Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed “ePlant” (http://bar.utoronto.ca/eplant) – a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the “3D Data Display Initiative” (http://3ddi.org).
Citation: Fucile G, Di Biase D, Nahal H, La G, Khodabandeh S, Chen Y, et al. (2011) ePlant and the 3D Data Display Initiative: Integrative Systems Biology on the World Wide Web. PLoS ONE 6(1): e15237. https://doi.org/10.1371/journal.pone.0015237
Editor: Shin-Han Shiu, Michigan State University, United States of America
Received: October 1, 2010; Accepted: November 1, 2010; Published: January 10, 2011
Copyright: © 2011 Fucile et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by an NSERC Discovery Grant to NJP. Open access publication costs were defrayed by the Ontario Genomics Institute Genomics Publication Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Model organisms are essential research tools for the biological sciences. Harnessing the full power of these model systems requires integrating data from the many diverse scales of their environment and physiology, and intuitive data displays that are accessible, comprehensible, and expandable by researchers from a broad range of disciplines –. For the model plant Arabidopsis thaliana, large-scale data sets have been generated at many different biological scales  and several algorithms are available to identify associations among these levels ,  towards a systems understanding of biological processes. However, the tools available to interpret these immense data sets are limited with respect to visualization, accessibility and expansion, and the scope of data integration. The integrated and interactive visualization of a broad range of data types has been identified as a fundamental challenge for the future of systems biology , . Effective data visualization facilitates the discovery of relationships between diverse data sets  and is therefore critical for integrative systems biology. With the exception of molecular structure viewers, the visualization of biological data has largely been trapped in two-dimensional (2D) representations. 2D data displays are limiting in two important aspects – humans have evolved an exceptional ability to visualize and conceptualize in the three spatial dimensions of our universe, and displaying these dimensions is critical to all fields of biology. Consider the relationship between protein fold and function, cellular polarity and development, the geographic distribution of organisms and evolution, and the interactions between each of these scopes. Three-dimensional (3D) data displays thus represent enormous potential for the biological sciences, particularly with consideration for the coming widespread availability of display technologies capable of creating 3D images autostereoscopically without requiring special eyewear for users to perceive them . The utility of mapping biological omics data onto 3D anatomical reconstructions of model organisms has been demonstrated in the mouse brain .
Although many excellent computational systems biology tools have been developed with publicly available source code, they are typically encapsulated in one programmatic language without modular compatibility to other programs and must be recompiled for functional expansion –. The accessibility of systems biology data is further limited by the frequent requirement for biologists to download, install, and configure data visualization and analysis software for their specific operating system. The status quo can thus impose steep learning curves and other barriers to user community-driven expansion of systems biology software. These limitations can be addressed through software development on the world wide web . Data accessibility and maintenance can be greatly improved through web services , and data display frameworks designed for web browsers and common scripting languages maximize their accessibility and facilitate their functional expansion by user communities (for example, Jmol: ). Recent years have seen remarkable developments in online resources for molecular systems biology . For example, BioCyc , Reactome –, and KEGG  are vast repositories of manually curated and publicly accessible molecular biological data. These tools allow some degree of data integration. However these tools are represented in 2D and are either network- or pathway-centric and are limited in their range of integrated biological scales.
Herein we report an open-source template for the integration and visualization of systems biology data as interactive 3D representations on the world wide web. We have applied this framework to the important model plant Arabidopsis thaliana in the form of “ePlant” (http://bar.utoronto.ca/eplant). To take advantage of the ePlant framework we generated a proteome-scale protein structure prediction and annotation for Arabidopsis and integrated existing omics-scale data for Arabidopsis. The template used to construct ePlant can be applied to any model organism to achieve intuitive and efficient data retrieval and display. To facilitate the development of 3D data display on the world wide web we have also established the “3D Data Display Initiative” (3DDI - http://3ddi.org). The ePlant framework can be flexibly modified and interact with other web services and data display modules. With only an identifier for a gene of interest, ePlant users can rapidly evaluate protein structure and function, protein-protein interactions, protein subcellular localization, gene expression patterns, and genetic variation. This integrates biological data from nanometer-scale molecular processes to genetic variation based on kilometer-scale geographic distributions. ePlant users can contemplate the relationships between these properties and their genes of interest towards a systems level understanding of model organism biology.
Results and Discussion
Querying the ePlant Data Display Modules
Gene products, such as proteins and RNA transcripts, and many important physiological phenotypes can be unambiguously linked to gene identifiers. An ePlant query thus begins with entering an Arabidopsis Genome Initiative gene identifier (AGI-GI) on the main query page and selecting one of the available modules to explore the properties associated with a query gene and its products. Biological data for the model organism Arabidopsis thaliana is rendered as an interactive 3D display module within the web browser. Currently, ePlant consists of the following modules: a sequence conservation explorer, a protein structure model explorer, a molecular interaction network explorer, a gene product sub-cellular localization explorer, and a gene expression pattern explorer. This form of semantic zooming facilitates the integration of biological data across several scales.
Proteome-Wide Protein Structure Prediction for the Model Plant Arabidopsis
The 3D structure of proteins can provide a wealth of information regarding their biological functions . However, while there are ∼34,000 polypeptides in the most recent TAIR9 collection of Arabidopsis proteins (http://www.arabidopsis.org) the Protein Data Bank (http://www.rcsb.org) contains only ∼62,000 macromolecular structures, with 2488 structure models from the Viridiplantae and only 495 from Arabidopsis at the time of preparing this manuscript. It is therefore difficult for researchers to find protein structural data directly related to their genes of interest. To address this knowledge gap we determined theoretical protein structures for the Arabidopsis proteome using the Phyre homology modeling method  with the TAIR9 proteome, including splice variants, as input sequences. We obtained 67,275 predicted protein structure models with the highest level of confidence, as per , for ∼72% of the Arabidopsis proteome. Most of the predicted protein structures span less than the entire amino acid sequence of each TAIR9 polypeptide. This results from the current implementation of Phyre which uses one template for homology modeling per protein sequence. The distribution of percent amino acid sequence coverage for this collection of predicted protein structures is bi-modal, with one peak at ∼35% coverage and the other at ∼80% coverage (Figure 1A). The distribution of sequence length in these two modes reveals that Phyre typically achieves greater sequence coverage for longer protein sequences (Figure 1B). The percent sequence coverage reported in Figure 1 understates the total sequence coverage for a given TAIR9 protein, as up to three predicted protein structures were generated for each TAIR9 protein sequence and in some cases map to separate sequence regions. Forthcoming implementations of Phyre can integrate multiple independent structure prediction templates to produce one multi-domain protein structure model. The Phyre models and the mapping of curated sites onto these models can be validated against experimentally characterized protein structures (Figure 2A–2B and Figure S1).
A) A bar graph showing the number of Phyre-predicted  protein structure models from the TAIR9 Arabidopsis proteome by percent amino acid sequence coverage of the model based on the full-length TAIR9 template sequence. B) A scatter plot of the number of predicted protein models versus amino acid sequence length of the models for those models with percent sequence coverage between 0–55% (X) and 56–100% (+).
A) Screenshot depicting the x-ray crystallographic dimeric protein structure model of Arabidopsis TATA binding protein 1 (TBP1, At3g13445) complexed with DNA, PDB:1QN3. The Jmol  van der Waal's surface rendering of the protein is colored with a blue to red gradient representing low to high mobility as calculated by the temperature factor of the model. The alpha carbons of the sites defining the DNA interaction surface as curated by the Conserved Domain – entry CD00652 are highlighted in yellow. B) Screenshot depicting the predicted monomeric protein structure model of Arabidopsis TBP1 (At3g13445), built with PDB:1MP9 as a template for homology modeling using Phyre . The alpha carbons of the sites defining the DNA interaction surface as curated by the Conserved Domain entry CD00652 are highlighted in yellow. The Jmol van der Waal's surface rendering is monochromatic grey as temperature factors are not currently calculated for predicted structures. C) Screenshot depicting the predicted protein structure model for the Arabidopsis water channel protein encoded by At2g45960. The ribbon model is colored grey for hydrophobic residues, which reside in the plasma membrane, and colored red for polar and charged residues which face the aqueous cellular interior and solvent exterior. D) Screenshot depicting the predicted protein structure model for the Arabidopsis ubiquitin-protein ligase encoded by At2g30110. The protein is shown as a ribbon diagram with a monochromatic grey surface. The loop and helical regions colored in yellow define the heterodimer interaction surface of the protein, described by CD01493.
ePlant Protein Structure Explorer
ePlant Sequence Explorer
The ability to assess sequence conservation within and between species is informative regarding sequence evolution and is critical to understanding the function of gene products. When integrated with knowledge of the geographical distribution of species and their genetic variation, these data provide kilometer scale resolution for the biology of model organisms. The ePlant sequence explorer is a first attempt at the 3D display of primary sequence data. We have incorporated 123,484 single nucleotide polymorphisms using data from ,  into the ePlant Sequence Explorer. Figure 3A shows a cluster of polymorphic sites for an oxidoreductase encoded by At4g04930. The interactive display indicates synonymous and non-synonymous polymorphic sites, the alternate amino acid encoded by non-synonymous polymorphisms, and the frequency and ecotypic distribution at each polymorphic site. Figure 3B illustrates the 3D representation of sequence data for the TIR1 auxin receptor encoded by At3g62980. Amino acid sequences of paralogs and putative orthologs (computed by Patel R and Provart NJ et al., manuscript in preparation) of the query gene are aligned across separate axes, with putative orthologous sequences at a plane that is orthogonal to the paralogs and splice variants of the query Arabidopsis gene. For example, this 3D multiple sequence alignment may be scrolled and rotated to view only the putatative orthologs or paralogs. Each of the one letter amino acid sites in the ePlant Structure Explorer are rendered as separate objects to integrate additional biological data. The color of the one-letter amino acid codes represents physico-chemical properties such as charge and solubility and the size of the letters are scaled to conservation scores in the alignment. This also allows the integration of primary sequence data with the cognate folded 3D protein structure by means of hyperlinks. Each letter is clickable, allowing the user to see the location of the residue in the cognate structure in the ePlant protein structure model explorer. Upon clicking a residue of interest, the user is prompted to select a protein structure related to their query sequence, which is rendered in the structure explorer module with the residue of interest labeled and highlighted in red.
ePlant Molecular Interactions Explorer
The abstraction of biological systems as networks is a powerful approach to understanding their functions , . Representing biological networks in 3D can facilitate user interaction with large and complicated data sets . The ePlant Molecular Interactions Viewer is a template for the visualization and analysis of biological networks in 3D using the Jmol rendering engine  and therefore has the advantage of deployment on the web and functional expansion through scripting. This module currently supports the interactive exploration of 70,944 predicted and 4,300 documented Arabidopsis protein-protein interactions, derived from ,  and others. Proteins are represented as spheres and edges connecting the spheres indicate undirected protein-protein interactions. The models are centered on the protein product of the query gene and by default display edges to the query's interaction neighbors as well as the query neighbors' neighbors. These “two-step” models thus describe the local protein-protein interaction neighborhood of the query gene. The large two-step interaction network centered on Arabidopsis TBP1, encoded by At3g13445, is shown in Figure 4A. Nodes and edges can be rendered dynamically to reflect additional data sets and network properties. For example, nodes can be colored to indicate sub-cellular localizations of the proteins in the two-step network, the size of nodes and edges can be rendered to reflect metrics of interaction confidence values, the expression correlation of the mRNA transcripts associated with the proteins in the two-step network, or other network properties such as clustering coefficients (Figure 4B). Protein interaction partners and other network data are available for download as plain text from the ePlant Molecular Interactions Viewer. Network topologies in the two-step protein-protein interaction models can illustrate ordered structures related to underlying biological phenomena such as protein complex formation and the connectivity between functional modules such as signal transduction pathways between sub-cellular compartments , . As networks are useful abstractions for many systems outside the realm of biology, our method for rendering networks in 3D on the web may find application to other fields of inquiry.
A) Screenshot displaying the “two-step” protein-protein interaction network centered on the Arabidopsis TBP1 (At3g13445). B) Protein-protein interaction network centered on an Arabidopsis protein of unknown function encoded by At1g13330. This layout was created by first re-sizing the query neighbors by mRNA expression correlation coefficients (shown in yellow), followed by selecting all edges between the query neighbors' neighbors and hiding the query neighbors' neighbors. As a result, the green edges depict interactions between the query's neighbors and can be used to validate network clustering coefficients calculations. The query node is scaled by its clustering coefficient (0.80), and all nodes are colored blue based on nuclear localization.
Gene Expression Patterns and Gene Product Localization at Tissue and Subcellular Scales
We have developed gross anatomical, tissue level, and sub-cellular models of Arabidopsis thaliana to integrate molecular omics data at physiological scales (mm-cm). For example, the mRNA expression patterns of a gene can be painted onto a 3D anatomical model of Arabidopsis (Figure 5A,B) and the subcellular localization of a gene's protein product can also be painted onto a 3D model of a plant cell (Figure 5C,D). Gene expression data for the whole-plant model of Arabidopsis are from . Subcellular gene product localization data are from the SUBA database . At the ‘Tissue Expression’ level, expression data from 34 different tissues and cell types may be explored (Figure 6A,B). The data are from guard cells and mesophyll cells  (http://biology.ucsd.edu/labs/schroeder/guardcellchips.html), xylem and cork (Campbell M, unpublished - http://affymetrix.arabidopsis.info/narrays/experimentpage.pl?experimentid=92), stigma and ovaries , stem epidermis , 15 cell types from 5 layers and 3 ages of the root , three areas of the shoot apical meristem , four pollen developmental stages , dry and imbibed seeds , and three pollen germination stages . ePlant permits easy access to 2.78 million gene expression measurements, and documented subcellular localizations for 6,897 Arabidopsis proteins and predicted subcellular localizations for most of the remainder of the Arabidopsis proteome. The Arabidopsis whole-plant, tissue, and cellular models are specified by the Collada Data Asset Exchange format (http://collada.org) and rendered in the web browser using PaperVision3D (http://papervision3D.org) (Figure 5A,C) or Google's O3D (http://code.google.com/apis/o3d) (Figure 5B,D). A prototype using the latter is available at http://3DDI.org. These models can be freely rotated and signal values can be painted dynamically.
A) Screenshot showing the gene expression patterns for Arabidopsis TBP1 (At3g13445) using the PaperVision3D rendering engine. B) O3D rendering of the the same model of gene expression for Arabidopsis TBP1 (At3g13445) as shown in panel A. C) ePlant Subcellular Localization Explorer showing the nuclear localization signal for Arabidopsis TBP1 (At3g13445) in a wireframe cartoon representation of a plant cell, rendered with PaperVision3D. D) O3D rendering of the same model of subcellular localization for Arabidopsis TBP1 (At3g13445) shown in panel C.
A) Screenshot showing the relative gene expression level of the potassium transporter encoded by At5g46140 across several tissue types. B) Screenshot showing the absolute gene expression level of the auxin transporter encoded by At1g73590. Individual tissue types can be identified by name and source data by clicking the identifiers in the list.
Materials and Methods
Data Storage and Retrieval
The fast performance of ePlant is achieved by rapid data transfer via JSON-type Representational State Transfer (REST) web services, server-side MySQL database queries at the Bio-Array Resource (http://bar.utoronto.ca) or the SUBA database  (http://suba.plantenergy.uwa.edu.au), and the ability of web browser-based rendering engines to use the memory and processing resources on the ePlant user's local computing device. All sequence data for Arabidopsis was retrieved from The Arabidopsis Information Resource (http://www.arabidopsis.org). Homologous sequences were retrieved from the National Center for Biotechnology Information (http://ncbi.nlm.nih.gov) or TIGR (Patel R and Provart NJ et al., manuscript in preparation). Protein sub-cellular localization assignments are retrieved via webservices from SUBA .
Arabidopsis Protein Structure Prediction and Annotation
The TAIR9 protein sequences, including splice variants, were used as input for the high-throughput structure prediction of the Arabidopsis proteome. Phyre models of the TAIR9 protein sequences with confidence values of 100% as per  were considered significant. TAIR9 protein sequences were mapped to the implicit protein sequence from each of the structure model PDB files using BLAST-P with an expect value E< 1e-5. Structure model implicit sequences were then compared to the Conserved Domain Database (CDD) ,  using RPS-BLAST. Matches to CDD profiles with E< 1e-5 were considered significant. Curated CDD amino acid sites of functional importance were mapped from cddannot.dat and the CDD master sequence onto the implicit primary sequence of the protein structure model sequence using Bioperl  methods on the RPS-BLAST alignments. Briefly, the site mapping algorithm counts the gaps in the homology string sequence for each RPS-BLAST hit to map the curated sites in the CDD master sequence onto the implicit PDB sequence. Multiple sequence alignment displays in the ePlant Molecular Interactions Explorer of the implicit protein model, CDD profile, and query sequences are generated on the server-side with ClustalW  using a BLOSUM matrix.
Arabidopsis Protein-Protein Interaction Network Layout
The 3D layout of the two-step networks were computed by the Mathematica 7.0 kernel (64-bit)  on a Linux architecture using the ‘GraphCoordinates3D’ function with the ‘Method’ parameter set to ‘Automatic’. The coordinates of each node in the 3D layout were retrieved using the Mathematica function ‘VertexList’ and the ‘GraphUtilities’ library . Perl scripts were used to pass the network edges from a MySQL database on the BAR server to Mathematica and convert the 3D coordinates of the nodes in the two-step network to the .XYZ format for rendering in the Jmol applet . There is a separate .xyz layout file for each TAIR9 protein with one or more documented or predicted protein-protein interactions as cataloged by ,  and housed on the BAR server . Protein-protein interaction confidence values were calculated as per  and clustering coefficients calculated as per . mRNA transcript expression correlation scores were calculated as per  across approximately 1000 different microarray data sets from the AtGenExpress Consortium, comprising gene expression data from a developmental series , abiotic  and biotic stresses, and hormones and chemicals .
Homology Relationships and Rendering of Sequence Data
Arabidopsis inparalogs and homologous sequences from Populus trichocarpa (poplar), Medicago trunculata, Oryza sativa (rice), and Hordeum vulgare (barley) were computed using OrthoMCL  by Rohan Patel and will be published elsewhere (Patel R and Provart NJ, manuscript in preparation). The sequence data comprising the homolog alignments are retrieved via webservices and aligned using MAFFT . The model of the alignments and annotations are written using ActionScript and rendered with PaperVision3D.
Mapping Single Nucleotide Polymorphism Data
Single nucleotide polymorphism data from ,  were mapped to the coding and amino acid sequences displayed in the ePlant Sequence Explorer using Perl scripts and the BioPerl  library. The coding sequence of the query gene is aligned to the sequence fragments flanking and including polymorphic sites from ,  using the (ends-free) Needleman-Wunsch dynamic programming algorithm and the FULLMAT substitution matrix implemented in EMBOSS. The FULLMAT matrix gives exact nucleotide/amino acid matches a score of 5 and mismatches a score of -4, which preserves the inequality “gap open < mismatch < gap extend < match”.
Availability and Future Directions
The ePlant framework has the potential for many novel extensions to systems biology data integration. Careful statistical analyses of co-evolving amino acid sites that can reliably detect co-evolving protein sectors  and other metrics of evolutionary constraints  could be computed in high-throughput for entire proteomes and incorporated into the ePlant Sequence Explorer and Protein Structure Model Explorer. Genetic variation such as single nucleotide polymorphisms, e.g. from the 1001 Arabidopsis genomes project , could be similarly incorporated into the ePlant Sequence Explorer and Protein Structure Model Explorer. The ePlant Molecular Interactions Viewer could be extended by additional 3D layout algorithms and the inclusion of multipartite networks that include RNA, metabolic networks – and/or the small molecules of metabolism and signal transduction. These networks could incorporate data from biological small molecule resources such as the Golm Metabolome Database . Computational modeling of many biological molecules in a complex cellular environment can provide invaluable insight into biological processes , . Web-based 3D rendering engines can support libraries of complex physics functions for both objects and environments. This raises the possibility of computationally modeling the coordination between morphological development and molecular function using methods that are accessible to a broad range of researchers with minimal training in computer programming. Powerful server-side applications, such as the Bioconductor packages for R , E-Cell , , or Mathematica , could dynamically compute the properties of a biological model with user-provided parameters via web browsers and return these modeling data for rendering in a web browser. The anatomical and physiological descriptions of Arabidopsis currently used by ePlant are essentially cartoon representations. Ideally, the data display modules would render 3D representations of anatomy from direct measurements such as magnetic resonance imaging (MRI) and Z-stacks of confocal microscopic images. This has in principle been achieved by projects such as Cortona3D (web browser plug-in; http://www.cortona3d.com), which can render 3D objects reconstructed from MRI-based anatomical descriptions (Figure S3). Public repositories of 3D reconstructions of biological materials from MRI and microscopy studies already exist, such as the fMRI Data Center (http://www.fmridc.org) and the Cell Centered Database (http://ccdb.ucsd.edu).
The ePlant framework for systems biology analyses on the world wide web includes an open-source policy for community development, script-accessibility, functional independence of operating system type, and the ability to dynamically render object and environmental properties in 3D. The data display modules are designed to allow interaction with any other module which can accept and pass parameters through RESTful channels. This allows content creators to choose their tools, such as Processing (http://processing.org), for developing data display modules. In the current implementation of ePlant, 3D models are specified by Collada or PDB objects and the interaction with these objects is handled by a rendering engine through the web browser which incorporates biological data from web service streams and manages user input. RESTful web services allow flexible reformatting of the “raw” biological data using human-interpretable formats such as JSON. This enables the data to be efficiently served according to the specifications of any rendering engine. This is important, as technologies associated with 3D rendering on the web are advancing rapidly. Currently, the ePlant modules are rendered by either PaperVision3D/Flash, Google's O3D, or Jmol. When rendered using PaperVision3D (Figure A,C) the Collada models are loaded and manipulated quickly on a wide variety of computing systems tested. Google's O3D rendering engine (Figure B,D) generates much richer 3D depth and performance compared to the Flash rendering engine. However this plug-in version of O3D was experimental and has now been implemented using WebGL (http://www.khronos.org/webgl). WebGL is integrated with the HTML 5 “canvas” elements allowing declarative rendering of 3D content without the use of plug-in software (http://X3DOM.org) and is under active development to become the standard for 3D content on the web . The fluidity of integration between the ePlant modules could be improved by implementing all of the modules with one rendering pipeline, such as WebGL. This would allow a seamless “zoom” from the function of organisms at the meter scale to the nanometer scale of protein function in one continuous environment. However, a combination of declarative and plug-in based 3D rendering will probably continue to be used. For example, it would require enormous programming efforts to replace Jmol's wealth of features for the study of molecules at the nanometer scale. It is also unlikely that the PDB format for the description of protein structures will change in the near future. We have demonstrated that protein structure models can be transcribed from the Protein Data Bank Markup Language (PDBML) specifications into the Collada mark-up language (Figure S4). However, the performance of these models when rendered using PaperVision3D/Flash is very poor compared to Jmol. Collada was designed as an intermediate exchange format for 3D content. Collada has the benefit of interchangeability with many common formats at the cost of relatively large file sizes with many unused properties. However, these Collada models can be easily parsed into more compact custom formats compatible with the evolving standards of 3D rendering engines.
Screenshot of the ePlant Protein Structure Explorer. A) The nucleotide binding site and B) the shikimate binding site described by CDD model cd00464 mapped onto a Phyre-predicted structure of an Arabidopsis shikimate kinase encoded by At2g21940. C) The crystallographic structure of At2g21940 (PDB:3NWJ) with the nucleotide binding site, as per , shown in yellow, D) The crystallographic structure of At2g21940 (PDB:3NWJ) with the shikimate binding site, as per , shown in yellow.
Screenshots from extant visualization tools for CDD models. A) Screenshot of a 2D schematic representations of CDD sites mapping to TBP1 encoded by At3g13445 returned from a BLAST analysis  (http://blast.ncbi.nlm.nih.gov). B) Screenshot of conserved domain structure for TBP1 encoded by At3g13445 returned from an InterProScan query  (http://www.ebi.ac.uk/Tools/InterProScan).
Screenshot of a 3D reconstruction of striatum and cerebral cortex of a monkey from the genus Callicebus from anti-KChIP2b immunostains. The reconstruction was rendered in a web browser using the Cortona3D plug-in (http://www.cortona3d.com). The 3D model was downloaded from the 3D Brain Objects (VRML) Database (http://brainmaps.org).
Rendering of structure models. Structure model of the Arabidopsis Leafy transcription factor bound to DNA (PDB accession 2VY2) transcribed from PDBML to the Collada format and rendered using the SwirlX3D viewer (http://www.pinecoast.com). The Leafy peptide bond alpha-carbon and nitrogen atoms are shown in green and blue, respectively. Atoms of the bound DNA molecule are shown in red.
We are grateful to Lev Kolobov for generating the 3D models used for the plant and cell views, and to Rohan Patel in the Provart Lab for providing access to homologous sequences.
Conceived and designed the experiments: NJP GF LK. Wrote the paper: GF NJP. Designed the software described: DD HN GL SK YK YC KE DC. Predicted structures: LK.
- 1. Ge H, Walhout AJ, Vidal M (2003) Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet 19: 551–560.H. GeAJ WalhoutM. Vidal2003Integrating ‘omic’ information: a bridge between genomics and systems biology.Trends Genet19551560
- 2. McDermott J, Samudrala R (2003) BIOVERSE: Functional, structural, and contextual annotation of proteins and proteomes. Nucleic Acids Res 31: 3736–3737.J. McDermottR. Samudrala2003BIOVERSE: Functional, structural, and contextual annotation of proteins and proteomes.Nucleic Acids Res3137363737
- 3. Garcia Castro A, Chen YP, Ragan MA (2005) Information integration in molecular bioscience. Appl. Bioinformatics 4: 157–173.A. Garcia CastroYP ChenMA Ragan2005Information integration in molecular bioscience.Appl. Bioinformatics4157173
- 4. Stephens SM, Rung J (2006) Advances in systems biology: measurement, modeling and representation. Curr Opin Drug Discov Devel 9: 240–250.SM StephensJ. Rung2006Advances in systems biology: measurement, modeling and representation.Curr Opin Drug Discov Devel9240250
- 5. Kahlem P, Birney E (2007) ENFIN a network to enhance integrative systems biology. Ann N Y Acad Sci 1115: 23–31.P. KahlemE. Birney2007ENFIN a network to enhance integrative systems biology.Ann N Y Acad Sci11152331
- 6. Zhong W, Sternberg PW (2007) Automated data integration for developmental biological research. Development 134: 3227–3228.W. ZhongPW Sternberg2007Automated data integration for developmental biological research.Development13432273228
- 7. de Tayrac M, Lê S, Aubry M, Mosser J, Husson F (2009) Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach. BMC Genomics 10: 32.M. de TayracS. LêM. AubryJ. MosserF. Husson2009Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach.BMC Genomics1032
- 8. Brady SM, Provart NJ (2009) Web-queryable large-scale data sets for hypothesis generation in plant biology. Plant Cell 21: 1034–1051.SM BradyNJ Provart2009Web-queryable large-scale data sets for hypothesis generation in plant biology.Plant Cell2110341051
- 9. Coruzzi G, Gutierrez R, Shasha D, Katari M, Gifford M, et al. (2006) A systems approach to nitrogen networks and the “VirtualPlant”. Dev Biol 295: 327.G. CoruzziR. GutierrezD. ShashaM. KatariM. Gifford2006A systems approach to nitrogen networks and the “VirtualPlant”.Dev Biol295327
- 10. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q (2008) GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 9: S4.S. MostafaviD. RayD. Warde-FarleyC. GrouiosQ. Morris2008GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function.Genome Biol9S4
- 11. Gehlenborg N, O'Donoghue SI, Baliga NS, Goesmann A, Hibbs Ma, et al. (2010) Visualization of omics data for systems biology. Nat Methods 7: S56–68.N. GehlenborgSI O'DonoghueNS BaligaA. GoesmannMa Hibbs2010Visualization of omics data for systems biology.Nat Methods7S5668
- 12. O'Donoghue SI, Gavin AC, Gehlenborg N, Goodsell DS, Hériché JK, et al. (2010) Visualizing biological data-now and in the future. Nat Methods 7: S2–4.SI O'DonoghueAC GavinN. GehlenborgDS GoodsellJK Hériché2010Visualizing biological data-now and in the future.Nat Methods7S24
- 13. Card SK, Mackinlay J, Shneiderman B (1999) Readings in information visualization: using vision to think. Burlington, MA: Morgan Kaufmann Publishers. 712 p.SK CardJ. MackinlayB. Shneiderman1999Readings in information visualization: using vision to think.Burlington, MAMorgan Kaufmann Publishers712
- 14. The Economist Technology Quarterly (2009) 3-D: it's nearly there. The Economist 392: 32–34.The Economist Technology Quarterly20093-D: it's nearly there.The Economist3923234
- 15. Lau C, Ng L, Thompson C, Pathak S, Kuan L, et al. (2008) Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain. BMC Bioinfo 9: 153.C. LauL. NgC. ThompsonS. PathakL. Kuan2008Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain.BMC Bioinfo9153
- 16. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, et al. (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37: 914–39.O. ThimmO. BläsingY. GibonA. NagelS. Meyer2004MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes.Plant J3791439
- 17. Pavlopoulos GA, O'Donoghue SI, Satagopam VP, Soldatos TG, Pafilis E, et al. (2008) Arena3D: visualization of biological networks in 3D. BMC Syst Biol 2: 104.GA PavlopoulosSI O'DonoghueVP SatagopamTG SoldatosE. Pafilis2008Arena3D: visualization of biological networks in 3D.BMC Syst Biol2104
- 18. Gehlenborg N, Yan W, Lee IY, Yoo H, Nieselt K, et al. (2009) Prequips–an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data. Bioinformatics 25: 682–6833.N. GehlenborgW. YanIY LeeH. YooK. Nieselt2009Prequips–an extensible software platform for integration, visualization and analysis of LC-MS/MS proteomics data.Bioinformatics256826833
- 19. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U (2009) Computational modeling of biochemical networks using COPASI. Methods Mol Biol 500: 17–59.P. MendesS. HoopsS. SahleR. GaugesJ. DadaU. Kummer2009Computational modeling of biochemical networks using COPASI.Methods Mol Biol5001759
- 20. Paananen J, Wong G (2009) FORG3D: force-directed 3D graph editor for visualization of integrated genome scale data. BMC Syst Biol 3: 26.J. PaananenG. Wong2009FORG3D: force-directed 3D graph editor for visualization of integrated genome scale data.BMC Syst Biol326
- 21. Theocharidis A, van Dongen S, Enright AJ, Freeman TC (2009) Network visualisation and analysis of gene expression data using BioLayout Express3D. Nature Protocols 4: 1535–50.A. TheocharidisS. van DongenAJ EnrightTC Freeman2009Network visualisation and analysis of gene expression data using BioLayout Express3D.Nature Protocols4153550
- 22. Sagotsky JA, Zhang L, Wang Z, Martin S, Deisboeck TS (2008) Life Sciences and the web: a new era for collaboration. Mol Syst Biol 4: 201.JA SagotskyL. ZhangZ. WangS. MartinTS Deisboeck2008Life Sciences and the web: a new era for collaboration.Mol Syst Biol4201
- 23. Neerincx PB, Leunissen JA (2005) Evolution of web services in bioinformatics. Brief Bioinform 6: 178–188.PB NeerincxJA Leunissen2005Evolution of web services in bioinformatics.Brief Bioinform6178188
- 24. Jmol: an open-source Java viewer for chemical structures in 3D. Jmol project website.Jmol: an open-source Java viewer for chemical structures in 3D. Jmol project website.Available: http://www.jmol.org/. Accessed 2009 Dec 12. Available: http://www.jmol.org/. Accessed 2009 Dec 12.
- 25. Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34: D504–D506.GD BaderMP CaryC. Sander2006Pathguide: a pathway resource list.Nucleic Acids Res34D504D506
- 26. Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, et al. (2005) Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33: 6083–9.PD KarpCA OuzounisC. Moore-KochlacsL. GoldovskyP. Kaipa2005Expansion of the BioCyc collection of pathway/genome databases to 160 genomes.Nucleic Acids Res3360839
- 27. Vastrik I, D'Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, et al. (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biology 8: R39.I. VastrikP. D'EustachioE. SchmidtG. Joshi-TopeG. Gopinath2007Reactome: a knowledge base of biologic pathways and processes.Genome Biology8R39
- 28. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, et al. (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37: D619–22.L. MatthewsG. GopinathM. GillespieM. CaudyD. Croft2009Reactome knowledgebase of human biological pathways and processes.Nucleic Acids Res37D61922
- 29. Tsesmetzis N, Couchman M, Higgins J, Smith A, Doonan JH, et al. (2008) Arabidopsis reactome: a foundation knowledgebase for plant systems biology. Plant Cell 20: 1426–1436.N. TsesmetzisM. CouchmanJ. HigginsA. SmithJH Doonan2008Arabidopsis reactome: a foundation knowledgebase for plant systems biology.Plant Cell2014261436
- 30. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27–30.M. KanehisaS. Goto2000KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res282730
- 31. Lee D, Redfern O, Orengo C (2007) Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 8: 995–1005.D. LeeO. RedfernC. Orengo2007Predicting protein function from sequence and structure.Nat Rev Mol Cell Biol89951005
- 32. Kelley LA, Sternberg MJE (2009) Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 4: 363–371.LA KelleyMJE Sternberg2009Protein structure prediction on the Web: a case study using the Phyre server.Nat Protoc4363371
- 33. Marchler-Bauer A, Anderson JB, Derbyshire MK, DeWeese-Scott C, Gonzales NR, et al. (2007) CDD: a conserved domain database for interactive domain family analysis. Nucleic Acids Res 35: D237–240.A. Marchler-BauerJB AndersonMK DerbyshireC. DeWeese-ScottNR Gonzales2007CDD: a conserved domain database for interactive domain family analysis.Nucleic Acids Res35D237240
- 34. Marchler-Bauer A, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. (2009) CDD: specific functional annotation with the Conserved Domain Database. Nucleic Acids Res 37: D205–210.A. Marchler-BauerJB AndersonF. ChitsazMK DerbyshireC. DeWeese-Scott2009CDD: specific functional annotation with the Conserved Domain Database.Nucleic Acids Res37D205210
- 35. Jander G, Norris SR, Rounsley SD, Bush DF, Levin IM, et al. (2002) Arabidopsis map-based cloning in the post-genome era. Plant Physiol 129: 440–450.G. JanderSR NorrisSD RounsleyDF BushIM Levin2002Arabidopsis map-based cloning in the post-genome era.Plant Physiol129440450
- 36. Nordborg M, Hu TT, Ishino Y, Jhaveri J, Toomajian C, et al. (2005) The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol 3: e196.M. NordborgTT HuY. IshinoJ. JhaveriC. Toomajian2005The pattern of polymorphism in Arabidopsis thaliana.PLoS Biol3e196
- 37. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8: 450–461.U. Alon2007Network motifs: theory and experimental approaches.Nat Rev Genet8450461
- 38. Merico D, Gfeller D, Bader GD (2009) How to visually interpret biological data using networks. Nat Biotechnol 27: 921–924.D. MericoD. GfellerGD Bader2009How to visually interpret biological data using networks.Nat Biotechnol27921924
- 39. Bader GD, Betel D, Hogue CW (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 31: 248–250.GD BaderD. BetelCW Hogue2003BIND: the Biomolecular Interaction Network Database.Nucleic Acids Res31248250
- 40. Geisler-Lee J, O'Toole N, Ammar R, Provart NJ, Millar AH, et al. (2007) A predicted interactome for Arabidopsis. Plant Physiol 145: 317–329.J. Geisler-LeeN. O'TooleR. AmmarNJ ProvartAH Millar2007A predicted interactome for Arabidopsis.Plant Physiol145317329
- 41. Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, et al. (2005) A gene expression map of Arabidopsis thaliana development. Nat Gen 37: 501–506.M. SchmidTS DavisonSR HenzUJ PapeM. Demar2005A gene expression map of Arabidopsis thaliana development.Nat Gen37501506
- 42. Heazlewood JL, Verboom RE, Tonti-Filippini J, Small I, Millar AH (2007) SUBA: the Arabidopsis Subcellular Database. Nucleic Acids Res 35: D213–218.JL HeazlewoodRE VerboomJ. Tonti-FilippiniI. SmallAH Millar2007SUBA: the Arabidopsis Subcellular Database.Nucleic Acids Res35D213218
- 43. Yang Y, Costa A, Leonhardt N, Siegel RS, Schroeder JI, et al. (2008) Isolation of a strong Arabidopsis guard cell promoter and its potential as a research tool. Plant Methods 4: 6.Y. YangA. CostaN. LeonhardtRS SiegelJI Schroeder2008Isolation of a strong Arabidopsis guard cell promoter and its potential as a research tool.Plant Methods46
- 44. Swanson R, Clark T, Preuss D (2005) Expression profiling of Arabidopsis stigma tissue identifies stigma-specific genes. Sex Plant Reprod 18: 163–171.R. SwansonT. ClarkD. Preuss2005Expression profiling of Arabidopsis stigma tissue identifies stigma-specific genes.Sex Plant Reprod18163171
- 45. Suh MC, Samuels AL, Jetter R, Kunst L, Pollard M, et al. (2005) Cuticular lipid composition, surface structure, and gene expression in Arabidopsis stem epidermis. Plant Physiol 139: 1649–1665.MC SuhAL SamuelsR. JetterL. KunstM. Pollard2005Cuticular lipid composition, surface structure, and gene expression in Arabidopsis stem epidermis.Plant Physiol13916491665
- 46. Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, et al. (2003) A gene expression map of the Arabidopsis root. Science 302: 1956–1960.K. BirnbaumDE ShashaJY WangJW JungGM Lambert2003A gene expression map of the Arabidopsis root.Science30219561960
- 47. Yadav RK, Girke T, Pasala S, Xie M, Reddy GV (2009) Gene expression map of the Arabidopsis shoot apical meristem stem cell niche. Proc Natl Acad Sci U S A 106: 4941–4946.RK YadavT. GirkeS. PasalaM. XieGV Reddy2009Gene expression map of the Arabidopsis shoot apical meristem stem cell niche.Proc Natl Acad Sci U S A10649414946
- 48. Honys D, Twell D (2004) Transcriptome analysis of haploid male gametophyte development in Arabidopsis. Genome Biol 5: R85.D. HonysD. Twell2004Transcriptome analysis of haploid male gametophyte development in Arabidopsis.Genome Biol5R85
- 49. Nakabayashi K, Okamoto M, Koshiba T, Kamiya Y, Nambara E (2005) Genome-wide profiling of stored mRNA in Arabidopsis thaliana seed germination: epigenetic and genetic regulation of transcription in seed. Plant J 41: 697–709.K. NakabayashiM. OkamotoT. KoshibaY. KamiyaE. Nambara2005Genome-wide profiling of stored mRNA in Arabidopsis thaliana seed germination: epigenetic and genetic regulation of transcription in seed.Plant J41697709
- 50. Qin Y, Leydon AR, Manziello A, Pandey R, Mount D, et al. (2009) Penetration of the stigma and style elicits a novel transcriptome in pollen tubes, pointing to genes critical for growth in a pistil. PLoS Genet 5: e1000621.Y. QinAR LeydonA. ManzielloR. PandeyD. Mount2009Penetration of the stigma and style elicits a novel transcriptome in pollen tubes, pointing to genes critical for growth in a pistil.PLoS Genet5e1000621
- 51. Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, et al. (2002) The Bioperl toolkit: Perl modules for the life sciences. Genome Res 12: 1611–1618.JE StajichD. BlockK. BoulezSE BrennerSA Chervitz2002The Bioperl toolkit: Perl modules for the life sciences.Genome Res1216111618
- 52. Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, et al. (2003) Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res 31: 3497–3500.R. ChennaH. SugawaraT. KoikeR. LopezTJ Gibson2003Multiple sequence alignment with the Clustal series of programs.Nucleic Acids Res3134973500
- 53. Wolfram Research, Inc. (2008) Mathematica, Version 7.0, Champaign, IL.Wolfram Research, Inc.2008Mathematica, Version 7.0, Champaign, IL.
- 54. Toufighi K, Brady SM, Austin R, Ly E, Provart NJ (2005) The Botany Array Resource: e-Northerns, Expression Angling, and promoter analyses. Plant J 43: 153–163.K. ToufighiSM BradyR. AustinE. LyNJ Provart2005The Botany Array Resource: e-Northerns, Expression Angling, and promoter analyses.Plant J43153163
- 55. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442.DJ WattsSH Strogatz1998Collective dynamics of ‘small-world’ networks.Nature393440442
- 56. Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, et al. (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J 50: 347–363.J. KilianD. WhiteheadJ. HorakD. WankeS. Weinl2007The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses.Plant J50347363
- 57. Goda H, Sasaki E, Akiyama K, Maruyama-Nakashita A, Nakabayashi K, et al. (2008) The AtGenExpress hormone- and chemical-treatment data set: Experimental design, data evaluation, model data analysis, and data access. Plant J 55: 526–542.H. GodaE. SasakiK. AkiyamaA. Maruyama-NakashitaK. Nakabayashi2008The AtGenExpress hormone- and chemical-treatment data set: Experimental design, data evaluation, model data analysis, and data access.Plant J55526542
- 58. Li L, Stoeckert CJ Jr, Roos DS (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13: 2178–2189.L. LiCJ Stoeckert JrDS Roos2003OrthoMCL: identification of ortholog groups for eukaryotic genomes.Genome Res1321782189
- 59. Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30: 3059–3066.K. KatohK. MisawaK. KumaT. Miyata2002MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.Nucleic Acids Res3030593066
- 60. Halabi N, Rivoire O, Leibler S, Ranganathan R (2009) Protein sectors: evolutionary units of three-dimensional structure. Cell 138: 774–786.N. HalabiO. RivoireS. LeiblerR. Ranganathan2009Protein sectors: evolutionary units of three-dimensional structure.Cell138774786
- 61. Binkley J, Karra K, Kirby A, Hosobuchi M, Stone EA, et al. (2010) ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses. Genome Res 20: 142–154.J. BinkleyK. KarraA. KirbyM. HosobuchiEA Stone2010ProPhylER: a curated online resource for protein function and structure based on evolutionary constraint analyses.Genome Res20142154
- 62. Weigel D, Mott R (2009) The 1001 genomes project for Arabidopsis thaliana. Genome Biol 10: 107.D. WeigelR. Mott2009The 1001 genomes project for Arabidopsis thaliana.Genome Biol10107
- 63. Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, et al. (2005) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21: 1635–1638.J. KopkaN. SchauerS. KruegerC. BirkemeyerB. Usadel2005GMD@CSB.DB: the Golm Metabolome Database.Bioinformatics2116351638
- 64. Fange D, Elf J (2006) Noise-induced Min phenotypes in E. coli. PLoS Comput Biol 2: e80.D. FangeJ. Elf2006Noise-induced Min phenotypes in E. coli.PLoS Comput Biol2e80
- 65. Fisher J, Henzinger TA (2007) Executable cell biology. Nat Biotechnol 25: 1239–1249.J. FisherTA Henzinger2007Executable cell biology.Nat Biotechnol2512391249
- 66. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.RC GentlemanVJ CareyDM BatesB. BolstadM. Dettling2004Bioconductor: open software development for computational biology and bioinformatics.Genome Biol5R80
- 67. Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, et al. (1999) E-CELL: software environment for whole-cell simulation. Bioinformatics 15: 72–84.M. TomitaK. HashimotoK. TakahashiTS ShimizuY. Matsuzaki1999E-CELL: software environment for whole-cell simulation.Bioinformatics157284
- 68. Takahashi K, Ishikawa N, Sadamoto Y, Sasamoto H, Ohta S, et al. (2003) E-Cell 2: multi-platform E-Cell simulation system. Bioinformatics 19: 1727–1729.K. TakahashiN. IshikawaY. SadamotoH. SasamotoS. Ohta2003E-Cell 2: multi-platform E-Cell simulation system.Bioinformatics1917271729
- 69. Behr J, Eschler P, Jung Y, Zöllner M (2009) X3DOM: a DOM-based HTML5/X3D integration model. In: Spencer SN, editor. Proceedings of the 14th International Conference on 3D Web Technology. pp. 127–135.J. BehrP. EschlerY. JungM. Zöllner2009X3DOM: a DOM-based HTML5/X3D integration model.SN SpencerProceedings of the 14th International Conference on 3D Web Technology127135
- 70. Fucile G, Falconer S, Christendat D (2008) Evolutionary diversification of plant shikimate kinase gene duplicates. PLoS Gen 4: e1000292.G. FucileS. FalconerD. Christendat2008Evolutionary diversification of plant shikimate kinase gene duplicates.PLoS Gen4e1000292
- 71. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410.SF AltschulW. GishW. MillerEW MyersDJ Lipman1990Basic local alignment search tool.J Mol Biol215403410
- 72. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, et al. (2009) InterPro: the integrative protein signature database. Nucleic Acids Res 37: D211–215.S. HunterR. ApweilerTK AttwoodA. BairochA. Bateman2009InterPro: the integrative protein signature database.Nucleic Acids Res37D211215