esyN: Network Building, Sharing and Publishing

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN (“easy networks”) as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.

[1]  Julie M. Sullivan,et al.  FlyMine: an integrated database for Drosophila and Anopheles genomics , 2007, Genome Biology.

[2]  L. Tran,et al.  Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease , 2013, Cell.

[3]  D. Fardo,et al.  ABCA7 expression is associated with Alzheimer's disease polymorphism and disease status , 2013, Neuroscience Letters.

[4]  Richard I. Morimoto,et al.  Adapting Proteostasis for Disease Intervention , 2008, Science.

[5]  A. Fagan,et al.  Fine Mapping of Genetic Variants in BIN1, CLU, CR1 and PICALM for Association with Cerebrospinal Fluid Biomarkers for Alzheimer's Disease , 2011, PloS one.

[6]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[7]  Gos Micklem,et al.  YeastMine—an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit , 2012, Database J. Biol. Databases Curation.

[8]  Johanna Huttenlocher-Moser.  Variant of TREM2 Associated with the Risk of Alzheimer's Disease , 2013 .

[9]  D. Cleveland,et al.  Converging Mechanisms in ALS and FTD: Disrupted RNA and Protein Homeostasis , 2013, Neuron.

[10]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[11]  Asim Khwaja,et al.  Apoptosis: Akt is more than just a Bad kinase , 1999, Nature.

[12]  Mohammad Asim,et al.  Differential C3NET reveals disease networks of direct physical interactions , 2011, BMC Bioinformatics.

[13]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[14]  Yongshuai Jiang,et al.  Pathway Analysis of Two Amyotrophic Lateral Sclerosis GWAS Highlights Shared Genetic Signals with Alzheimer’s Disease and Parkinson’s Disease , 2014, Molecular Neurobiology.

[15]  A. Miyashita,et al.  A Map of Alzheimer's Disease–Signaling Pathways: A Hope for Drug Target Discovery , 2013, Clinical pharmacology and therapeutics.

[16]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[17]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[18]  Monika Heiner,et al.  Snoopy - a unifying Petri net framework to investigate biomolecular networks , 2010, Bioinform..

[19]  Ian M. Donaldson,et al.  iRefIndex: A consolidated protein interaction database with provenance , 2008, BMC Bioinformatics.

[20]  Huaxi Xu,et al.  APP processing in Alzheimer's disease , 2011, Molecular Brain.

[21]  Erick R. Scott,et al.  Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease , 2013, Nature.

[22]  Jennifer M. Pocock,et al.  Insights into TREM2 biology by network analysis of human brain gene expression data , 2013, Neurobiology of Aging.

[23]  Sergio Contrino,et al.  modMine: flexible access to modENCODE data , 2011, Nucleic Acids Res..

[24]  Melanie I. Stefan,et al.  BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models , 2010, BMC Systems Biology.

[25]  Sourav Bandyopadhyay,et al.  Rewiring of Genetic Networks in Response to DNA Damage , 2010, Science.

[26]  Rafael C. Jimenez,et al.  The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases , 2013, Nucleic Acids Res..

[27]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[28]  P J Goss,et al.  Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Nick C Fox,et al.  Letter abstract - Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's Disease , 2009 .

[30]  Sergio Contrino,et al.  InterMine: a flexible data warehouse system for the integration and analysis of heterogeneous biological data , 2012, Bioinform..

[31]  Elizabeth A Miller,et al.  Collapse of proteostasis represents an early molecular event in Caenorhabditis elegans aging , 2009, Proceedings of the National Academy of Sciences.

[32]  Robert Kleta,et al.  Crisponi syndrome is caused by mutations in the CRLF1 gene and is allelic to cold-induced sweating syndrome type 1. , 2007, American journal of human genetics.

[33]  H. Rhinn,et al.  Integrative genomics identifies APOE ε4 effectors in Alzheimer's disease , 2013, Nature.

[34]  Jesús Avila,et al.  GSK3 and tau: two convergence points in Alzheimer's disease. , 2012, Journal of Alzheimer's disease : JAD.

[35]  Gos Micklem,et al.  metabolicMine: an integrated genomics, genetics and proteomics data warehouse for common metabolic disease research , 2013, Database J. Biol. Databases Curation.

[36]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[37]  Thomas W. Mühleisen,et al.  Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease , 2013, Nature Genetics.