Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
| | - Juha Klefström
- Translational Cancer Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
| | - Rainer Lehtonen
- Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
| | - Sampsa Hautaniemi
- Genome Scale Biology Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, P.O. Box 63 (Haartmaninkatu 8), Helsinki, FI-00014 Finland
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Yan Y, Wang LF, Wang RF. Role of cancer-associated fibroblasts in invasion and metastasis of gastric cancer. World J Gastroenterol 2015; 21:9717-9726. [PMID: 26361418 PMCID: PMC4562955 DOI: 10.3748/wjg.v21.i33.9717] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 06/05/2015] [Accepted: 07/18/2015] [Indexed: 02/06/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) are important components of various types of tumors, including gastric cancer (GC). During tumorigenesis and progression, CAFs play critical roles in tumor invasion and metastasis via a series of functions including extracellular matrix deposition, angiogenesis, metabolism reprogramming and chemoresistance. However, the mechanism of the interaction between gastric cancer cells and CAFs remains largely unknown. MicroRNAs (miRNAs) are a class of non-coding small RNA molecules, and their expression in CAFs not only regulates the expression of a number of target genes but also plays an essential role in the communication between tumor cells and CAFs. In this review, we provide an overview of recent studies on CAF miRNAs in GC and the relevant signaling pathways in gastrointestinal tumors. Focusing the attention on these signaling pathways may help us better understand their role in tumor invasion and metastasis and identify new molecular targets for therapeutic strategies.
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Affiliation(s)
- Christine Nardini
- Lazzari Bologna, Italy ; Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences Shanghai, China
| | | | - Paolo Tieri
- Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo Rome, Italy
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Hernansaiz-Ballesteros RD, Salavert F, Sebastián-León P, Alemán A, Medina I, Dopazo J. Assessing the impact of mutations found in next generation sequencing data over human signaling pathways. Nucleic Acids Res 2015; 43:W270-5. [PMID: 25883139 PMCID: PMC4489259 DOI: 10.1093/nar/gkv349] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Accepted: 04/02/2015] [Indexed: 01/20/2023] Open
Abstract
Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.
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Affiliation(s)
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - Patricia Sebastián-León
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Alejandro Alemán
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - Ignacio Medina
- HPC Services, University of Cambridge, Cambridge, CB3 0RB, UK
| | - Joaquín Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain Functional Genomics Node, (INB) at CIPF, Valencia, 45012, Spain
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