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No retraction, correction or concern found when last checked on Oct 16, 2026.

The BioMart community portal: an innovative alternative to large, centralized data repositories

Damian Smedley, Syed Haider, Steffen Durinck and 97 more

Nucleic Acids Research · 2015 · CC BY

doi:10.1093/nar/gkv350

Abstract

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 16, 2026. Paragraph numbers are ours, so an answer can point at one.

INTRODUCTION

The methods of data generation and processing that are utilized in biomedical sciences have radically changed in recent years. With the advancement of new high-throughput technologies, data have grown in terms of quantity as well as complexity. However, the significance of the information that is hidden in the newly generated experimental data can only be deciphered by linking it to other types of biological data that have been accumulated previously. As a result there are already numerous bioinformatics resources and new ones are constantly being created. Typically, each resource comes with its own query interface. This poses a problem for the scientists who want to utilize such resources in their research. Even the simplest task such as compiling results from a few existing resources is challenging due to the lack of a complete, up to date catalogue of already existing resources and the necessity of constantly learning how to navigate new query interfaces. A different challenge is faced by collaborating groups of scientists who independently generate or maintain their own data. Such collaborations are seriously hampered by the lack of a simple data management solution that would make it possible to connect their disparate, geographically distributed data sources and present them in a uniform way to other scientists. The BioMart project has been set up to address these challenges.

SOFTWARE

BioMart is an open source data management system, which is based on a data federation model. Under this model, each data source is managed, updated and released independently by their host organization while the BioMart software provides a unified view of these sources that are distributed worldwide. The data sources are presented to the user through a unified set of graphical and programmatic interfaces so that they appear to be a single integrated database. To navigate this database and compile a query the user does not have to learn the underlying structure of each data source but instead use a set of simple abstractions: datasets, filters and attributes. Once a user's input is provided, the software distributes parts of the query to individual data sources, collects the data and presents the user with the unified result set.

The BioMart software is data agnostic and its applications are not limited to biological data. It is cross-platform and supports many popular relational database managements systems, including MySQL, Oracle, PostgreSQL. It also supports many third party packages such as Taverna, Galaxy, Cytoscape and biomaRt, which part of the Bioconductor library.

The BioMart project currently maintains two independent code bases: one written in Java and one written in Perl. For more information about the architecture and capabilities of each of the packages please refer to previous publications. The latest version of the Java based BioMart software has been significantly enhanced with new additions to the existing collection of graphical user interfaces (GUIs). It has also been re-engineered to provide better support and extensibility for data analysis and visualization tools. The first of the BioMart tools based on this new framework has already been implemented and is accessible from the BioMart Community Portal.

The BioMart project adheres to the open source philosophy that promotes collaboration and code reuse. Two good examples of how this philosophy benefits the scientific community are provided by two independent research groups. The INRA group based in Toulouse, France has recently released a software package called RNAbrowse (RNA-Seq De Novo Assembly Results Browser). The Pfizer group based in La Jolla, USA has just announced the release of OASIS: A Web-based Platform for Exploratory Analysis of Cancer Genome and Transcriptome data (www.oasis-genomics.org). Both of these software packages are based on the BioMart software.

DATA

The BioMart community consists of a wide spectrum of different research groups that use the BioMart technology to provide access to their databases. It currently comprises 30 scientific organizations supporting 38 database projects that contain over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. The BioMart community is constantly growing and since the last publication, 11 new database projects have become available. As new BioMart databases become available locally they also become gradually integrated into the BioMart Community Portal. The main function of the portal is to provide a convenient single point of access to all available data that is distributed worldwide (Figure 1). All BioMart databases that are included in the portal are independently administered and funded. Table 1 provides a detailed list of all BioMart community resources as of March 2015.

PORTAL

The current version of the BioMart Community Portal operates two different instances of the web server: one implemented in Perl and the other in Java. Both servers support complex database searches and although they use different types of GUIs, they share the same navigation and query compilation logic based on selection of datasets, filters and attributes. The Java version of the portal also includes a section for specialized tools, which consists of the following: Sequence retrieval, ID Converter and Enrichment Analysis. Sequence retrieval allows easy querying of sequences while the ID Converter tool allows users to enter or upload a list of identifiers in any format (currently supported by Ensembl), and retrieve the same list converted to any other supported format. The enrichment tool supports enrichment analysis of genes in all species included in the current Ensembl release. For each of those species a broad range of gene identifiers is available. Furthermore, the tool supports cross species analysis using Ensembl homology data. For instance, it is possible to perform a one step enrichment analysis against a human disease dataset using experimental data from any of the species for which human homology data is available. Finally, the enrichment tool facilitates analysis of BED files containing genomic features such as Copy Number Variations or Differentially Methylated Regions. The output is provided in tabular and network graphic format (Figure 2).

WEB SERVICE

The BioMart Community Portal handles queries from several interfaces such as: PERL APIJava APIWeb interfacesURL based accessRESTful web serviceSPARQL

PERL API

Java API

Web interfaces

URL based access

RESTful web service

SPARQL

For more detailed description of all the interfaces please refer to earlier publications. In the section below we provide a description and compare the REST-based web service, which is implemented in Perl and its counterpart, which is implemented in Java. It is worth noting that the web service maintains the same query interface both in Perl and Java implementations. For example, the web service query (Figure 3A) can be run against java-based server as follows: curl –data-urlencode query@query.xml http://central.biomart.org/martservice/resultsor its Perl-based counter-part as belowcurl –data-urlencode query@query.xml http://www.biomart.org/biomart/martservice

curl –data-urlencode query@query.xml http://central.biomart.org/martservice/results

or its Perl-based counter-part as below

curl –data-urlencode query@query.xml http://www.biomart.org/biomart/martservice

By default, query sets the attribute processor to ‘TSV’ requesting tab-delimited results (Figure 3B). Alternatively, by setting processor to ‘JSON’, would return JSON formatted results (Figure 3C), which are readily consumable by third-party web-based clients saving overhead of parsing and format translations. Please note that JSON format is only available in the java version.

A simple way to compile a web service query for later programmatic use is to use one of the web GUIs and generate the query XML using REST/SOAP button. After following the steps outlined by the GUI and clicking the ‘results’ button, the user needs to click the REST/SOAP button, save the query and run it as described above. Alternatively a user can take advantage of the programmatic access to all the metadata defining marts, datasets, filters and attributes. The access to the metadata served by the Java and Perl BioMart servers is provided using the following webservice requests:

Java (central.biomart.org) registry information:http://central.biomart.org/martservice/portalavailable marts:http://central.biomart.org/martservice/martsdatasets available for a config:http://central.biomart.org/martservice/datasets?config=snp_configattributes available for a dataset:http://central.biomart.org/martservice/attributes?datasets=btaurus_snp&config=snp_configfilters available for a dataset:http://central.biomart.org/martservice/filters?datasets=btaurus_snp&config=snp_config

registry information:

http://central.biomart.org/martservice/portal

available marts:

http://central.biomart.org/martservice/marts

datasets available for a config:

http://central.biomart.org/martservice/datasets?config=snp_config

attributes available for a dataset:

http://central.biomart.org/martservice/attributes?datasets=btaurus_snp&config=snp_config

filters available for a dataset:

http://central.biomart.org/martservice/filters?datasets=btaurus_snp&config=snp_config

Perl (www.biomart.org) registry information:http://www.biomart.org/biomart/martservice?type=registrydatasets available for a mart:http://www.biomart.org/biomart/martservice?type=datasets&mart=ensemblattributes available for a dataset:http://www.biomart.org/biomart/martservice?type=attributes&dataset=oanatinus_gene_ensemblfilters available for a dataset:http://www.biomart.org/biomart/martservice?type=filters&dataset=oanatinus_gene_ensemblconfiguration for a dataset:http://www.biomart.org/biomart/martservice?type=configuration&dataset=oanatinus_gene_ensembl

registry information:

http://www.biomart.org/biomart/martservice?type=registry

datasets available for a mart:

http://www.biomart.org/biomart/martservice?type=datasets&mart=ensembl

attributes available for a dataset:

http://www.biomart.org/biomart/martservice?type=attributes&dataset=oanatinus_gene_ensembl

filters available for a dataset:

http://www.biomart.org/biomart/martservice?type=filters&dataset=oanatinus_gene_ensembl

configuration for a dataset:

http://www.biomart.org/biomart/martservice?type=configuration&dataset=oanatinus_gene_ensembl

Please note that the granularity between mart and dataset has been improved in the Java version through the introduction of multiple dataset configs. This facilitates the end-users to browse various views of the same dataset, which are presented through the portal either using a different GUI or subsets of data.

QUERY EXAMPLES

Given the coverage of the current BioMart datatsets, many relevant biological questions can be answered. For example, a researcher who has detected potentially pathogenic variants in FGFR2 (ENSG00000066468) from exome sequencing patients may be interested if the same variants have been previously described and if they were associated with the same or similar diseases. To answer this, integrated data from Ensembl can be queried as shown in Table 2 to display all known variants annotated within FGFR2 that are predicted as pathogenic by SIFT and Polyphen. The genomic position outputs can be compared to the researcher's variants and the phenotype data used to assess candidacy for their cases. For example, the first batch of results shows a C->G variant at position 121520160 on chromosome 10 that is associated with Apert syndrome (OMIM:176943).

Another common use case that BioMart is used for is to analyse a list of genes to establish whether they are associated with particular protein functions, pathways or diseases more often than would be expected by chance (enrichment analysis). For example, a researcher may have discovered that AURKA, AURKB, AURKC, PLK1, CDK1 and CDK4 are differentially expressed in their experiment and used BioMart's enrichment tool with its default settings to analyse these genes. The results show that these genes are enriched for involvement in the cell cycle, kinase activity and mitotic nuclear division amongst others. Many other real usage examples are documented in our previous paper and the BioMart special issue in Database: the journal of biological databases and biocuration (www.oxfordjournals.org/our_journals/databa/biomart_virtual_issue.html).

CONCLUSIONS

Since its conception as a data-mining interface for the Human Genome Project BioMart has rapidly grown to become an international collaboration involving a large number of different groups and organizations both in academia and in industry. It has been successfully applied to many different types of data including genomics, proteomics, model organisms, cancer data, etc., proving that its generic data model is widely applicable. BioMart has also provided a first successful solution for the unprecedented data management needs of the International Cancer Genome Consortium proving that the federated model scales well with the amounts of data generated by Next Generation Sequencing.

There are a number of important factors that contributed to the BioMart's success and its adoption by many different types of projects around the world as their data management platform. BioMart's ability to quickly deploy a website hosting any type of data, user-friendly GUI, several programmatic interfaces and support for third party tools has proved to be an attractive solution for data managers who were in need of a rapid and reliable solution for their user community. BioMart has also proven to be a platform of choice for many smaller organizations that lack the necessary resources to embark on the development of their own data management solution. As a result, more and more database projects have become accessible through the BioMart interface. The arrival of these new resources coupled with the data federation technology provided by the BioMart software has galvanized the creation of the BioMart Community Portal. The federated model has proven to be very cost-effective since all development and maintenance of individual databases is left to the individual data providers. It also has proven to be very scalable as the internet and database traffic is handled by the local BioMart servers. As a result the BioMart Community Portal service has grown impressively not only in terms of available data but also the level of service. The BioMart community portal now averages over million requests per our services per day. Building on this level of service and the wealth of information that has become accessible through the BioMart interface, the BioMart Community Portal has effectively introduced a new, more scalable and much more cost-effective alternative to the large data stores maintained by specialized organizations.

References 53

  1. 1.

    Zhang J., Haider S., Baran J., Cros A., Guberman J.M., Hsu J., Liang Y., Yao L., Kasprzyk A. BioMart: a data federation framework for large collaborative projects. Database. 2011;bar038.

    Not in our library10.1093/database/bar038

  2. 2.

    Hull D., Wolstencroft K., Stevens R., Goble C., Pocock M.R., Li P., Oinn T. Taverna: a tool for building and running workflows of services. Nucleic Acids Res. 2006;34:W729-W732.

    Not in our library10.1093/nar/gkl320

  3. 3.

    Giardine B., Riemer C., Hardison R.C., Burhans R., Elnitski L., Shah P., Zhang Y., Blankenberg D., Albert I., Taylor J., et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005;15:1451-1455.

    Not in our library10.1101/gr.4086505

  4. 4.

    Cline M.S., Smoot M., Cerami E., Kuchinsky A., Landys N., Workman C., Christmas R., Avila-Campilo I., Creech M., Gross B., et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2007;2:2366-2382.

    Not in our library10.1038/nprot.2007.324

  5. 5.

    Durinck S., Moreau Y., Kasprzyk A., Davis S., De Moor B., Brazma A., Huber W. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;21:3439-3440.

    Not in our library10.1093/bioinformatics/bti525

  6. 6.

    Reimers M., Carey V.J. Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol. 2006;411:119-134.

    Not in our library10.1016/s0076-6879(06)11008-3

  7. 7.

    Haider S., Ballester B., Smedley D., Zhang J., Rice P., Kasprzyk A. BioMart Central Portal–unified access to biological data. Nucleic Acids Res. 2009;37:W23-W27.

    Not in our library10.1093/nar/gkp265

  8. 8.
    RNAbrowse: RNA-Seq de novo assembly results browser. · 2014
    Not checked
  9. 9.

    Guberman J.M., Ai J., Arnaiz O., Baran J., Blake A., Baldock R., Chelala C., Croft D., Cros A., Cutts R.J., et al. BioMart Central Portal: an open database network for the biological community. Database. 2011;bar041.

    Not in our library10.1093/database/bar041

  10. 10.
    BioMart – biological queries made easy · 2009
    No notice
  11. 11.

    C Ng Pauline, Henikoff Steven. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812-3814.

    Not in our library10.1093/nar/gkg509

  12. 12.

    A Adzhubei Ivan, Schmidt Steffen, Peshkin Leonid, E Ramensky Vasily, Gerasimova Anna, Bork Peer, S Kondrashov Alexey, R Sunyaev Shamil. A method and server for predicting damaging missense mutations. Nature. 2010;7:248-249.

    Not in our library10.1038/nmeth0410-248

  13. 13.

    Kasprzyk A., Keefe D., Smedley D., London D., Spooner W., Melsopp C., Hammond M., Rocca-Serra P., Cox T., Birney E. EnsMart: a generic system for fast and flexible access to biological data. Genome Res. 2004;14:160-169.

    Not in our library10.1101/gr.1645104

  14. 14.

    Kasprzyk A. BioMart: driving a paradigm change in biological data management. Database. 2011;bar049.

    Not in our library10.1093/database/bar049

  15. 15.

    Mallon A.M., Iyer V., Melvin D., Morgan H., Parkinson H., Brown S.D., Flicek P., Skarnes W.C. Accessing data from the International Mouse Phenotyping Consortium: state of the art and future plans. Mamm. Genome. 2012;23:641-652.

    Not in our library10.1007/s00335-012-9428-9

  16. 16.
    Gateways to the FANTOM5 promoter level mammalian expression atlas · 2015
    No notice
  17. 17.

    Lardenois A., Gattiker A., Collin O., Chalmel F., Primig M. GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle. Database. 2010;baq030.

    Not in our library10.1093/database/baq030

  18. 18.

    Steinbach D., Alaux M., Amselem J., Choisne N., Durand S., Flores R., Keliet A.O., Kimmel E., Lapalu N., Luyten I., et al. GnpIS: an information system to integrate genetic and genomic data from plants and fungi. Database. 2013;bat058.

    Not in our library10.1093/database/bat058

  19. 19.
    GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies · 2013
    No notice
  20. 20.

    The International HapMap Project. Nature. 2003;426:789-796.

    Not in our library10.1038/nature02168

  21. 21.

    Povey S., Lovering R., Bruford E., Wright M., Lush M., Wain H. The HUGO Gene Nomenclature Committee (HGNC). Hum. Genet. 2001;109:678-680.

    Not in our library10.1007/s00439-001-0615-0

  22. 22.

    Lee H.S., Bae T., Lee J.H., Kim D.G., Oh Y.S., Jang Y., Kim J.T., Lee J.J., Innocenti A., Supuran C.T., et al. Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug. BMC Syst. Biol. 2012;6:80.

    Not in our library10.1186/1752-0509-6-80

  23. 23.

    Shaw D.R. Searching the Mouse Genome Informatics (MGI) resources for information on mouse biology from genotype to phenotype. Curr. Protoc. Bioinformatics. 2009;2009.

    Not in our library10.1002/0471250953.bi0107s25

  24. 24.

    Dayem Ullah A.Z., Cutts R.J., Ghetia M., Gadaleta E., Hahn S.A., Crnogorac-Jurcevic T., Lemoine N.R., Chelala C. The pancreatic expression database: recent extensions and updates. Nucleic Acids Res. 2014;42:D944-D949.

    Not in our library10.1093/nar/gkt959

  25. 25.

    Arnaiz O., Sperling L. ParameciumDB in 2011: new tools and new data for functional and comparative genomics of the model ciliate Paramecium tetraurelia. Nucleic Acids Res. 2011;39:D632-D636.

    Not in our library10.1093/nar/gkq918

  26. 26.

    Goodstein D.M., Shu S., Howson R., Neupane R., Hayes R.D., Fazo J., Mitros T., Dirks W., Hellsten U., Putnam N., et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40:D1178-D1186.

    Not in our library10.1093/nar/gkr944

  27. 27.

    Althammer S., Pages A., Eyras E. Predictive models of gene regulation from high-throughput epigenomics data. Comp. Funct. Genomics. 2012;2012:284786.

    Not in our library10.1155/2012/284786

  28. 28.

    Burge S.W., Daub J., Eberhardt R., Tate J., Barquist L., Nawrocki E.P., Eddy S.R., Gardner P.P., Bateman A. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 2013;41:D226-D232.

    Not in our library10.1093/nar/gks1005

  29. 29.

    Zhang S.J., Liu C.J., Shi M., Kong L., Chen J.Y., Zhou W.Z., Zhu X., Yu P., Wang J., Yang X., et al. RhesusBase: a knowledgebase for the monkey research community. Nucleic Acids Res. 2013;41:D892-D905.

    Not in our library10.1093/nar/gks835

  30. 30.
    Rice-Map: a new-generation rice genome browser · 2011
    Not checked
  31. 31.

    Di Genova A., Aravena A., Zapata L., Gonzalez M., Maass A., Iturra P. SalmonDB: a bioinformatics resource for Salmo salar and Oncorhynchus mykiss. Database. 2011;bar050.

    Not in our library10.1093/database/bar050

  32. 32.
    Activities at the Universal Protein Resource (UniProt) · 2013
    No notice
  33. 33.

    Megy K., Emrich S.J., Lawson D., Campbell D., Dialynas E., Hughes D.S., Koscielny G., Louis C., Maccallum R.M., Redmond S.N., et al. VectorBase: improvements to a bioinformatics resource for invertebrate vector genomics. Nucleic Acids Res. 2012;40:D729-D734.

    Not in our library10.1093/nar/gkr1089

  34. 34.

    Harrow J.L., Steward C.A., Frankish A., Gilbert J.G., Gonzalez J.M., Loveland J.E., Mudge J., Sheppard D., Thomas M., Trevanion S., et al. The Vertebrate Genome Annotation browser 10 years on. Nucleic Acids Res. 2014;42:D771-D779.

    Not in our library10.1093/nar/gkt1241

  35. 35.
    WormBase 2014: new views of curated biology · 2013
    No notice
  36. 36.

    Dassi E., Re A., Leo S., Tebaldi T., Pasini L., Peroni D., Quattrone A. AURA 2 Empowering discovery of post-transcriptional networks. Translation. 2014;2:e27738.

    Not in our library10.4161/trla.27738

  37. 37.

    Cutts R.J., Guerra-Assuncao J.A., Gadaleta E., Dayem Ullah A.Z., Chelala C. BCCTBbp: the Breast Cancer Campaign Tissue Bank bioinformatics portal. Nucleic Acids Res. 2015;43:D831-D836.

    Not in our library10.1093/nar/gku984

  38. 38.

    Arnaiz O., Cohen J., Tassin A.M., Koll F. Remodeling Cildb, a popular database for cilia and links for ciliopathies. Cilia. 2014;3:9.

    Not in our library10.1186/2046-2530-3-9

  39. 39.

    Shepherd R., Forbes S.A., Beare D., Bamford S., Cole C.G., Ward S., Bindal N., Gunasekaran P., Jia M., Kok C.Y., et al. Data mining using the Catalogue of Somatic Mutations in Cancer BioMart. Database. 2011;2011:bar018.

    Not in our library10.1093/database/bar018

  40. 40.

    Stevenson P., Richardson L., Venkataraman S., Yang Y., Baldock R. The BioMart interface to the eMouseAtlas gene expression database EMAGE. Database. 2011;2011:bar029.

    Not in our library10.1093/database/bar029

  41. 41.

    Kinsella R.J., Kahari A., Haider S., Zamora J., Proctor G., Spudich G., Almeida-King J., Staines D., Derwent P., Kerhornou A., et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database. 2011;2011:bar030.

    Not in our library10.1093/database/bar030

  42. 42.
    A High-Resolution Anatomical Atlas of the Transcriptome in the Mouse Embryo · 2011
    No notice
  43. 43.

    Spooner W., Youens-Clark K., Staines D., Ware D. GrameneMart: the BioMart data portal for the Gramene project. Database. 2012;2012:bar056.

    Not in our library10.1093/database/bar056

  44. 44.

    Jones P., Binns D., McMenamin C., McAnulla C., Hunter S. The InterPro BioMart: federated query and web service access to the InterPro Resource. Database. 2011;2011:bar033.

    Not in our library10.1093/database/bar033

  45. 45.

    Ndegwa N., Cote R.G., Ovelleiro D., D'Eustachio P., Hermjakob H., Vizcaino J.A., Croft D. Critical amino acid residues in proteins: a BioMart integration of Reactome protein annotations with PRIDE mass spectrometry data and COSMIC somatic mutations. Database. 2011;2011:bar047.

    Not in our library10.1093/database/bar047

  46. 46.

    Moreews F., Rauffet G., Dehais P., Klopp C. SigReannot-mart: a query environment for expression microarray probe re-annotations. Database. 2011;2011:bar025.

    Not in our library10.1093/database/bar025

  47. 47.

    Cutts R.J., Gadaleta E., Lemoine N.R., Chelala C. Using BioMart as a framework to manage and query pancreatic cancer data. Database. 2011;2011:bar024.

    Not in our library10.1093/database/bar024

  48. 48.

    Zhang J., Baran J., Cros A., Guberman J.M., Haider S., Hsu J., Liang Y., Rivkin E., Wang J., Whitty B., et al. International Cancer Genome Consortium Data Portal–a one-stop shop for cancer genomics data. Database. 2011;2011:bar026.

    Not in our library10.1093/database/bar026

  49. 49.

    Oakley D.J., Iyer V., Skarnes W.C., Smedley D. BioMart as an integration solution for the International Knockout Mouse Consortium. Database. 2011;2011:bar028.

    Not in our library10.1093/database/bar028

  50. 50.

    Croft D., O'Kelly G., Wu G., Haw R., Gillespie M., Matthews L., Caudy M., Garapati P., Gopinath G., Jassal B., et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39:D691-D697.

    Not in our library10.1093/nar/gkq1018

  51. 51.

    Perez-Llamas C., Gundem G., Lopez-Bigas N. Integrative cancer genomics (IntOGen) in Biomart. Database. 2011;2011:bar039.

    Not in our library10.1093/database/bar039

  52. 52.
    The International Mouse Phenotyping Consortium Web Portal, a unified point of access for knockout mice and related phenotyping data · 2013
    No notice
  53. 53.

    Wilkinson P., Sengerova J., Matteoni R., Chen C.K., Soulat G., Ureta-Vidal A., Fessele S., Hagn M., Massimi M., Pickford K., et al. EMMA–mouse mutant resources for the international scientific community. Nucleic Acids Res. 2010;38:D570-D576.

    Not in our library10.1093/nar/gkp799

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