Pathway Projector: Web-Based Zoomable Pathway Browser Using KEGG Atlas and Google Maps API

Kono, Nobuaki; Arakawa, Kazuharu; Ogawa, Ryu; Kido, Nobuhiro; Oshita, Kazuki; Ikegami, Keita; Tamaki, Satoshi; Tomita, Masaru

doi:10.1371/journal.pone.0007710

Cited by 77 publications

(67 citation statements)

References 48 publications

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“…In the domain of bioinformatics, the Google Maps API is used to visualize multi-scale structures, e.g. Genome Projector, Protein Interactive Network, Gene Coexpression, Pathway Projector [2,[22][23][24].…”

Section: Other Applications With Google Maps Apimentioning

confidence: 99%

Collaborative E-Learning through Open Social Student Modeling and Progressive Zoom Navigation

Liang¹,

Guerra²,

Marai³

et al. 2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-Students usually do not study individually; instead, they tend to study collaboratively. The "power of known peers" can be embraced to provide implicit navigation support for collaborative social e-learning environments. In this paper, we present a novel approach to collaborative e-learning through open social student modeling with Progressive Zoom navigation support. Progressive Zoom is a Google-Maps paradigm which seeks to address information overload issues. It enables students to zoom in or out in a multi-layer fashion, so students can reflect on their individual and peer progress based on the knowledge and pedagogical context. In our initial survey, all students were satisfied with the navigation interface and would recommend the interface to their classmates.

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Section: Other Applications With Google Maps Apimentioning

confidence: 99%

Collaborative E-Learning through Open Social Student Modeling and Progressive Zoom Navigation

Liang¹,

Guerra²,

Marai³

et al. 2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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“…A webbased zoomable Pathway Projector has recently been proposed using the KEGG Atlas and Google Map technologies for magnifying details on global metabolic maps (66). However, such approaches necessarily fail when input metabolites (or enzymes) are not included in KEGG Atlas.…”

Section: Mapping and Visualization Of Metabolomic Data To Biochemicalmentioning

confidence: 99%

Extending Biochemical Databases by Metabolomic Surveys

Fiehn

Barupal

Kind

2011

Journal of Biological Chemistry

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Metabolomics can map the large metabolic diversity in species, organs, or cell types. In addition to gains in enzyme specificity, many enzymes have retained substrate and reaction promiscuity. Enzyme promiscuity and the large number of enzymes with unknown enzyme function may explain the presence of a plethora of unidentified compounds in metabolomic studies. Cataloguing the identity and differential abundance of all detectable metabolites in metabolomic repositories may detail which compounds and pathways contribute to vital biological functions. The current status in metabolic databases is reviewed concomitant with tools to map and visualize the metabolome.Biological databases are indispensable for comparing genomes, proteins, and biological regulation. GenBank TM , Protein Data Bank (PDB), and Gene Expression Omnibus (GEO) are prime examples of how collecting biological information in a coherent manner enables novel insights into evolution and to derive testable hypotheses for gene function, yet biochemical databases on substrate-product relationships and organismspecific metabolic networks have lagged behind. Much of this lag is due to the inherent complexity of enzymology. Small changes in protein folding or in mutations in catalytic sites not only may change reaction kinetics but also have large impact on substrate specificity. Enzymes may have much broader substrate specificity than usually considered. Moreover, many enzymes exert reaction promiscuity (1), which is exploited in bioengineering but which also complicates the reconstruction of metabolic networks. Low-abundant enzymatic side reactions have likely not been reported in favor of the dominant and apparently biologically relevant functions and are consequently lacking in biochemical databases, yet such side reactions may become the major enzyme function through evolutionary pressure. Hence, the number of metabolites per species (or per cell type in multicellular organisms) is hard to predict except for the most conserved metabolic pathways.Accordingly, a surprisingly small fraction of detected metabolites can be readily identified by sensitive screening tools such as HPLC-or GC-coupled MS (Fig. 1). It appears that the metabolome is much larger than anticipated. Phenotypes of species need to be determined by their individual metabolic capacities, defined by the plasticity and flexibility of their metabolic networks. Metabolites can act as intracellular and extracellular signals at very low concentrations and enable communication between organs, as well as serve multiple and vital roles for species in their ecological niches, e.g. for defense or reproductive purposes.

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“…Since a large number of entities are visualized in one graph, it is difficult to visually access the final pathway map. Therefore, a web-enabled zoomable Pathway Projector has recently been developed (Kono et al, 2009). It uses the KEGG atlas and Google map technologies for zooming into global maps.…”

Section: Pathway Mapsmentioning

confidence: 99%

Data Processing, Metabolomic Databases and Pathway Analysis

Fiehn

Kind

Barupal

2011

Annual Plant Reviews Volume 43

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Metabolomics relies on a variety of bioinformatics tools aiming to derive information from data. Data acquisition with chromatography-coupled mass spectrometry yields high volumes of raw data that need to be processed to achieve de-noised and meaningful biochemical data that subsequently can be presented for statistics and pathway analysis in a coherent manner. A variety of new tools and algorithms have recently been developed that help in this process. Nevertheless, a surprisingly low number of metabolic signals can be unambiguously identified. If cutting-edge methods are used, we can confidently interpret signals that can neither be identified by reference standards nor annotated by database matching as 'novel metabolites'. Such methods may comprise high-resolution, high-accurate mass data in conjunction with good data alignment programs that perform peak picking and deconvolution, calculation of elemental formulas, database queries and constraining hit lists by interpreting mass spectral fragmentations and retention-based metabolomic libraries. In an effort to improve standardizations for metabolomic reports, we propose that not only metabolites must be named but also, for all reported metabolites, identifiers or structure codes (InChI keys) from public (bio)chemical databases need to be detailed. Machine-readable structures will vastly accelerate our knowledge of the magnitude and importance of the metabolome in various plant species, organs and physiological conditions. We detail how well-annotated metabolome data can then be used to visualize and interrogate biochemical pathways by matching information to the broad plant databases that currently exist.

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Pathway Projector: Web-Based Zoomable Pathway Browser Using KEGG Atlas and Google Maps API

Cited by 77 publications

References 48 publications

Collaborative E-Learning through Open Social Student Modeling and Progressive Zoom Navigation

Collaborative E-Learning through Open Social Student Modeling and Progressive Zoom Navigation

Extending Biochemical Databases by Metabolomic Surveys

Data Processing, Metabolomic Databases and Pathway Analysis

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