401
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Tsui IF, Chari R, Buys TP, Lam WL. Public databases and software for the pathway analysis of cancer genomes. Cancer Inform 2007; 3:379-97. [PMID: 19455256 PMCID: PMC2410087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways.
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Affiliation(s)
- Ivy F.L. Tsui
- Correspondence: Ivy Tsui, BC Cancer Research Centre, 675 West 10th Avenue Vancouver, BC, V5Z 1L3, Canada. Tel: +1 604-675-8111; Fax: +1 604-675-8232;
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402
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Adler P, Reimand J, Janes J, Kolde R, Peterson H, Vilo J. KEGGanim: pathway animations for high-throughput data. Bioinformatics 2007; 24:588-90. [DOI: 10.1093/bioinformatics/btm581] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
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403
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Lee D, Redfern O, Orengo C. Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol 2007; 8:995-1005. [PMID: 18037900 DOI: 10.1038/nrm2281] [Citation(s) in RCA: 354] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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404
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Scaria V, Jadhav V. microRNAs in viral oncogenesis. Retrovirology 2007; 4:82. [PMID: 18036240 PMCID: PMC2217556 DOI: 10.1186/1742-4690-4-82] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2007] [Accepted: 11/24/2007] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs are a recently discovered class of small noncoding functional RNAs. These molecules mediate post-transcriptional regulation of gene expression in a sequence specific manner. MicroRNAs are now known to be key players in a variety of biological processes and have been shown to be deregulated in a number of cancers. The discovery of viral encoded microRNAs, especially from a family of oncogenic viruses, has attracted immense attention towards the possibility of microRNAs as critical modulators of viral oncogenesis. The host-virus crosstalk mediated by microRNAs, messenger RNAs and proteins, is complex and involves the different cellular regulatory layers. In this commentary, we describe models of microRNA mediated viral oncogenesis.
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405
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Cogburn LA, Porter TE, Duclos MJ, Simon J, Burgess SC, Zhu JJ, Cheng HH, Dodgson JB, Burnside J. Functional genomics of the chicken--a model organism. Poult Sci 2007; 86:2059-94. [PMID: 17878436 DOI: 10.1093/ps/86.10.2059] [Citation(s) in RCA: 86] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection ( approximately 600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of approximately 20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken. Recently, about 20 papers have been published on transcriptional profiling with DNA microarrays in chicken tissues under various conditions. Proteomics is another powerful high-throughput tool currently used for examining the dynamics of protein expression in chicken tissues and fluids. Computational analyses of the chicken genome are providing new insight into the evolution of gene families in birds and other organisms. Abundant functional genomic resources now support large-scale analyses in the chicken and will facilitate identification of transcriptional mechanisms, gene networks, and metabolic or regulatory pathways that will ultimately determine the phenotype of the bird. New technologies such as marker-assisted selection, transgenics, and RNA interference offer the opportunity to modify the phenotype of the chicken to fit defined production goals. This review focuses on functional genomics in the chicken and provides a road map for large-scale exploration of the chicken genome.
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Affiliation(s)
- L A Cogburn
- Department of Animal and Food Sciences, University of Delaware, Newark 19717, USA.
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406
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Bruford EA, Lush MJ, Wright MW, Sneddon TP, Povey S, Birney E. The HGNC Database in 2008: a resource for the human genome. Nucleic Acids Res 2007; 36:D445-8. [PMID: 17984084 PMCID: PMC2238870 DOI: 10.1093/nar/gkm881] [Citation(s) in RCA: 166] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique and ideally meaningful name and symbol to every human gene. The HGNC database currently comprises over 24 000 public records containing approved human gene nomenclature and associated gene information. Following our recent relocation to the European Bioinformatics Institute our homepage can now be found at http://www.genenames.org, with direct links to the searchable HGNC database and other related database resources, such as the HCOP orthology search tool and manually curated gene family webpages.
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Affiliation(s)
- Elspeth A Bruford
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
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407
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Ruebenacker O, Moraru II, Schaff JC, Blinov ML. Kinetic Modeling using BioPAX ontology. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2007; 2007:339-348. [PMID: 20862270 PMCID: PMC2941992 DOI: 10.1109/bibm.2007.55] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Thousands of biochemical interactions are available for download from curated databases such as Reactome, Pathway Interaction Database and other sources in the Biological Pathways Exchange (BioPAX) format. However, the BioPAX ontology does not encode the necessary information for kinetic modeling and simulation. The current standard for kinetic modeling is the System Biology Markup Language (SBML), but only a small number of models are available in SBML format in public repositories. Additionally, reusing and merging SBML models presents a significant challenge, because often each element has a value only in the context of the given model, and information encoding biological meaning is absent. We describe a software system that enables a variety of operations facilitating the use of BioPAX data to create kinetic models that can be visualized, edited, and simulated using the Virtual Cell (VCell), including improved conversion to SBML (for use with other simulation tools that support this format).
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Affiliation(s)
- Oliver Ruebenacker
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, 06030
| | - Ion. I. Moraru
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, 06030
| | - James C. Schaff
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, 06030
| | - Michael L. Blinov
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT, 06030
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408
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Diella F, Gould CM, Chica C, Via A, Gibson TJ. Phospho.ELM: a database of phosphorylation sites--update 2008. Nucleic Acids Res 2007; 36:D240-4. [PMID: 17962309 PMCID: PMC2238828 DOI: 10.1093/nar/gkm772] [Citation(s) in RCA: 183] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Phospho.ELM is a manually curated database of eukaryotic phosphorylation sites. The resource includes data collected from published literature as well as high-throughput data sets. The current release of Phospho.ELM (version 7.0, July 2007) contains 4078 phospho-protein sequences covering 12 025 phospho-serine, 2362 phospho-threonine and 2083 phospho-tyrosine sites. The entries provide information about the phosphorylated proteins and the exact position of known phosphorylated instances, the kinases responsible for the modification (where known) and links to bibliographic references. The database entries have hyperlinks to easily access further information from UniProt, PubMed, SMART, ELM, MSD as well as links to the protein interaction databases MINT and STRING. A new BLAST search tool, complementary to retrieval by keyword and UniProt accession number, allows users to submit a protein query (by sequence or UniProt accession) to search against the curated data set of phosphorylated peptides. Phospho.ELM is available on line at: http://phospho.elm.eu.org
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Affiliation(s)
- Francesca Diella
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany and Center for Molecular Bioinformatics, Dept. of Biology, Tor Vergata University, Rome, Italy
| | - Cathryn M. Gould
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany and Center for Molecular Bioinformatics, Dept. of Biology, Tor Vergata University, Rome, Italy
| | - Claudia Chica
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany and Center for Molecular Bioinformatics, Dept. of Biology, Tor Vergata University, Rome, Italy
| | - Allegra Via
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany and Center for Molecular Bioinformatics, Dept. of Biology, Tor Vergata University, Rome, Italy
| | - Toby J. Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany and Center for Molecular Bioinformatics, Dept. of Biology, Tor Vergata University, Rome, Italy
- * To whom correspondence should be addressed.+49 6221 3878398+49 6221 3878517
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409
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Schmidt CJ, Romanov M, Ryder O, Magrini V, Hickenbotham M, Glasscock J, McGrath S, Mardis E, Stein LD. Gallus GBrowse: a unified genomic database for the chicken. Nucleic Acids Res 2007; 36:D719-23. [PMID: 17933775 PMCID: PMC2238981 DOI: 10.1093/nar/gkm783] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Gallus GBrowse (http://birdbase.net/cgi-bin/gbrowse/gallus/) provides online access to genomic and other information about the chicken, Gallus gallus. The information provided by this resource includes predicted genes and Gene Ontology (GO) terms, links to Gallus In Situ Hybridization Analysis (GEISHA), Unigene and Reactome, the genomic positions of chicken genetic markers, SNPs and microarray probes, and mappings from turkey, condor and zebra finch DNA and EST sequences to the chicken genome. We also provide a BLAT server (http://birdbase.net/cgi-bin/webBlat) for matching user-provided sequences to the chicken genome. These tools make the Gallus GBrowse server a valuable resource for researchers seeking genomic information regarding the chicken and other avian species.
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Affiliation(s)
- Carl J Schmidt
- Department of Animal and Food Sciences, University of Delaware, Newark, DE 19706, USA.
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410
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Grafahrend-Belau E, Weise S, Koschützki D, Scholz U, Junker BH, Schreiber F. MetaCrop: a detailed database of crop plant metabolism. Nucleic Acids Res 2007; 36:D954-8. [PMID: 17933764 PMCID: PMC2238923 DOI: 10.1093/nar/gkm835] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
MetaCrop is a manually curated repository of high quality information concerning the metabolism of crop plants. This includes pathway diagrams, reactions, locations, transport processes, reaction kinetics, taxonomy and literature. MetaCrop provides detailed information on six major crop plants with high agronomical importance and initial information about several other plants. The web interface supports an easy exploration of the information from overview pathways to single reactions and therefore helps users to understand the metabolism of crop plants. It also allows model creation and automatic data export for detailed models of metabolic pathways therefore supporting systems biology approaches. The MetaCrop database is accessible at http://metacrop.ipk-gatersleben.de.
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Affiliation(s)
- Eva Grafahrend-Belau
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Gatersleben, Germany
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411
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KAHLEM P, BIRNEY E. ENFIN a Network to Enhance Integrative Systems Biology. Ann N Y Acad Sci 2007; 1115:23-31. [DOI: 10.1196/annals.1407.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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412
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Vinogradov AE, Anatskaya OV. Organismal complexity, cell differentiation and gene expression: human over mouse. Nucleic Acids Res 2007; 35:6350-6. [PMID: 17881362 PMCID: PMC2095826 DOI: 10.1093/nar/gkm723] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2007] [Revised: 08/12/2007] [Accepted: 09/01/2007] [Indexed: 01/25/2023] Open
Abstract
We present a molecular and cellular phenomenon underlying the intriguing increase in phenotypic organizational complexity. For the same set of human-mouse orthologous genes (11 534 gene pairs) and homologous tissues (32 tissue pairs), human shows a greater fraction of tissue-specific genes and a greater ratio of the total expression of tissue-specific genes to housekeeping genes in each studied tissue, which suggests a generally higher level of evolutionary cell differentiation (specialization). This phenomenon is spectacularly more pronounced in those human tissues that are more directly involved in the increase of complexity, longevity and body size (i.e. it is reflected on the organismal level as well). Genes with a change in expression breadth show a greater human-mouse divergence of promoter regions and encoded proteins (i.e. the functional genomics data are supported by the structural analysis). Human also shows the higher expression of translation machinery. The upstream untranslated regions (5'UTRs) of human mRNAs are longer than mouse 5'UTRs (even after correction for the difference in genome sizes) and contain more uAUG codons, which suggest a more complex regulation at the translational level in human cells (and agrees well with the augmented cell specialization).
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Affiliation(s)
- Alexander E Vinogradov
- Institute of Cytology, Russian Academy of Sciences, Tikhoretsky Avenue 4, St. Petersburg 194064, Russia.
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413
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Computational prediction of protein-protein interactions. Mol Biotechnol 2007; 38:1-17. [PMID: 18095187 DOI: 10.1007/s12033-007-0069-2] [Citation(s) in RCA: 126] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2007] [Accepted: 07/16/2007] [Indexed: 01/19/2023]
Abstract
Recently a number of computational approaches have been developed for the prediction of protein-protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.
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414
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Alfieri R, Merelli I, Mosca E, Milanesi L. A data integration approach for cell cycle analysis oriented to model simulation in systems biology. BMC SYSTEMS BIOLOGY 2007; 1:35. [PMID: 17678529 PMCID: PMC1995223 DOI: 10.1186/1752-0509-1-35] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2007] [Accepted: 08/01/2007] [Indexed: 12/13/2022]
Abstract
Background The cell cycle is one of the biological processes most frequently investigated in systems biology studies and it involves the knowledge of a large number of genes and networks of protein interactions. A deep knowledge of the molecular aspect of this biological process can contribute to making cancer research more accurate and innovative. In this context the mathematical modelling of the cell cycle has a relevant role to quantify the behaviour of each component of the systems. The mathematical modelling of a biological process such as the cell cycle allows a systemic description that helps to highlight some features such as emergent properties which could be hidden when the analysis is performed only from a reductionism point of view. Moreover, in modelling complex systems, a complete annotation of all the components is equally important to understand the interaction mechanism inside the network: for this reason data integration of the model components has high relevance in systems biology studies. Description In this work, we present a resource, the Cell Cycle Database, intended to support systems biology analysis on the Cell Cycle process, based on two organisms, yeast and mammalian. The database integrates information about genes and proteins involved in the cell cycle process, stores complete models of the interaction networks and allows the mathematical simulation over time of the quantitative behaviour of each component. To accomplish this task, we developed, a web interface for browsing information related to cell cycle genes, proteins and mathematical models. In this framework, we have implemented a pipeline which allows users to deal with the mathematical part of the models, in order to solve, using different variables, the ordinary differential equation systems that describe the biological process. Conclusion This integrated system is freely available in order to support systems biology research on the cell cycle and it aims to become a useful resource for collecting all the information related to actual and future models of this network. The flexibility of the database allows the addition of mathematical data which are used for simulating the behavior of the cell cycle components in the different models. The resource deals with two relevant problems in systems biology: data integration and mathematical simulation of a crucial biological process related to cancer, such as the cell cycle. In this way the resource is useful both to retrieve information about cell cycle model components and to analyze their dynamical properties. The Cell Cycle Database can be used to find system-level properties, such as stable steady states and oscillations, by coupling structure and dynamical information about models.
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Affiliation(s)
- Roberta Alfieri
- Istituto di Tecnologie Biomediche - Consiglio Nazionale delle Ricerche, via F,lli Cervi 93, Segrate (Milano), Italy.
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415
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GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 2007; 8:217. [PMID: 17588266 PMCID: PMC1924866 DOI: 10.1186/1471-2105-8-217] [Citation(s) in RCA: 205] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2006] [Accepted: 06/24/2007] [Indexed: 12/03/2022] Open
Abstract
Background Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses. Results To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages. Conclusion GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms.
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416
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Salwinski L, Eisenberg D. The MiSink Plugin: Cytoscape as a graphical interface to the Database of Interacting Proteins. Bioinformatics 2007; 23:2193-5. [PMID: 17553858 DOI: 10.1093/bioinformatics/btm304] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
UNLABELLED The MiSink Plugin converts Cytoscape, an open-source bioinformatics platform for network visualization, to a graphical interface for the database of interacting proteins (DIP: http://dip.doe-mbi.ucla.edu). Seamless integration is possible by providing bi-directional communication between Cytoscape and any Web site supplying data in XML or tab-delimited format. AVAILABILITY MiSink is freely available for download at http://dip.doe-mbi.ucla.edu/Software.cgi.
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Affiliation(s)
- Lukasz Salwinski
- UCLA-DOE Institute for Genomics & Proteomics, Department of Chemistry, Howard Hughes Medical Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA.
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417
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Abstract
There exists a sense of urgency to begin to generate a cohesive assembly of biomedical knowledge as the pace of knowledge accumulation accelerates. The urgency is in part driven by the emergence of systems molecular medicine that emphasizes the combination of systems analysis and molecular dissection in the future of medical practice and research. A potentially powerful approach is to build integrative pathway knowledge bases that link organ systems function with molecules.
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Affiliation(s)
- Mingyu Liang
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA.
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