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1. ns Ons ae ars che ano the object gene protein in one module of the system automatically changes the states of the respective objects genes proteins in all opened modules This makes it possible for example to map color coded expression data or ontology annotations onto any collec tion of genes proteins as well as pathways and interac tion relation networks or chromosomal sequence view that are currently opened in the project The modules are described in detail in the subsequent sections Microarray data search and analysis Most current web based tools are focused on retrieving expression and or co expression patterns for individual genes from particular microarray experiment Multi experiment multi gene co expression analysis is a labor intensive and computationally challenging task involving collecting suitable datasets data downloads preprocessing normalization and gene annotation management the inte gration of different datasets merging cross platform data Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 and handling ambiguous mappings between genes and probe sets Microarray databases Gene Expression Omni bus GEO 20 and ArrayExpress 21 provide tools for finding and analyzing most relevant datasets but neither currently provides a comprehensive gene co expression search over many datasets simultaneously We have refin2. a 590 38 218812 _ 4 172 04 60 Kit RELB T 2 590 2 218812 8 075604 K60 hCG_43716 i 2 590 12 218812 1 043 03 60 hcG_43716 GO Biological Process itotic spindle assembly 3 p1 237 139192 1 05603 K60 Kit hCG 4 GO Biological Process jation of NF kappaB inducing kinase 1 p1 1 139192 105 03 Moiz Cellular Component dnetochore 1 191 2 139192 1 182 03 keo ju of pro apoptotic gene products 1 jor 3 139192 1 696E 03 Miz 1 91 3 139192 2 019603 Ku 1 1 3 210447 2 3403 kit 2 590 25 218812 3 268603 Kiti TANK 2 590 27 218812 3 728E 03 K60 hCG_43716 1 590 1 218812 6 286E 03 hhec_43716 2 590 29 218812 7 99403 Kt hCG _43716 2 146 116 210447 8 067 03 TANK hCG_4371 o bs Tnaanan In ream nn wanna ne annar ES se N as Set a Cea S s eos Sone ses ieee ores can Sees Fe oe A ae i oo x eer sors a gee taser ae al oS ee DO Se ao Ae ort a per representation in BiologicalNetworks Figure 2 Biological Themes Biological Themes GO terms diseases cell types tissues curated pathways phenotypes and human anatomy BioNets ontology integrates Sequence Ontology Gen eOntology BioPAX Disease Ontology Chemical Ontol ogy the Functional Genomics Ontology FUGO and the Phenotype and Trait Ontology PATO and other ontologies provided by OBO consortium at http www bioontology org It is implemented so that any individual ontology describing another type o
3. Co ear Figure 1 Integrative view of data in BiologicalNetworks Microarrays a msriert reservor chemorepelent Provetes Porere HEEREN translation regdator Eca 3 E chemoattractant cos B 219 188 323 LJE Pipan m pag transcription regulator agoottt ot togasttt cancctt sat togt gosat ogoast gaaggagace socagasose ac acagct aed pact oacct qgoaat sgat cat ooaat at ct agaac exon E 0 178 0 be ie aa aodo 4070 valoo abso sodo sabia soo sodo sods pe craven poiniei W 0720 178 methionine adenosyltransf mac _ana 720 0 UTPighease 1 phasphate gt ncRNA W 10 178 178 E caspase regulator gaat at tt saccat aacot ctt aasgaasaatt ot at gattt ssaagat tataaaactttattact gggct att tpcacat ttt agttott tct cat asaat gt at aac at rRNA E nss C caspase activator rep_origin E 101 32 196 caspase mhibiter i zoso oso mosso 7080 7070 70 7 70 7010 i KEETA Pharma AA tRNA m 1432106 4 36 hey puking stereospectic synt 37 si el 33 ji Ttltascatsucttattt sasascat te atatt tact avsacttttecasttttacaatt EC Seod cal asad tt actastaotaeattcase 2 39 _ cytokine and chemokine mediated signe aso 199760 199770 199780 198700 199000 199810 199820 199830 199840 19 40 negative regulation of osteodast differ 41 _ T helper 1 type immune response 42_ TGFbeta receptor signaling pathway 43 MAP kinase kinase activity L 144 _ lpositive reaulation of interferon cammi Sequences Sequance Datasource Window
4. BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 4 of 14 uss iseh Curated Pathways Projects Pathways DataSo G8 Ces asanca RelA ESR1 Tools ective Window Help List of genes NFKB1 EGFR CDC20 Interaction networks a Ca 6 F rearea Homo capers 33056 2965076 neers Mas ruc 9865 1911268 tera File Edit View T Drosophta melancoactar Aad f Droserble ponidoobecurepesudobecra fehistudy ett wane re Dresophia ereta Drosophila persindls H Projects Sachaomyces cereveise 107438 62 L transmembrane receptor activity cascade regulation of interleukin 6 bios regulation of transaiption molecular _funcvon v C molecular _function a molecular transducer r herr gere at Spe 2 ereampanaes the comman ag ate FRA and equa nacietnin nd cel ines of Lrg head ard ech stad pars aagative calines resus nel i apoptotic oxtiay and regression of the umarceric l ars support the hypothesis of a role For FHIT in human ewerogenass but itte ts naan mechanism oF acten idertfeatn of transewoseral targets oF FEIT a thref are atico understand the pallays by nc FHT prometes roth arest and apoptos
5. methods still have a high false positive rate because although a TFBS might be correctly predicted binding of the respective factor might only occur in certain cells or tissues The BiologicalNetworks Comparative Geno mics Browser addresses both these problems of TFBS prediction via 1 integrating TFBSs predicted by phylo genetic footprinting methods 25 26 2 integrating all available experimental and computational data sources on transcriptional regulation transcription factor bind ing sites together with scores p Values 31 user can Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 8 of 14 F BiologicalNetworks2 200906241340 File Edit View Navigate Source Refactor Run Analyse Tools Perspective Window Help TL S TERREA Ac Jeee KBF1 TANK NFKB1 P i Welcome Page a Wekome Page a alt Celera _ch14 1 e g 81K FE Z e 1 r g i eE ii IG i o 277600 mo 277800 277900 278000 zio Human Microarray DataSource Window Pathways DataSource Window piff Projects 37800 40197900 40198000 40198100 14746000 14746100 14746200 22161900 22162000 22162100 22162200 Figure 4 Screen shot of Comparative Genomix Browser Comparative Genomix Browser shown together with the pa ion factor DNA binding network for CREB NRSF GATA 1 ATF 1 green squares and their targets red circles Genomix Brows
6. 361 393 p53 modified Correspondence baitaluk sdsc edu San Diego Supercomputer Center University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA Full list of author information is available at the end of the article C BioMed Central Thr 212 or pCMX mutant p53 Among the name ID conflicts that can be resolved is for example the conflict between different genes proteins having the same syno nym or the conflict between two databases naming the same gene differently these and similar name ID conflicts can be automatically resolved if there are other databases that recognize the conflicting names To ana lyze and visually integrate publicly available data on the systems level several web based tools have been devel oped Genomatix 1 2 GeneGO 3 STRING 4 Cytos cape 5 VisANT 6 Ingenuity 7 Pathway Studio 8 PipelinePilot 9 and BiologicalNetworks 10 Workflow systems like Taverna 11 GenePattern 12 and Galaxy 13 have been designed for the automatic application of the computational methods and data provenance man agement rather than visual integration representation querying and analysis of the data which are addressed in BiologicalNetworks Each of the mentioned tools has a distinct set of features which are highlighted in Table 1 2010 Kozhenkov et al licensee BioMed Central Ltd This is an Open Access article distributed under the terms of the Creative Commons
7. A BuildPathwayWizard BPW assists the user in finding regulatory paths and functional links between selected objects searches for common targets or regulators for the group of molecules finds connection to curated pathways e g KEGG BPW can find functional links between proteins in the lists imported from other programs e g gene expression clusters B Specialized Search Boxes are available on the Microarrays Curated Pathways and Networks panels C Search by attributes provides an advanced querying facility for retrieving the data of user s interest by querying Objects and Properties types User friendly querying interface allows user to make query with any logical combination of conditions both on Node and Property types D Organism Filter on 21 mostly used import a list of entities and search for relations among them and other entities in the database The seven querying options listed below allow for the specification and search for any logical combination of entities processes relations and their properties The first four options are available from Quick Search Box at the top right corner of the program Figure 5 and allow searching the entire database The next three query options define more specific searches they are accessible by clicking the buttons to the left and right of the search boxes in the correspondent windows Figure 5A C The search results appear in the bottom list panel Figure 5 bottom 1 S
8. Partridge NC Comparative promoter analysis and its application in analysis of PTH regulated gene expression J Mol Biol 2003 326 1327 1336 Halfon MS Grad Y Church GM Michelson AM Computation based iscovery of related transcriptional regulatory modules and motifs using n experimentally validated combinatorial model Genome Res 2002 2 1019 1028 uang DW Sherman BT Lempicki RA Systematic and integrative analysis f large gene lists using DAVID Bioinformatics Resources Nature Protocol 009 4 1 44 57 ead B Karolchik D Kuhn RM Hinrichs AS Zweig AS Fujita P Diekhans M mith KE Rosenbloom KR Raney BJ Pohl A Pheasant M Meyer L Hsu F illman Jackson J Harte RA Giardine B Dreszer T Clawson H Barber GP Haussler D Kent WJ The UCSC Genome Browser database update Nucleic Acids Res 2010 38 D613 9 Cochrane GR Galperin MY The 2010 Nucleic Acids Research Database Issue and online Database Collection a community of data resources Nucleic Acids Res 2010 38 D1 4 The Open Biomedical Ontologies http www obofoundry org index cgi show mappings Lee T et al Transcriptional regulatory networks in Saccharomyces cerevisiae Science 2002 298 799804 Tatusov RL Fedorova ND Jackson JD Jacobs AR Kiryutin B Koonin EV Krylov DM Mazumder R Mekhedov SL Nikolskaya AN et al The COG database an updated version includes eukaryotes BMC Bioinformatics 2003 4 41 3 Kozhenkov S Sedova M Dubinina Y Ponomarenko J Gupta A
9. Ray A Baitaluk M BiologicalNetworks tools enabling the integration of multi scale data for the host pathogen studies BMC Systems Biology 2010 Seshadri R Kravitz SA Smarr L Gilna P Frazier M CAMERA A Community Resource for Metagenomics PLoS Biol 2007 5 3 e75 Yeast meiosis http www biologicalnetworks org analysis tut0 php Becker SA Feist AM Mo ML Hannum G Palsson BO Herrgard MJ Quantitative prediction of cellular metabolism with constraint based models The COBRA Toolbox Nat Protocols 2007 2 3 727 738 Birkeland SR Preheim SP Davids BJ Cipriano MJ Palm D Reiner DS Svard SG Gillin FD McArthur AG Transcriptome analyses of the Giardia lamblia life cycle Mol Biochem Parasitol 2010 Microbial metabolism http www biologicalnetworks org analysis tut6 php Fries RS Mahboubi P Mahapatra NR Mahata SK Schork NJ Schmid Schoenbein GW O Connor DT Neuroendocrine transcriptome in genetic hypertension multiple changes in diverse adrenal physiological systems Hypertension 2004 43 1301 1311 Chang LW Fontaine BR Stormo GD Nagarajan R PAP a comprehensive workbench for mammalian transcriptional regulatory sequence analysis Nucleic Acids Res 2007 35 W238 W244 CT 9 Q NO cw a doi 10 1186 1471 2105 11 610 Cite this article as Kozhenkov et al BiologicalNetworks 2 0 an integrative view of genome biology data BMC Bioinformatics 2010 11 610 Submit your next man
10. demonstrated examples and data can be accessed in BMC Bioinformatics Demo Project upon launching the BiologicalNetworks application Addition ally the BiologicalNetworks Welcome Screen and front page of the web site contains a list of driving biological projects for various species and types of analysis which can be replicated by simply running the respective project BiologicalNetworks along with the user Manual and Video tutorials and Quick Start Guide is available at http www biologicalnetworks org Availability and requirements Project name BiologicalNetworks Project home page http www biologicalnetworks org Operating systems Windows 2000 XP Vista 7 Linux Ubuntu Redhat MacOSX Programming language Java License Free for academic purposes Other requirements 2GB RAM Additional material Additional file 1 Methods Detailed description of the methods and data types used in the BiologicalNetworks system Acknowledgements and Funding We would like to thank our colleagues from UCSD Medical School Daniel O Connor and Ryan Fries and Animesh Ray from KGI for valuable discussions of Hypertension case study and Ching Wai Tan for critical reading of the manuscript This work is supported by National Institutes of Health RO1GM084881 to MB and AG and RO1GM085325 to JP Author details San Diego Supercomputer Center University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA
11. format Any type of data can be integrated given that they are pro vided in the tab delimited format User support and Problem Handling To address the critical need for user support we devel oped Bug and Problem Report Tools This tool allows the user to report problems or bugs while working in BiologicalNetworks During the installation of Biologi calNetworks and the initial run of the program the user is asked for the agreement for permission to send from his computer any future bug reports If the user agrees Bug Reports will be automatically generated and sent to our support server it will include the environment set tings and the last user s steps before the program gave an error To report the problem the user needs to use Problem Report Tool that is located in the Tools menu of the program The Sun NetBeans Report and Bugzilla mechanisms are employed in the Bug Problem Report Tools Our experience shows that most of the user especially beginners problems are minor problems that can be quickly fixed by the developers if the Bug and Problem Report Tools are used Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Results The BiologicalNetworks analytical and querying func tionality were applied to and tested in a number of differ ent biological systems projects both eukaryotic and prokaryotic host pathogen interactions specifically the influenza and Streptococcus
12. last option is espe cially important for discovering gene regulatory modules e g a set of transcription factors regulating a set of co expressed genes since several studies exploit the fact that co expressed and or functionally related genes may be transcriptionally coordinated 22 24 see the Demonstration study below The results are presented in highly interactive graphical format with strong emphasis on further data mining In the resulting tree the user can pick the datasets manually and visualize it in the main window with the description of the experi ment annotations and metadata Datasets in the query results can be ordered by highest co expression to input genes In addition to search functionalities the platform provides the user with clustering and functional enrich ment analysis tools Integrated visualization of Biological Themes and gene list enrichment analysis The implementation of the Biological Themes within the BiologicalNetworks framework provides a set of tools for giving the researcher a biological interpretation Page 5 of 14 of gene clusters based on the indices provided in the input data set and information linking those indices to biological themes e g GO cell types diseases etc Our gene list functional enrichment analysis currently uses as many as 8 annotation categories including 3 categories of GO terms curated e g KEGG pathways diseases cell types tissue gene express
13. pneumoniae human mouse rat interactions 33 meta genomics CAMERA project 34 yeast meiosis 35 whole genome metabolic recon struction in Humans and E coli 36 parasite studies in Giardia lamblia 37 and microbial metabolism in Ther matoga maritime 38 All described projects are accessi ble from BiologicalNetworks main page under Driving Projects panel or upon launching the BiologicalNet works application on the Welcome Screen These studies can be replicated by running the respective projects In this section we demonstrate a case study of the search for potential therapeutic targets for hypertension Specifically it is shown how using BiologicalNetworks and starting with a single microarray experiment in the model organism Rattus norvegicus 39 one can identify regulatory regions in the hypertension essential genes and investigate transcriptional modules and gene regulatory networks describing multi factorial nature of hypertension Figure 6 Below the case study is described step by step First among about 1000 genes significantly perturbed in hypertension in the microarray experiment 39 Figure 6 1 we found 25 over expressed and 110 under expressed genes that showed the maximal fold change of expression in hypertension Table S1 in Addi tional File 1 Figure 6 2 For the demonstration pur pose of this study we focused only on over expressed genes Further using the multi experiment multi gene Mi
14. Attribution License http creativecommons org licenses by 2 0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 2 of 14 Table 1 Web accessible tools for microarray pathway and DNA sequence regulation analysis Features Tools GG PS ST IN PA CS GE VS GX TV PP BN Pathway Networks Curated Pathways Content tS Biological Themes Functional Enrichment z Build Pathways Networks inference s Microarray Multi Experiment Support 7 Search of Public Expression Compendiums Microarray Pathway Sequence Integration 7 pe P H DNA Sequences General Gene Regulation z 2 ye af 4 Regulatory regions jj 4j if Sequence search 7 Sequence Annotation z z 7 4j Comparative Genomics Homology Search z Jp 2 Search for homologous TFBS z if 3D Structure Drug design Visualization 7 7 lt 3 z Ligand search z Back end Database General Popo o p a H H H 4 H Integration of user s data gt Scalability s 7 B gt O
15. BO ontologies integration ae 3s z z 4 General Project Workspace Data Sharing gt 5 H t 4 H H H API Plugins H H H Free for Academic Use z 4 5 IN Ingenuity GG GeneGO suite PS Ariadne Genomics Pathway Studio GE Genomatix suite ST STRING CS Cytoscape VS VisANT PA Partek PP PipelinePilot GX Galaxy TV Taverna BN BiologicalNetworks a feature is present not present present but not fully unknown cannot be determined TFBS transcription factor binding site facilitating functional analysis of networks pathways as well as comparative gene sequence analyses including cis element prediction expression profiling and co expression analysis In this work the application BiologicalNetworks 2 0 for integration of functional genomics data with biologi cal networks is presented In comparison with other tools Table 1 only features that are present in Biologi calNetworks are shown BiologicalNetworks integrates much more different types of data and provides broader analytical capabilities The developed system minimizes the need for prior knowledge of existing nomenclatures and data formats representing microarrays networks pathways sequences and other types of data Biological Networks allows integral visualization and analyses of over 100 000 features from multiple different data types that are integrated in IntegromeDB 14 15 and provide i
16. Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 BMC Bioinformatics BiologicalNetworks 2 0 an integrative view of genome biology data Sergey Kozhenkov Yulia Dubinina Mayya Sedova Amarnath Gupta Julia Ponomarenko Michael Baitaluk Abstract biologica Biological Background A significant problem in the study of mechanisms of an organism s development is the elucidation of interrelated factors which are making an impact on the different levels of the organism such as genes molecules cells and cell systems Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems Results Here we report on a new version of BiologicalNetworks a research environment for the integra visualization and analysis of heterogeneous biological data BiologicalNetworks can be queried for properties of thousands of different types of biological entities genes proteins promoters COGs pathways binding sites and other and their relations interactions co expression co citations and other The system includes the build pathways infrastructure for molec
17. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Authors contributions MB AG and MS contributed to system concept SK YD MS and MB implemented the system and performed major programming work MB and JP coordinated this work contributed to data analysis and wrote the manuscript All authors read and approved the final manuscript Received 12 October 2010 Accepted 29 December 2010 Published 29 December 2010 References 1 20 21 22 23 24 Werner T Regulatory networks Linking microarray data to systems biology Mech Ageing Dev 2007 128 168 72 Genomatix Suite http www genomatix de GeneGO Suite http www genego com Jensen LJ Kuhn M Stark M Chaffron S Creevey C Muller J Doerks T Julien P Roth A Simonovic M Bork P von Mering C STRING 8 a global view on proteins and their functional interactions in 630 organisms Nucleic Acids Res 2009 37 D412 6 Shannon P Markiel A Ozier O Baliga NS Wang JT Ramage D Amin N Schwikowski B Ideker T Cytoscape a software environment for integrated models of biomolecular interaction networks Genome Research 2003 13 11 2498 504 Hu Z Hung JH Wang Y Chang Y Huang C Huyck M DeLisi C VisANT 3 5 multi scale network visualization analysis and inference based
18. appear first However no conflict data are removed or become invisible to the user since the data sources are not weighted or judged Inconsistencies among the names IDs of all objects in the database that were found for human mouse and rat are provided at http www integromedb org If the user searches the http integromedb org page for an object for which inconsistencies were calculated and found they can be seen on the result search page by clicking the red button The IntegromeDB data can be searched at http www integromedb org however in comparison with the application BiologicalNetworks described in this work the web site provides only general quick search capabil ities and no extensive data analysis dynamic integration and visualization capabilities Integrative View of data in BiologicalNetworks The typical user of BiologicalNetworks starts with load ing the file or searching the integrated database for a list of genes e g tab delimited text file networks sif file curated pathways SBML microarrays proteomics data tab delimited file or sequence data gbk FASTA or gbs file BiologicalNetworks provides an Integrative View of the found data Figure 1 opening the modules of the system Network Sequence Microarray Ontol ogy and other modules in separate windows All opened modules are synchronized and interconnected by object gene protein IDs so that changing the state of Kozhenkov et al
19. cific species or specific data base dataset to be included in the pathway and d relations e g p value of the protein protein interactions for all or selected experiments to be included in the new pathway Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 6 Specialized search Figure 5B These Search Boxes are available on the Microarrays Curated Pathways and Networks and Sequence panels They are for quick retrieval the most popular public data sets 7 Search by attributes Figure 5C The search by attributes allows the user searching the database as well as user s data files for specific entities using many types of data as search conditions These include for example node type effect positive negative unknown mechanism transcription phos phorylation tissue type description user defined attributes text and so forth Figure 5C These types of queries can contain logical operators on the attri butes a detailed description is provided in the online Tutorial Search Boxes accept lists of gene names that can be loaded from files accession numbers from public data bases SwissProt UniGene GenBank etc or GEO experiments Series and Datasets The system recog nizes most of the currently available gene protein IDs and synonyms for thousands of organisms integrated from over 100 data sources To perform a search in the microarray pathways networks s
20. croarray Search Panel we searched the microarray experiments for co expressed pairs containing the selected over expressed genes Figure 6 3 Data in every experiment was normalized that is the expression vec tor was subtracted with the mean of the expression values in the experiment and divided by the standard deviation Since different experiments have different number of time points and conditions calculation of Pearson correlation coefficient PCC was FDR cor rected so that calculated p values for PCC took into account the length of the expression vectors experi ments For further analysis only pairs that were co expressed in more than 10 experiments were chosen Figure 3 Section 1 4 in Additional File S1 Second we built a network of 20 selected genes selected at the previous step that were co expressed and at the same time over expressed in hypertension together with TFs that might potentially regulate tran scription of these genes 40 To search for TFs we con sidered that gene regulation is controlled to a significant degree by TFBSs within proximal promoters and the fact that in orthologous promoters the relative order Page 11 of 14 and spacing among TFBSs expected to be conserved during evolution 22 24 For the 20 selected genes we first identified orthologous promoter regions in human mouse and rat Figure 6 4 We considered the regions from 6 kb to 500 bp relative to the transcription start sites a
21. d change GeneName Foldchange Extraction of hypertension 2 regulated genes with See 22 Nuts on significant genes A E ChaB 187 Naufas 067 maximal experession Che 133 Coxsa 035 fold change key genes 4 Co expression linked regulation of hypertension _ significant genes a s 4 Phylogenetically conserved TFBSs mtr human Gene2 mouse Search of molecular interaction integrated database KEGG TRANSFAC i Generate hypothesis by construction of Sets of co expressed gene pairs 3 Gene1 Gene2 Genel Gene 2 Genel Gene2 TF1 TF2 TFn human EE E ie n Genef mouse EEEL EET Promoter analysis of PET ETTE T E co expressed genes for mee ses atest 6 86 8 y N A A Mechanistic explanation Transcription regulation MS an of linked regulation from 5 of co expressed genes with 1 MEN co expression of genes phylogenetically conserved rs ra al significant in hypertension promoters L174 f _Gene2_ Pree Genel 4 STRING Molecular BioCyc interactions MIPS TRRD emo E L O PAZAR novel candidate genes 7 i molecular intearaction and linked to pathways gene regulatory networks 8 Experimental validation of novel candiate genes and regulation of hypertension significant genes Figure 6 Systems analysis in BiologicalNetworks The figure details the strategy that was used to systematically search for co expressed co regulated gen
22. ed and extended the process of multi experiment multi gene co expression analysis in the Microarray Ana lysis window BiologicalNetworks database integrates tens of thousands microarray experiments from GEO ArrayEx press and other public data sources see the Nucleic Acids Research list of databases on microarray data and other gene expression data 29 Data from every experiment are normalized so every expression vector is subtracted from the mean and divided by the standard deviation of the experimental expression values Since different experi ments have different numbers of time points and condi tions the Pearson correlation calculation is FDR corrected so that the p Values calculated for PCC take the length of the expression vectors into account The user can upload the microarray data files and analyze them in conjunction with the integrated com pendium of publicly available microarray data using the Microarray Analysis window When searching in the Microarray Analysis window the user can select the Default Co expression pairs or Co expression Triples search modes In the first case the search will return all available microarray experiments in which genes from the input gene list are over or under expressed In the second case it will return experiments and gene pairs where genes are co expressed and in the third case it will return gene triples and experiments where these gene triples are co expressed This
23. ed on the Semantic Web technologies such as the Resource Description Framework RDF http www w3 org RDF and the Web Ontology Language OWL http www w3 org TR owl ref IntegromeDB also enables research ers to integrate their own data into the database as described in the section Integration of User s Data The IntegromeDB schema is based on BioNets Ontol ogy the core of which is Basic Ontology that was manu ally developed by the authors Basic Ontology describes classes from different domains such as protein gene pathway interaction disease cell tissue drug chromo some COG functional group gene set e g operon reg ulon Basic Ontology is manually mapped onto 25 OBO ontologies including Sequence Ontology GeneOntology Human Disease CheBI BRENDA Tissues that were selected from the best curated and regularly updated ontologies provided by the OBO consortium http www bioontology org The mapping among the OBO ontolo gies which is provided by OBO allows for the automatic integration of 98 ontologies in BioNets Ontology The basic owl file with Basic Ontology and mappings from it to other ontologies can be downloaded at http www integromedb org bionetsonto php Due to the generic schema of the database and ontol ogy driven mapping during integration new objects and their properties are automatically added in the database For example if the database stored information about the interaction betwee
24. equence annotations and PubMed repositories the user can specify any com bination of keywords including authors names tissue types diseases gene protein names The Search Box contains different configuration options and filters and enables limiting searches on specific species opened network pathway and sequences The organism drop down menus in each search window include 21 model organisms which are mostly represented in the data base and the following options to narrow down the search and subsequent data analysis All Organisms Eukaryotes Prokaryotes and Viruses Eukaryotes are subdivided into Plants Fungi Protists Archea and Metazoa Animals which in turn are subdivided into Vertebrate Invertebrate and Mammals Loading files and Output results in BiologicalNetworks In BiologicalNetworks the user can load open files in the following standard formats e networks SIF Simple Interaction Format PSI MI Tab delimited network file SBML and BiGG model format e microarray data tab delimited file format Illumina tab delimited and Affymetrix file formats e sequences GenBank GBK GBS and FASTA formats The results of analysis and visualization in Biological Networks can be saved at any moment as the BioNets Page 10 of 14 XML Project file and then opened at other computers the user s settings data files results of search built net works clustering colorings and all other visualizations will appear exact
25. er right window for selected species TFBSs collected from public data and predicted using phylogenetic foo shown aligned as provided by phylogenetic footprinting The user can zoom in out scroll the sequences and network regions for respective TFs and genes to see the binding sites relative chromosomal positions of target genes and respective sequence events depicts transcrip Goes za hway window that In Comparative printing are filter interactions by p value in the Build Pathway Wizard when constructing gene regulatory networks of binding event for each transcription factor and a gene or a pair of genes and 3 filtering the final TF gene interaction networks by tissue and cell type specificity in order to correct the probability of binding and thus reduce the false positive rate of TFBSs prediction and narrow the list of potential target genes for further investigation Described methods can be applied to those genes and species for which data on TFBSs and gene homology are available in the BiologicalNetworks integrated database if such data are available gene regu latory regions can be visualized in the browser as it is shown in Figure 4 right window The homology infor mation is imported from the COGs database 32 we extend the COG groups to cover all organisms available in the IntegromeDB database of BiologicalNetworks The Comparative Genomics Browser can be explored together with other modules window
26. es and promoter frameworks linked to the co regulation of genes significant in hypertension in human mouse and rat common transcription factor binding sites etc Search for interactions and novel candidates by regulatory features DBTSS Functional expression and genetic context host pathogen interactions metagenomics meiosis in fungi microbial metabolism and whole genome meta bolic reconstruction in eukaryotes and prokaryotes The BiologicalNetworks database has a general purpose graph architecture and is data type neutral thus there is the prospect of further data integration for more com plete systems biology studies The integration of addi tional orthogonal sources of information such as Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 13 of 14 1 Oxidoreductase activity electron transport P lt 10 2 Chromogranin secretogranin family P lt 10 3 Trancription factor DNA binding activity P lt 10 4 Transcription P lt 10 Gene co epression Bw TF gene binding p TF kinase interaction Highly coexpressed genes A TFs C Hipertension essential TFs and genes Figure 7 Integrated molecular interaction network of human mouse rat hypertension Red rectangles highly co expressed red lines and over expressed genes in hypertension Yellow triangles TFs potentially regulating hypertension specific genes whic
27. f biological knowl edge for example epidemiology or pharmacology e g PharmGKB can be introduced and modified with minimal impact on the rest of the system This is imple mented through ontology mapping 30 for every new adapted ontology for ex SequenceOntology that maps a class for example class Gene that maintains mapping to sour i e class mappingSO 012345 O 12345 a new class ce ontology is generated which is connected to a Gene in our BioNets ontology through same_as Homo sapiens Chromosome 4 8 EEEE Global properties Properties _ A Microarray DataSource Window ijl Projects Pathways DataSource Window types integrated in the BiologicalNetworks database 74606110 74606120 74606130 74606140 74606150 74605160 74606170 74606180 74606190 74606200 74606210 74606220 74606230 Fwuuu mu smi tuuuiuu Ga 118 silencer region cacao Ga GEA a ea ema CMe EAE eS ca EEE et toe B ocactecat aagoe acaaactt tcaganacapcagagcacacaagctt yrovel70 74606280 Figure 3 Genomic Sequence Analysis Browser and Annotations A Using the Sequence Search at the left box the user finds genes in Sequence Browser In addition to gene related information all regulatory regions integrated in the database are represented in Sequence Analysis Browser B In addition to annotations integrated from public databases Bio
28. h TF binding sites are conserved in human mouse and rat Black lines physical protein TF kinase protein TF kinase interactions Green and Blue squares genes proteins TFs that are well known from literature to be essential in hypertension Red lines co expressed genes proteins Dashed lines predicted TFs and their target genes Numbers 1 4 correspond to gene protein groups functionally enriched by particular GO terms shown in the upper left corner clinical data will enable quantitative associations of clin ical variables with the activities of molecular pathways and processes We also demonstrated how Biological Networks can be used to find disease specific interaction networks through the application of multi level analysis of microarray sequence regulatory and other data Besides customization on the level of selecting analysis methods tools in BiologicalNetworks the user has an option to change the parameters of each method for example specify the homology level in the Build Homology Wizard when building the clusters of homo logous genes proteins or specify data sources types of interactions species and p values in the Build Pathway Wizard We are also customizing BiologicalNetworks constantly adding new features methods data formats and sources by the users requests To allow for the replication and comparison of the results presented in this work with other related analysis all available
29. ids Res 2008 36 013 D21 Kanehisa M Goto S KEGG Kyoto Encyclopedia of Genes and Genomes Nucleic Acid Res 2000 27 29 34 Matys V et al TRANSFAC and its module TRANSCompel transcriptional gene regulation in eukaryotes Nucleic Acids Res 2006 34 D108 110 Barrett T Troup DB Wilhite SE Ledoux P Rudnev D Evangelista C Kim IF Soboleva A Tomashevsky M Edgar R NCBI GEO mining tens of millions of expression profiles database and tools update Nucleic Acids Res 2007 35 D760 D765 Parkinson H Kapushesky M Shojatalab M Abeygunawardena N Coulson R Farne A Holloway E Kolesnykov N Lilja P Lukk M Mani R Rayner T Sharma A William E Sarkans U Brazma A ArrayExpress a public database of microarray experiments and gene expression profiles Nucleic Acids Res 2007 35 D747 D750 Fessele S Maier H Zischek C Nelson PJ Werner T Regulatory context is a crucial part of gene function Trends Genet 2002 18 60 63 Liu R McEachin RC States DJ Computationally identifying novel NF kappaB regulated immune genes in the human genome Genome Res 2003 13 654 661 Dohr S Klingenhoff A Maier H Hrabe de Angelis M Werner T Schneider Linking disease associated genes to regulatory networks via promoter organization Nucleic Acids Res 2005 33 864 872 25 26 27 28 29 30 31 32 33 34 35 36 Bf 38 39 40 Page 14 of 14 Qiu P Qin L Sorrentino RP Greene JR Wang L
30. imple Gene Protein search default search For example the search for the three genes proteins p53 egfr esrl returns these three genes in all speci fied species and gene properties The list of genes proteins can be uploaded from the file 2 Keyword search For example the keyword search p53 egfr esr returns all database objects includ ing experiments publications pathways and all properties of all objects that contain either p53 egfr or esrl 3 Wild card search For example the search neu rodeg returns all objects related to neurodegenera tive neurodegeneration and all words in the databases beginning with neurodeg 4 Multi word search For example the search obe sity diabetes no comma separating obesity and diabetes comma means OR returns the results for obesity AND diabetes and obesity OR diabetes 5 Build Pathway Wizard Path Queries Build Path way Wizard contains dozens types of pathway and net work searches in the opened networks pathways or integrated database in protein protein interactions transcription factor DNA networks relational co expression co citation etc networks and curated pathways e g KEGG Figure 5A The three wizard windows Figure 5A allow users to specify a algorithm type for pathway building b select the directionality of relations c types of objects and property values e g speci fic proteins from spe
31. ing sites are color coded in the browser window The whole genome Chro mosome Viewer Figure 1 bottom is a chromosomal representation of the entire genome of a sample This view integrated with experimental data e g microar ray provides easy identification of large scale abnorm alities and the overall aneuploidy of a sample The display consists of a number of bars Figure 1 each representing a chromosome Each bar is composed of a series of colored linear segments each representing a probe The user can click on any clone in the chromo somal viewer to see its clone name and chromosome and all properties e g RefSeq IDs from integrated data base fho Comparative Genomics Browser BiologicalNetworks provides the ability to investigate transcriptional cascades by integrating and visualizing transcription factor gene regulation networks relevant transcription factor binding sites and target genes with multiple sequence annotations thus facilitating valida tion experiments e g primer design applications Figure 4 A fundamental problem in building tran scription factor TF gene networks based on binding motifs in DNA sequences of putative target genes is the rate of false positive predictions of TF binding sites TFBSs To reduce the false positive rate of TFBSs pre dictions phylogenetic footprinting methods are used that search only for genomic DNA sequences which are conserved among species Phylogenetic footprinting
32. ion phenotypes and human anatomy Figure 2 Hypergeometric distri bution with Bonferroni correction is used as the stan dard method for determining statistical significance 27 The result of the analysis is a group of biological themes that are represented as gene clusters by themes Statisti cal reports show the probability that the prevalence of a particular theme within the cluster is due to chance alone given the prevalence of that theme in the popula tion of genes under study Although ontologies are stored as DAGs directed acyclic graph in our internal database we visualize them as tables Themes Tables for the sake of usability and easier navigation Figure 2 Since BiologicalNet works provides Integrative View of the visualized data all windows of the system and data inside them are syn chronized and interconnected by IDs In the Themes Tables this is exemplified by the simultaneous high lighting of all gene protein instances in all opened path ways networks experiments sequences etc representing the same Theme whenever one of them is chosen Terms under different categories are highlighted using different colors All clusters of genes proteins discovered for enriched biological terms are organized by themes under the subfolder Analysis in the Project Panel on the left Clicking on the expansion symbol or double click ing over Project Panel tree nodes will expand or collapse it Other information s
33. logicalNetworks allows users to annotate genomic sequences Annotation Tab and two ontologies used for annotation are represented C Sequence intervals are connected to BioNets Ontology in this case to Sequence Ontology nodes through the internal Objects and Attribute values tables that list objects and attributes of different Froperties Presence Coli B 21918832 W 00 178 B 0 178 178 B 178 00 B 1781409 B 117800 Bi 05 186 521 B 1731780 W 178 078 W 178 122 122 W 00 178 B 10 1780 W 186 32 54 E 0 32 188 E 0 178 178 Bi 52 141 188 E 144 32 195 promoter region FRNA rep_origin OOOooooooeoonooonan 74606290 74606300 74606310 746 Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 relation This is done to not modify BioNets ontology classes every time new ontology is ingested thus while unifying different biological data types to keep specifi city of every member schema of our integrated database Examples of operations on RI trees that will apply on all substructures e g sequence intervals called SUB_X are represented below ifOverlap function SUB X SUB_X gt 0 1 returns true if the two interval substructures overlap Next function SUB_X gt SUB_X is applicable on data types for which there is a strict ordering on the domain it returns the sub structure encountered nex
34. ly how they were at the moment of the file saving The project can be also saved exported to the SIF and SVG formats Integration of users data BiologicalNetworks allows the user to work with his her own data and to integrate them into the system data base IntegromeDB The integration procedure is differ ent from the procedure of loading opening data files described above in that the loaded data are available to the user and also to whoever obtains the BioNets XML Project file that includes the user s data The integration procedure allows the data to be made public they become integrated in IntegromeDB as any other data base and become searchable in the BiologicalNetworks application and at the web page http integromedb org The user can integrate the data at http integromedb org under User s Data Integration menu Figure S2 in Additional File 1 The data will become public but unless it is already curated by the data administrators it will remain tagged as uncurated under the contribu tor s name The integration procedure consists of 3 steps see Figure S2 in Additional File 1 1 Registra tion 2 Data Mapping and 3 Data Integration Data mapping and integration is done automatically it fol lows the same procedure as one described in the section BiologicalNetworks integrated database The user can accept or not accept the resulting mapping To be integrated the data needs to be in the table
35. n the two objects proteins x and Y Page 3 of 14 new information about this interaction will be integrated as a new property s e g a property p value from the experiment A and the experiment A will be added in the database as a new object If a clear evidence of or reference to a class from the BioNets ontology is absent missed an automatic procedure to statistically evaluate the content of the integrated table and assign a term from the ontology is applied The procedure takes each word and word combination in the table search for them in the BioNets ontology calculates the statistical signifi cance of the occurrence and assigns the most significant term to the table Upon the integration of a new data source the database automatically identifies conflicts in names name syno nyms and IDs of all objects in the database genes pro teins organisms etc among various databases and identification systems All names IDs are weighted and sorted by the number of integrated data sources support ing the name ID When the user searches the database names IDs appear in the search result in the descending order by weight the potential conflicts will appear in the bottom of the list Thus if one database for example names the specific gene as x and another database as Y both names x and Y will be equally searchable while in the search result the name that is the most common among all integrated databases will
36. nd other data The user can start searching for an entity of interest and then query for relations to that entity Alternatively the user can Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 9 of 14 Refactor Run Analyse Tools Perspective Window Help fy ee cham mes 8S A A Ales Em ee Wekome Page u RELA pAS RA CC a RELA posh Invertebrates Plants meca E E e Protists Archaea O ed a en erected ated recs pind pehar re oy drect entra ben tected nda roteetotood Fo al sate pt bet neen etd nodet Fin cima tae ft sled noes Fd cementite fa winters la Search Results Window ante BSS Vertebrates 4 Metazoa Bnimals 4 Eukaryotes AN Organisms Protaryotes Danio rerio zebrafish Mycoplasma pneumoniae Takifugu rubripes Dictyostelium discoideum Oyna sativa Xenopus laevis Drosophila melanogaster Plasmodium falciparum Zea mays H t a e Renis for Rel Ren oft pS Microarray DataSource Window Qr pss on search S S NCBI GEO jal a Over Expressed B Under Expressed O Co Expressed fl Triple Co Expressed AGG Big organisms and organism groups Figure 5 BiologicalNetworks data querying environment
37. nd used Comparative Genomics Browser which was described in the above section and shown in Figure 4 Then we searched for TFBSs that were conserved in the orthologous promoters Figure 6 5 Binding sites were filtered at p values below 10 and examined visually for consistency In the result we obtained 103 TFs that might potentially regulate transcription of 18 hypertension specific genes Figure S3B 3C Section 1 5 in Additional File S1 To construct the final net work we searched for all reported interactions among identified 103 TFs and 20 genes Figure 6 6 The obtained network is shown in Figure 7 it consists of 78 TFs proteins genes since depicts only those of ana lyzed TFs proteins genes for which at least one pairwise interaction relation was reported in the databases inte grated in Biological Networks Third to investigate the constructed network we searched the GEO compendium for co expressed TF gene pairs we found pairs that are connected by red lines in Figure 7 We also investigated whether more disease infor mation related to the found genes TFs can be obtained from OMIM database Using the keyword search mechan ism we first searched the integrated database for MeSH terms related to hypertension and then searched for these terms in the OMIM database Among all human genes hypertension associated MeSH terms were found in 504 genes or about 1 6 of all human genes While in the set of 103 transcription facto
38. nformation concerning pathways molecular interac tions gene function expression data sequences tran scription factor binding sites promoters and other gene regulatory regions orthology mutations and disease relations for thousands of eukaryotic prokaryotic and viral organisms Querying capabilities implemented in BiologicalNetworks allows accessing all integrated data simultaneously from specified databases only or in con junction with the user s data BiologicalNetworks pro vides an interactive and user friendly interface with a strong emphasis on graphical data representation This paper aims to describe BiologicalNetworks 2 0 and its application to navigating through the sea of integrated data and extracting biological knowledge Implementation BiologicalNetworks 2 0 is built on the NetBeans plat form from SUN Microsystems 16 The system has a modular architecture and an intuitive and customizable user interface and has been tested for robustness to sys tem failure and big data loads A typical user of Biologi calNetworks loads large GBs datasets from files and analyzes these data in conjunction with publicly avail able data integrated in our database Data uploads to the program are limited only by the user s computer capabilities Now by default a 32 bit Java can upload from local files or from the database 2GB of data and 64 bit Java has practically no limits and can go up to 64 GB and more in modern com
39. on the gene ontology Nucleic Acids Res 2009 1 37 Web Server W115 W121 IPA http www ingenuity com ikitin A Egorov S Daraselia N Mazo Pathway Studio the analysis and navigation of molecular networks Bioinformatics 2003 19 2155 2157 PipelinePilot http accelrys com products pipeline pilot Baitaluk M et al BiologicalNetworks visualization and analysis tool for systems biology Nucleic Acids Res 2006 34 W466 471 Oinn T Addis M Ferris J Marvin D Greenwood M Carver T Pocock MR Wipat A Li P Taverna a tool for the composition and enactment of bioinformatics workflows Bioinformatics 2004 20 3045 3054 Reich M Liefeld T Gould J Lerner J Tamayo P Mesirov JP GenePattern 2 0 Nature Genetics 2006 38 5 500 501 Goecks J Nekrutenko A Taylor J The Galaxy Team Galaxy a comprehensive approach for supporting accessible reproducible and transparent computational research in the life sciences Genome Biology 2010 11 R86 Baitaluk M Ponomarenko J Semantic Integration of Data on Transcriptional Regulation Bioinformatics 2010 26 13 1651 1661 Baitaluk M et al PathSys integrating molecular interaction graphs for systems biology BMC Bioinformatics 2006 7 55 NetBeans platform http www netbeans org Wheeler DL Barrett T Benson DA Bryant SH Canese K Chetvernin V Church DM Dicuccio M Edgar R Federhen S et al Database resources of the National Center for Biotechnology Information Nucleic Ac
40. puters Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 BiologicalNetworks integrated database The backend database of BiologicalNetworks called Inte gromeDB 14 is a semantic graph base deep web data integration system or data warehouse that automatically captures integrates and manages publicly available data concerning transcriptional regulation along with other relevant biological information IntegromeDB integrates over 100 experimental and computational data sources providing genomics transcriptomics genetics functional and interaction data in eukaryotes and prokaryotes The example databases integrated in IntegromeDB include NCBI nucleotide and protein databases 17 metabolic and signaling pathway databases such as KEGG 18 interaction networks database such as STRING 4 and databases of transcription factors and gene regulatory regions such as TRANSFAC 19 the full list of integrated databases is provided at http www biological networks org Database tut5 php IntegromeDB data is stored in a Postgres database under the MetaGraph schema and is updated monthly being synchronized with the latest changes in most current databases Detailed information on the statistics integrated databases catalog and organism list can be found at http www integromedb org The procedure of data integration and mapping to the internal database is fully automated and is bas
41. r found in this study 20 genes were not considered 32 genes or 31 had hypertension associated terms Table S2 in Additional File S1 Figure 7 shows the integrated picture visualizing in BiologicalNetworks of the network of found genes pro teins TFs and interactions among them Well known genes and TFs associated with hypertension are depicted in blue and green squares in Figure 7 while other genes TFs can be suggested for further experimental investigation on association with hypertension and con sidered as drug target candidates for hypertension Simi lar analysis can be done using the genes that were under expressed in the considered microarray experi ment The described project can be seen and further analyzed launching BiologicalNetworks and opening BMC Bioinformatics Demo Project Conclusions The new release of BiologicalNetworks introduces extensive functionality for a more efficient integrated analysis and visualization of diverse data in studies of different biological systems concerning human diseases Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 12 of 14 Significantly differentially expressed genes in a strain b age c strain by age interaction Current biomedical knowledge of hypertension Search GEO compendium for highly correlated gene pairs Genel Gene2 Gene2 Genel Select Up and Down Up Dew n i s p GeneName Fol
42. s of BiologicalNet works For example Figure 4 shows how the browser can be used in tandem with the network pathway win dow on the example of four transcription factors CREB NRSF GATA 1 ATF 1 in three species human mouse and rat Other species including prokaryotes can be also studied subject to data availability Because if the user intends to explore specific genes species in these two modules simultaneously all these types of data interaction networks pathways TFBSs and gene homol ogy must be available The browser displays regions surrounding orthologous genes highlighting orthology relations among them and cases of synteny co localized orthologs The user may change the stringency of evo lutionary conservation of TFBSs and apply tissue specific and cell type specific filters in the results data and their representation visualized in both windows will change synchronically The Comparative Genomics Browser together with other modules of BiologicalNet works forms a valuable tool for investigating transcrip tional cascades as it is described in the section Demonstration Study below Data Querying The BiologicalNetworks interface contains multiple search and build pathways networks capabilities Figure 5 allow ing the simultaneous querying of and the building of path ways networks using microarray or proteomics data networks curated pathways sequences sequence annota tions gene regulatory regions a
43. so that sequence elements represented as a RI tree structure become graph objects within the meta graph database All heterogeneous data for example gene properties in the Property Panel Figure 3 top right integrated in the meta graph data base thus appear to be mapped on genomic intervals and vice versa In the result DNA sequences molecular interaction graphs 3D protein structures images of expression and other types of data integrated in the BiologicalNetworks become connected and annotated within the same context The sequence part of Biologi calNetworks integrated database is updated monthly from the primary public servers GenBank Ensembl and the UCSC Genome Browser database Page 7 of 14 In the Sequence Analysis Browser the user can upload from the local files or retrieve from the database large GBs volumes of sequence data and analyze them together with the integrated data on gene sequences annotations orthologs and cross references to the major biological databases displayed in the Sequence Annota tion Browser Figure 3 The browser window allows any sequence region within specific gene loci or whole genome to be displayed Figure 1 using the set of navi gation tools that are functionally similar to the tools in UCSC Genome Browser 28 The rich collection of sequence features such as exons introns the transcrip tion start site repetitive elements conserved sequence regions and transcription factor bind
44. t in the ordering input substructure The semantics of next depends upon the data types sequence anatomical geo graphical region etc Intersect function SUB_X SUB_X gt SUB_X returns the intersection of two SUB X This operation is valid for convex data types such as sequences and rectangles These operators are extensively used in Sequence Analysis Browser for Navigation scroll upstream down stream get_next gene operon chromosome or next gen e_regulatory region Figure 3 and Annotation of multiply overlapping gene regulatory regions binding sites composite regulatory elements TATA box etc Figure 3B Figure 3 represents different types binding site TATA box operon etc of segment elements and different annotations properties integrated from many data sources for one gene or gene upstream region Genomic Sequences are integrated with the meta graph schema of Biological Networks database through an Ele mentId ObjectID connection table where elements are sequence elements for example a core promoter TATA box or binding site that are attributed to a par ticular gene by means of known localization in the gene according to the GenBank global position Internal enu merations in the integrated databases TRANSFAC for example provides localization of regulatory regions in respect to the transcription start are recalculated accordingly The connection table assigns sequence ele ments to meta graph objects
45. uch as the number of genes directly annotated under the term connections microar ray experiments network sequence data if any etc are also provided Sequence Annotation and Sequence Analysis Browser Sequence data together with annotation data including binding sites promoters and other regulatory regions that have been integrated in the BiologicalNetworks database represent the collection of interval trees a sin gle interval tree is created per chromosome instead of per annotated DNA sequence regions Nodes of the interval RI trees or sequence intervals are connected to the BioNets Ontology http www integromedb org bionetsonto php nodes Figure 3C through the internal Objects and Attribute values tables that list a huge amount of objects and attributes of different types inte grated in the BiologicalNetworks system BioNets Ontol ogy is implemented as a directed labeled meta graph data structure that serves as a general purpose labeled join index The general purpose OWL schema of the Kozhenkov et al BMC Bioinformatics 2010 11 610 http www biomedcentral com 1471 2105 11 610 Page 6 of 14 iological Themes woe AN Molecular Function Cellular Component Biological Process Curated Pathways Tissues Cell Types Human Diseases Mammal Phenotypes Human Anatomy ListHits List Size Pop Hits Pop Size Fisher s Exact Set
46. ular interactions relations and module discovery in high throughput experiments Also implemented in BiologicalNetworks are the Integrated Genome Viewer and Comparative Genomics Browser applications which allow for the search and analysis of gene regulatory regions and their conservation in multiple species in conjunction with molecular pathways networks experimental data and functional annotations Conclusions The new release of BiologicalNetworks together with its back end database introduces extensive functionality for a more efficient integrated multi level analysis of microarray sequence regulatory and other data etworks is freely available at http Awww biologicalnetworks org Background As substantial amounts of data concerning expression interactions pathways sequences and other types of information for a variety of tissues developmental stages stimuli and organisms are generated it becomes difficult for researchers with no background in bioinformatics and statistics to extract the information they seek Suc cessful data integration is hampered by the orthogonal nature of the integrated data and by the multitude of controversies and name ID conflicts in public databases Examples of conflicts that cannot be automatically resolved include the situations when genes with the same name point to different chromosome locations or a gene protein in different modification states has different names for example p53 p53
47. uscript to BioMed Central and take full advantage of e Convenient online submission e Thorough peer review e No space constraints or color figure charges e Immediate publication on acceptance e Inclusion in PubMed CAS Scopus and Google Scholar e Research which is freely available for redistribution Submit your manuscript at www biomedcentral com submit BioMed Central
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