PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways
Identifieur interne : 000083 ( Main/Exploration ); précédent : 000082; suivant : 000084PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways
Auteurs : Yongnan Zhu [États-Unis] ; Liang Sun [États-Unis] ; Alexander Garbarino [États-Unis] ; Carl Schmidt [États-Unis] ; Jinglong Fang ; Jian Chen [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2015.
Abstract
High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.
We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management.
PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at
The online version of this article (doi:10.1186/s12859-015-0585-1) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-015-0585-1
PubMed: 25982732
PubMed Central: 4436019
Affiliations:
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China</wicri:noCountry></affiliation></author><author><name sortKey="Sun, Liang" sort="Sun, Liang" uniqKey="Sun L" first="Liang" last="Sun">Liang Sun</name><affiliation wicri:level="2"><nlm:aff id="Aff3">Department of Animal & Food Sciences, University of Delaware, Newark, DE USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Delaware</region></placeName><wicri:cityArea>Department of Animal & Food Sciences, University of Delaware, Newark</wicri:cityArea></affiliation></author><author><name sortKey="Garbarino, Alexander" sort="Garbarino, Alexander" uniqKey="Garbarino A" first="Alexander" last="Garbarino">Alexander Garbarino</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore</wicri:cityArea></affiliation></author><author><name sortKey="Schmidt, Carl" sort="Schmidt, Carl" uniqKey="Schmidt C" first="Carl" last="Schmidt">Carl Schmidt</name><affiliation wicri:level="2"><nlm:aff id="Aff3">Department of Animal & Food Sciences, University of Delaware, Newark, DE USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Delaware</region></placeName><wicri:cityArea>Department of Animal & Food Sciences, University of Delaware, Newark</wicri:cityArea></affiliation></author><author><name sortKey="Fang, Jinglong" sort="Fang, Jinglong" uniqKey="Fang J" first="Jinglong" last="Fang">Jinglong Fang</name><affiliation><nlm:aff id="Aff2">Department of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang Province P.R. 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China</nlm:aff><wicri:noCountry code="subfield">Zhejiang Province P.R. China</wicri:noCountry></affiliation></author><author><name sortKey="Sun, Liang" sort="Sun, Liang" uniqKey="Sun L" first="Liang" last="Sun">Liang Sun</name><affiliation wicri:level="2"><nlm:aff id="Aff3">Department of Animal & Food Sciences, University of Delaware, Newark, DE USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Delaware</region></placeName><wicri:cityArea>Department of Animal & Food Sciences, University of Delaware, Newark</wicri:cityArea></affiliation></author><author><name sortKey="Garbarino, Alexander" sort="Garbarino, Alexander" uniqKey="Garbarino A" first="Alexander" last="Garbarino">Alexander Garbarino</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore</wicri:cityArea></affiliation></author><author><name sortKey="Schmidt, Carl" sort="Schmidt, Carl" uniqKey="Schmidt C" first="Carl" last="Schmidt">Carl Schmidt</name><affiliation wicri:level="2"><nlm:aff id="Aff3">Department of Animal & Food Sciences, University of Delaware, Newark, DE USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Delaware</region></placeName><wicri:cityArea>Department of Animal & Food Sciences, University of Delaware, Newark</wicri:cityArea></affiliation></author><author><name sortKey="Fang, Jinglong" sort="Fang, Jinglong" uniqKey="Fang J" first="Jinglong" last="Fang">Jinglong Fang</name><affiliation><nlm:aff id="Aff2">Department of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang Province P.R. China</nlm:aff><wicri:noCountry code="subfield">Zhejiang Province P.R. China</wicri:noCountry></affiliation></author><author><name sortKey="Chen, Jian" sort="Chen, Jian" uniqKey="Chen J" first="Jian" last="Chen">Jian Chen</name><affiliation wicri:level="2"><nlm:aff id="Aff1">Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD USA</nlm:aff><country>États-Unis</country><placeName><region type="state">Maryland</region></placeName><wicri:cityArea>Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore</wicri:cityArea></affiliation></author></analytic><series><title level="j">BMC Bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists.</p></sec><sec><title>Results</title><p>We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management.</p></sec><sec><title>Conclusions</title><p>PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at <ext-link ext-link-type="uri" xlink:href="http://raven.anr.udel.edu/~sunliang/PathRings">http://raven.anr.udel.edu/~sunliang/PathRings</ext-link> and the source code is hosted on Github: <ext-link ext-link-type="uri" xlink:href="https://github.com/ivcl/PathRings">https://github.com/ivcl/PathRings</ext-link>.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12859-015-0585-1) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Croft, D" uniqKey="Croft D">D Croft</name></author><author><name sortKey="O Elly, G" uniqKey="O Elly G">G O’Kelly</name></author><author><name sortKey="Wu, G" uniqKey="Wu G">G Wu</name></author><author><name sortKey="Haw, R" uniqKey="Haw R">R Haw</name></author><author><name sortKey="Gillespie, M" uniqKey="Gillespie M">M 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Gillette</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Delaware</li><li>Maryland</li></region></list><tree><noCountry><name sortKey="Fang, Jinglong" sort="Fang, Jinglong" uniqKey="Fang J" first="Jinglong" last="Fang">Jinglong Fang</name></noCountry><country name="États-Unis"><region name="Maryland"><name sortKey="Zhu, Yongnan" sort="Zhu, Yongnan" uniqKey="Zhu Y" first="Yongnan" last="Zhu">Yongnan Zhu</name></region><name sortKey="Chen, Jian" sort="Chen, Jian" uniqKey="Chen J" first="Jian" last="Chen">Jian Chen</name><name sortKey="Garbarino, Alexander" sort="Garbarino, Alexander" uniqKey="Garbarino A" first="Alexander" last="Garbarino">Alexander Garbarino</name><name sortKey="Schmidt, Carl" sort="Schmidt, Carl" uniqKey="Schmidt C" first="Carl" last="Schmidt">Carl Schmidt</name><name sortKey="Sun, Liang" sort="Sun, Liang" uniqKey="Sun L" first="Liang" last="Sun">Liang 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