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Sequeira JC, Rocha M, Madalena Alves M, Salvador AF. UPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets. Comput Struct Biotechnol J 2022; 20:1798-1810. [PMID: 35495109 PMCID: PMC9034014 DOI: 10.1016/j.csbj.2022.03.042] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 03/31/2022] [Accepted: 03/31/2022] [Indexed: 11/25/2022] Open
Abstract
Omics and meta-omics technologies are powerful approaches to explore microorganisms’ functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for meta-omics analyses, many require significant computational expertise. Web interfaces are more user-friendly, but often struggle to handle large data files, such as those obtained in metagenomics, metatranscriptomics, or metaproteomics experiments. In this work, we present three novel bioinformatics tools, which are available through user-friendly command-line interfaces, can be run sequentially or stand-alone, and combine popular resources for functional annotation. UPIMAPI performs sequence homology-based annotation and obtains data from UniProtKB (e.g., protein names, EC numbers, Gene Ontology, Taxonomy, cross-references to external databases). reCOGnizer performs multithreaded domain homology-based annotation of protein sequences with several functional databases (i.e., CDD, NCBIfam, Pfam, Protein Clusters, SMART, TIGRFAM, COG and KOG) and in addition, obtains information on domain names and descriptions and EC numbers. KEGGCharter represents omics results, including differential gene expression, in KEGG metabolic pathways. In addition, it shows the taxonomic assignment of the enzymes represented, which is particularly useful in metagenomics studies in which several microorganisms are present. reCOGnizer, UPIMAPI and KEGGCharter together provide a comprehensive and complete functional characterization of large datasets, facilitating the interpretation of microbial activities in nature and in biotechnological processes.
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Patel MK, Pandey S, Kumar M, Haque MI, Pal S, Yadav NS. Plants Metabolome Study: Emerging Tools and Techniques. PLANTS (BASEL, SWITZERLAND) 2021; 10:2409. [PMID: 34834772 PMCID: PMC8621461 DOI: 10.3390/plants10112409] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 05/06/2023]
Abstract
Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.
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Affiliation(s)
- Manish Kumar Patel
- Department of Postharvest Science of Fresh Produce, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
| | - Sonika Pandey
- Independent Researcher, Civil Line, Fathepur 212601, India;
| | - Manoj Kumar
- Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel;
| | - Md Intesaful Haque
- Fruit Tree Science Department, Newe Ya’ar Research Center, Agriculture Research Organization, Volcani Center, Ramat Yishay 3009500, Israel;
| | - Sikander Pal
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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Wang J, Wang C, Liu H, Qi H, Chen H, Wen J. Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology. Crit Rev Biotechnol 2018; 38:1106-1120. [DOI: 10.1080/07388551.2018.1462141] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Junhua Wang
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, People’s Republic of China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
| | - Cheng Wang
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, People’s Republic of China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
| | - Huanhuan Liu
- Key Laboratory of Food Nutrition and Safety, Ministry of Education, School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin, China
| | - Haishan Qi
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, People’s Republic of China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
| | - Hong Chen
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, People’s Republic of China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
| | - Jianping Wen
- Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, People’s Republic of China
- SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), School of Chemical Engineering and Technology, Tianjin University, Tianjin, People’s Republic of China
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Ramirez-Gaona M, Marcu A, Pon A, Grant J, Wu A, Wishart DS. A Web Tool for Generating High Quality Machine-readable Biological Pathways. J Vis Exp 2017:54869. [PMID: 28287524 PMCID: PMC5409186 DOI: 10.3791/54869] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological information. The pathways generated by this online application are machine-readable and fully compatible with essentially all web-browsers and computer operating systems. It uses a specially developed, web-enabled pathway drawing interface that permits the selection and placement of different combinations of pre-drawn biological or biochemical entities to depict reactions, interactions, transport processes and binding events. This palette of entities consists of chemical compounds, proteins, nucleic acids, cellular membranes, subcellular structures, tissues, and organs. All of the visual elements in it can be interactively adjusted and customized. Furthermore, because this tool is a web server, all pathways and pathway elements are publicly accessible. This kind of pathway "crowd sourcing" means that PathWhiz already contains a large and rapidly growing collection of previously drawn pathways and pathway elements. Here we describe a protocol for the quick and easy creation of new pathways and the alteration of existing pathways. To further facilitate pathway editing and creation, the tool contains replication and propagation functions. The replication function allows existing pathways to be used as templates to create or edit new pathways. The propagation function allows one to take an existing pathway and automatically propagate it across different species. Pathways created with this tool can be "re-styled" into different formats (KEGG-like or text-book like), colored with different backgrounds, exported to BioPAX, SBGN-ML, SBML, or PWML data exchange formats, and downloaded as PNG or SVG images. The pathways can easily be incorporated into online databases, integrated into presentations, posters or publications, or used exclusively for online visualization and exploration. This protocol has been successfully applied to generate over 2,000 pathway diagrams, which are now found in many online databases including HMDB, DrugBank, SMPDB, and ECMDB.
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Affiliation(s)
| | - Ana Marcu
- Department of Computer Science, University of Alberta
| | - Allison Pon
- Department of Computer Science, University of Alberta
| | - Jason Grant
- Department of Computer Science, University of Alberta
| | - Anthony Wu
- Department of Computer Science, University of Alberta
| | - David S Wishart
- Department of Computer Science, University of Alberta; Department of Biological Sciences, University of Alberta;
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Pon A, Jewison T, Su Y, Liang Y, Knox C, Maciejewski A, Wilson M, Wishart DS. Pathways with PathWhiz. Nucleic Acids Res 2015; 43:W552-9. [PMID: 25934797 PMCID: PMC4489271 DOI: 10.1093/nar/gkv399] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Accepted: 04/15/2015] [Indexed: 01/06/2023] Open
Abstract
PathWhiz (http://smpdb.ca/pathwhiz) is a web server designed to create colourful, visually pleasing and biologically accurate pathway diagrams that are both machine-readable and interactive. As a web server, PathWhiz is accessible from almost any place and compatible with essentially any operating system. It also houses a public library of pathways and pathway components that can be easily viewed and expanded upon by its users. PathWhiz allows users to readily generate biologically complex pathways by using a specially designed drawing palette to quickly render metabolites (including automated structure generation), proteins (including quaternary structures, covalent modifications and cofactors), nucleic acids, membranes, subcellular structures, cells, tissues and organs. Both small-molecule and protein/gene pathways can be constructed by combining multiple pathway processes such as reactions, interactions, binding events and transport activities. PathWhiz's pathway replication and propagation functions allow for existing pathways to be used to create new pathways or for existing pathways to be automatically propagated across species. PathWhiz pathways can be saved in BioPAX, SBGN-ML and SBML data exchange formats, as well as PNG, PWML, HTML image map or SVG images that can be viewed offline or explored using PathWhiz's interactive viewer. PathWhiz has been used to generate over 700 pathway diagrams for a number of popular databases including HMDB, DrugBank and SMPDB.
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Affiliation(s)
- Allison Pon
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Timothy Jewison
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Yilu Su
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Yongjie Liang
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Craig Knox
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Adam Maciejewski
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - Michael Wilson
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada
| | - David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada Department of Biological Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, Alberta, T6G 2M9, Canada
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Cicek AE, Qi X, Cakmak A, Johnson SR, Han X, Alshalwi S, Ozsoyoglu ZM, Ozsoyoglu G. An online system for metabolic network analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau091. [PMID: 25267793 PMCID: PMC4178370 DOI: 10.1093/database/bau091] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. Database URL: http://nashua.case.edu/PathwaysMAW/Web
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Affiliation(s)
- Abdullah Ercument Cicek
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xinjian Qi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Ali Cakmak
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Stephen R Johnson
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xu Han
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Sami Alshalwi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Zehra Meral Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Gultekin Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
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7
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He X, Sun Q, Jiang H, Zhu X, Mo J, Long L, Xiang L, Xie Y, Shi Y, Yuan Y, Cai Y. Identification of novel microRNAs in the Verticillium wilt-resistant upland cotton variety KV-1 by high-throughput sequencing. SPRINGERPLUS 2014; 3:564. [PMID: 25332864 PMCID: PMC4190182 DOI: 10.1186/2193-1801-3-564] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2014] [Accepted: 09/16/2014] [Indexed: 12/26/2022]
Abstract
Plant microRNAs (miRNAs) play essential roles in the post-transcriptional regulation of gene expression during development, flowering, plant growth, metabolism, and stress responses. Verticillium wilt is one of the vascular disease in plants, which is caused by the Verticillium dahlia and leads to yellowing, wilting, lodging, damage to the vascular tissue, and death in cotton plants. Upland cotton varieties KV-1 have shown resistance to Verticillium wilt in multiple levels. However, the knowledge regarding the post-transcriptional regulation of the resistance is limited. Here two novel small RNA (sRNA) libraries were constructed from the seedlings of upland cotton variety KV-1, which is highly resistant to Verticillium wilts and inoculated with the V991 and D07038 Verticillium dahliae (V. dahliae) of different virulence strains. Thirty-seven novel miRNAs were identified after sequencing these two libraries by the Illumina Solexa system. According to sequence homology analysis, potential target genes of these miRNAs were predicted. With no more than three sequence mismatches between the novel miRNAs and the potential target mRNAs, we predicted 49 target mRNAs for 24 of the novel miRNAs. These target mRNAs corresponded to genes were found to be involved in plant–pathogen interactions, endocytosis, the mitogen-activated protein kinase (MAPK) signaling pathway, and the biosynthesis of isoquinoline alkaloid, terpenoid backbone, primary bile acid and secondary metabolites. Our results showed that some of these miRNAs and their relative gene are involved in resistance to Verticillium wilts. The identification and characterization of miRNAs from upland cotton could help further studies on the miRNA regulatory mechanisms of resistance to Verticillium wilt.
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Affiliation(s)
- Xiaohong He
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Quan Sun
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Huaizhong Jiang
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Xiaoyan Zhu
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Jianchuan Mo
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Sciences, Henan University, Kaifeng, 475004 China
| | - Lu Long
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Sciences, Henan University, Kaifeng, 475004 China
| | - Liuxin Xiang
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Yongfang Xie
- College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Cotton Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, Henan 455000 China
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, Cotton Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture, Anyang, Henan 455000 China
| | - Yingfan Cai
- State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Sciences, Henan University, Kaifeng, 475004 China ; College of Bioinformation, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
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Babur Ö, Dogrusoz U, Çakır M, Aksoy BA, Schultz N, Sander C, Demir E. Integrating biological pathways and genomic profiles with ChiBE 2. BMC Genomics 2014; 15:642. [PMID: 25086704 PMCID: PMC4131037 DOI: 10.1186/1471-2164-15-642] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 07/24/2014] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets. RESULTS ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks. CONCLUSIONS ChiBE's new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers. The software is freely available at http://code.google.com/p/chibe.
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Affiliation(s)
- Özgün Babur
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA.
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Coskun SA, Cicek AE, Lai N, Dash RK, Ozsoyoglu ZM, Ozsoyoglu G. An online model composition tool for system biology models. BMC SYSTEMS BIOLOGY 2013; 7:88. [PMID: 24006914 PMCID: PMC3846440 DOI: 10.1186/1752-0509-7-88] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 08/21/2013] [Indexed: 11/21/2022]
Abstract
Background There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models. Results We present the design and implementation of the Model Composition Tool (Interface) within the PathCase-SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user’s input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models. Conclusions Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well.
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Affiliation(s)
- Sarp A Coskun
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.
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iPathCase(KEGG): An iPad interface for KEGG metabolic pathways. Health Inf Sci Syst 2013; 1:4. [PMID: 25825656 PMCID: PMC4336120 DOI: 10.1186/2047-2501-1-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Accepted: 09/04/2012] [Indexed: 11/14/2022] Open
Abstract
Background Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online and integrated molecular database for several organisms. KEGG has been a highly useful site, helping domain scientists understand, research, study, and teach metabolisms by linking sequenced genomes to higher level systematic functions. KEGG databases are accessible through the web pages of the system, but the capabilities of the web interface are limited. Third party systems have been built over the KEGG data to provide extensive functionalities. However, there have been no attempts towards providing a tablet interface for KEGG data. Recognizing the rise of mobile technologies and the importance of tablets in education, this paper presents the design and implementation of iPathCaseKEGG, an iPad interface for KEGG data, which is empowered with multiple browsing and visualization capabilities. Results iPathCaseKEGG has been implemented and is available, free of charge, in the Apple App Store (locatable by searching for “Pathcase” in the app store). The application provides browsing and interactive visualization functionalities on the KEGG data. Users can pick pathways, visualize them, and see detail pages of reactions and molecules using the multi-touch interface of iPad. Conclusions iPathCaseKEGG provides a mobile interface to access KEGG data. Interactive visualization and browsing functionalities let users to interact with the data in multiple ways. As the importance of tablets and their usage in research education continue to rise, we think iPathCaseKEGG will be a useful tool for life science instructors and researchers.
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Zhou H, Jin J, Zhang H, Yi B, Wozniak M, Wong L. IntPath--an integrated pathway gene relationship database for model organisms and important pathogens. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 2:S2. [PMID: 23282057 PMCID: PMC3521174 DOI: 10.1186/1752-0509-6-s2-s2] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background Pathway data are important for understanding the relationship between genes, proteins and many other molecules in living organisms. Pathway gene relationships are crucial information for guidance, prediction, reference and assessment in biochemistry, computational biology, and medicine. Many well-established databases--e.g., KEGG, WikiPathways, and BioCyc--are dedicated to collecting pathway data for public access. However, the effectiveness of these databases is hindered by issues such as incompatible data formats, inconsistent molecular representations, inconsistent molecular relationship representations, inconsistent referrals to pathway names, and incomprehensive data from different databases. Results In this paper, we overcome these issues through extraction, normalization and integration of pathway data from several major public databases (KEGG, WikiPathways, BioCyc, etc). We build a database that not only hosts our integrated pathway gene relationship data for public access but also maintains the necessary updates in the long run. This public repository is named IntPath (Integrated Pathway gene relationship database for model organisms and important pathogens). Four organisms--S. cerevisiae, M. tuberculosis H37Rv, H. Sapiens and M. musculus--are included in this version (V2.0) of IntPath. IntPath uses the "full unification" approach to ensure no deletion and no introduced noise in this process. Therefore, IntPath contains much richer pathway-gene and pathway-gene pair relationships and much larger number of non-redundant genes and gene pairs than any of the single-source databases. The gene relationships of each gene (measured by average node degree) per pathway are significantly richer. The gene relationships in each pathway (measured by average number of gene pairs per pathway) are also considerably richer in the integrated pathways. Moderate manual curation are involved to get rid of errors and noises from source data (e.g., the gene ID errors in WikiPathways and relationship errors in KEGG). We turn complicated and incompatible xml data formats and inconsistent gene and gene relationship representations from different source databases into normalized and unified pathway-gene and pathway-gene pair relationships neatly recorded in simple tab-delimited text format and MySQL tables, which facilitates convenient automatic computation and large-scale referencing in many related studies. IntPath data can be downloaded in text format or MySQL dump. IntPath data can also be retrieved and analyzed conveniently through web service by local programs or through web interface by mouse clicks. Several useful analysis tools are also provided in IntPath. Conclusions We have overcome in IntPath the issues of compatibility, consistency, and comprehensiveness that often hamper effective use of pathway databases. We have included four organisms in the current release of IntPath. Our methodology and programs described in this work can be easily applied to other organisms; and we will include more model organisms and important pathogens in future releases of IntPath. IntPath maintains regular updates and is freely available at http://compbio.ddns.comp.nus.edu.sg:8080/IntPath.
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Affiliation(s)
- Hufeng Zhou
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, Singapore
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12
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Coskun SA, Qi X, Cakmak A, Cheng E, Cicek AE, Yang L, Jadeja R, Dash RK, Lai N, Ozsoyoglu G, Ozsoyoglu ZM. PathCase-SB: integrating data sources and providing tools for systems biology research. BMC SYSTEMS BIOLOGY 2012; 6:67. [PMID: 22697505 PMCID: PMC3410775 DOI: 10.1186/1752-0509-6-67] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Accepted: 06/14/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND Integration of metabolic pathways resources and metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation of metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. RESULTS PathCase Systems Biology (PathCase-SB) is built and released. This paper describes PathCase-SB user interfaces developed to date. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate systems biology models data and metabolic network data of selected biological data sources on the web (currently, BioModels Database and KEGG, respectively), and to provide more powerful and/or new capabilities via the new web-based integrative framework. CONCLUSIONS Each of the current four PathCase-SB interfaces, namely, Browser, Visualization, Querying, and Simulation interfaces, have expanded and new capabilities as compared with the original data sources. PathCase-SB is already available on the web and being used by researchers across the globe.
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Affiliation(s)
- Sarp A Coskun
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Xinjian Qi
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Ali Cakmak
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - En Cheng
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - A Ercument Cicek
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Lei Yang
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Rishiraj Jadeja
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Ranjan K Dash
- Department of Physiology, Medical College of Wisconsin, Milwaukee, USA
| | - Nicola Lai
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Pediatrics, Case Western Reserve University, Cleveland, USA
| | - Gultekin Ozsoyoglu
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
| | - Zehra Meral Ozsoyoglu
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, USA
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13
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2012; 59:893-903. [PMID: 21712727 DOI: 10.2310/jim.0b013e318224d8cc] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX 77030, USA.
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14
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Wong L. Using Biological Networks in Protein Function Prediction and Gene Expression Analysis. ACTA ACUST UNITED AC 2011. [DOI: 10.1080/15427951.2011.604561] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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15
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Cakmak A, Qi X, Coskun SA, Das M, Cheng E, Cicek AE, Lai N, Ozsoyoglu G, Ozsoyoglu ZM. PathCase-SB architecture and database design. BMC SYSTEMS BIOLOGY 2011; 5:188. [PMID: 22070889 PMCID: PMC3229461 DOI: 10.1186/1752-0509-5-188] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Accepted: 11/09/2011] [Indexed: 11/16/2022]
Abstract
Background Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. Description PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database. Conclusions PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world.
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Affiliation(s)
- Ali Cakmak
- Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA
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16
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Kemper B, Matsuzaki T, Matsuoka Y, Tsuruoka Y, Kitano H, Ananiadou S, Tsujii J. PathText: a text mining integrator for biological pathway visualizations. Bioinformatics 2010; 26:i374-81. [PMID: 20529930 PMCID: PMC2881405 DOI: 10.1093/bioinformatics/btq221] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact:brian@monrovian.com.
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Affiliation(s)
- Brian Kemper
- Department of Computer Science, University of Tokyo, Tokyo, Japan.
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Soh D, Dong D, Guo Y, Wong L. Consistency, comprehensiveness, and compatibility of pathway databases. BMC Bioinformatics 2010; 11:449. [PMID: 20819233 PMCID: PMC2944280 DOI: 10.1186/1471-2105-11-449] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2009] [Accepted: 09/07/2010] [Indexed: 11/16/2022] Open
Abstract
Background It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need. Description Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases (KEGG, Ingenuity and Wikipathways). The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs. Conclusions Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at http://www.pathwayapi.com.
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Affiliation(s)
- Donny Soh
- School of Computing, National University of Singapore, Building COM1, 117417 Singapore.
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Cakmak A, Ozsoyoglu G, Hanson RW. Querying metabolism under different physiological constraints. J Bioinform Comput Biol 2010; 8:247-93. [PMID: 20401946 DOI: 10.1142/s0219720010004604] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2009] [Revised: 11/02/2009] [Accepted: 11/02/2009] [Indexed: 11/18/2022]
Abstract
Metabolism is a representation of the biochemical principles that govern the production, consumption, degradation, and biosynthesis of metabolites in living cells. Organisms respond to changes in their physiological conditions or environmental perturbations (i.e. constraints) via cooperative implementation of such principles. Querying inner working principles of metabolism under different constraints provides invaluable insights for both researchers and educators. In this paper, we propose a metabolism query language (MQL) and discuss its query processing. MQL enables researchers to explore the behavior of the metabolism with a wide-range of predicates including dietary and physiological condition specifications. The query results of MQL are enriched with both textual and visual representations, and its query processing is completely tailored based on the underlying metabolic principles.
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Affiliation(s)
- Ali Cakmak
- Department of Electrical Engineering & Computer Science, Case Western Reserve University, USA.
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Plaisier SB, Taschereau R, Wong JA, Graeber TG. Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res 2010; 38:e169. [PMID: 20660011 PMCID: PMC2943622 DOI: 10.1093/nar/gkq636] [Citation(s) in RCA: 304] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Comparing independent high-throughput gene-expression experiments can generate hypotheses about which gene-expression programs are shared between particular biological processes. Current techniques to compare expression profiles typically involve choosing a fixed differential expression threshold to summarize results, potentially reducing sensitivity to small but concordant changes. We present a threshold-free algorithm called Rank–rank Hypergeometric Overlap (RRHO). This algorithm steps through two gene lists ranked by the degree of differential expression observed in two profiling experiments, successively measuring the statistical significance of the number of overlapping genes. The output is a graphical map that shows the strength, pattern and bounds of correlation between two expression profiles. To demonstrate RRHO sensitivity and dynamic range, we identified shared expression networks in cancer microarray profiles driving tumor progression, stem cell properties and response to targeted kinase inhibition. We demonstrate how RRHO can be used to determine which model system or drug treatment best reflects a particular biological or disease response. The threshold-free and graphical aspects of RRHO complement other rank-based approaches such as Gene Set Enrichment Analysis (GSEA), for which RRHO is a 2D analog. Rank–rank overlap analysis is a sensitive, robust and web-accessible method for detecting and visualizing overlap trends between two complete, continuous gene-expression profiles. A web-based implementation of RRHO can be accessed at http://systems.crump.ucla.edu/rankrank/.
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Affiliation(s)
- Seema B Plaisier
- Crump Institute for Molecular Imaging, Department of Molecular and Medical Pharmacology, Institute for Molecular Medicine, Jonsson Comprehensive Cancer Center and California NanoSystems Institute, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
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Ananiadou S, Pyysalo S, Tsujii J, Kell DB. Event extraction for systems biology by text mining the literature. Trends Biotechnol 2010; 28:381-90. [PMID: 20570001 DOI: 10.1016/j.tibtech.2010.04.005] [Citation(s) in RCA: 140] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2010] [Revised: 04/20/2010] [Accepted: 04/26/2010] [Indexed: 01/08/2023]
Abstract
Systems biology recognizes in particular the importance of interactions between biological components and the consequences of these interactions. Such interactions and their downstream effects are known as events. To computationally mine the literature for such events, text mining methods that can detect, extract and annotate them are required. This review summarizes the methods that are currently available, with a specific focus on protein-protein interactions and pathway or network reconstruction. The approaches described will be of considerable value in associating particular pathways and their components with higher-order physiological properties, including disease states.
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Dilek A, Belviranli ME, Dogrusoz U. VISIBIOweb: visualization and layout services for BioPAX pathway models. Nucleic Acids Res 2010; 38:W150-4. [PMID: 20460470 PMCID: PMC2896092 DOI: 10.1093/nar/gkq352] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
With recent advancements in techniques for cellular data acquisition, information on cellular processes has been increasing at a dramatic rate. Visualization is critical to analyzing and interpreting complex information; representing cellular processes or pathways is no exception. VISIBIOweb is a free, open-source, web-based pathway visualization and layout service for pathway models in BioPAX format. With VISIBIOweb, one can obtain well-laid-out views of pathway models using the standard notation of the Systems Biology Graphical Notation (SBGN), and can embed such views within one's web pages as desired. Pathway views may be navigated using zoom and scroll tools; pathway object properties, including any external database references available in the data, may be inspected interactively. The automatic layout component of VISIBIOweb may also be accessed programmatically from other tools using Hypertext Transfer Protocol (HTTP). The web site is free and open to all users and there is no login requirement. It is available at: http://visibioweb.patika.org.
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Affiliation(s)
- Alptug Dilek
- Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
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Babur O, Dogrusoz U, Demir E, Sander C. ChiBE: interactive visualization and manipulation of BioPAX pathway models. ACTA ACUST UNITED AC 2009; 26:429-31. [PMID: 20007251 DOI: 10.1093/bioinformatics/btp665] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
SUMMARY Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context. AVAILABILITY http://www.bilkent.edu.tr/%7Ebcbi/chibe.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ozgun Babur
- Center for Bioinformatics, Bilkent University, Ankara, Turkey
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Pathway projector: web-based zoomable pathway browser using KEGG atlas and Google Maps API. PLoS One 2009; 4:e7710. [PMID: 19907644 PMCID: PMC2770834 DOI: 10.1371/journal.pone.0007710] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2009] [Accepted: 10/11/2009] [Indexed: 11/19/2022] Open
Abstract
Background Biochemical pathways provide an essential context for understanding comprehensive experimental data and the systematic workings of a cell. Therefore, the availability of online pathway browsers will facilitate post-genomic research, just as genome browsers have contributed to genomics. Many pathway maps have been provided online as part of public pathway databases. Most of these maps, however, function as the gateway interface to a specific database, and the comprehensiveness of their represented entities, data mapping capabilities, and user interfaces are not always sufficient for generic usage. Methodology/Principal Findings We have identified five central requirements for a pathway browser: (1) availability of large integrated maps showing genes, enzymes, and metabolites; (2) comprehensive search features and data access; (3) data mapping for transcriptomic, proteomic, and metabolomic experiments, as well as the ability to edit and annotate pathway maps; (4) easy exchange of pathway data; and (5) intuitive user experience without the requirement for installation and regular maintenance. According to these requirements, we have evaluated existing pathway databases and tools and implemented a web-based pathway browser named Pathway Projector as a solution. Conclusions/Significance Pathway Projector provides integrated pathway maps that are based upon the KEGG Atlas, with the addition of nodes for genes and enzymes, and is implemented as a scalable, zoomable map utilizing the Google Maps API. Users can search pathway-related data using keywords, molecular weights, nucleotide sequences, and amino acid sequences, or as possible routes between compounds. In addition, experimental data from transcriptomic, proteomic, and metabolomic analyses can be readily mapped. Pathway Projector is freely available for academic users at http://www.g-language.org/PathwayProjector/.
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