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BioMart is a query-oriented data management system developed jointly by the Ontario Institute for Cancer Research (OiCR) and the European Bioinformatics Institute (EBI). The system can be used with any type of data and is particularly suited for providing 'data mining' like searches of complex descriptive data. BioMart comes with an 'out of the box' website that can be installed, configured and customised according to user requirements. Further access is provided by graphical and text based applications or programmatically using web services or API written in Perl and Java. BioMart has built-in support for query optimisation and data federation and in addition can be configured to work as a DAS 1.5 Annotation server. The process of converting a data source into BioMart format is fully automated by the tools included in the package. Currently supported RDBMS platforms are MySQL, Oracle and Postgres. BioMart is completely Open Source, licensed under the LGPL, and freely available to anyone without restrictions.

Why bother about openness and licensing for data? After all they don't matter in themselves: what we really care about are things like the progress of human knowledge or the freedom to understand and share.

SWAN (Semantic Web Applications in Neuromedicine) is a Web-based collaborative program that aims to organize and annotate scientific knowledge about Alzheimer disease (AD) and other neurodegenerative disorders. Its goal is to facilitate the formation, development and testing of hypotheses about the disease. The ultimate goal of this project is to create tools and resources to manage the evolving universe of data and information about AD in such a way that researchers can easily comprehend their larger context ("what hypothesis does this support or contradict?"), compare and contrast hypotheses ("where do these two hypotheses agree and disagree?"), identify unanswered questions and synthesize concepts and data into ever more comprehensive and useful hypotheses and treatment targets for this disease. The SWAN project is designed to allow the community of AD researchers to author, curate and connect a diversity of data and ideas about AD via secure personal and public SWAN workspaces, using the emerging Semantic Web paradigm for deep interconnection of data, information and knowledge. We are initially focusing on developing a fully public Web resource deployed as part of the Alzheimer Research Forum web site (www.alzforum.org). After the public resource has been launched, we will also develop secure personal workspaces (MySWAN) and semi-private lab workspaces (LabSWAN). An essential component of this project is development of an initial, core knowledge base within SWAN, which will provide immediate value to researchers at the time of deployment. This is a critically important part of our strategy to ensure that the SWAN system gains wide adoption and active participation by the AD research community. As part of our development strategy, we are also recruiting a "beta test" community of AD researchers to enter their own hypotheses, add commentaries and citations, and provide feedback on the technology and content. SWAN is being developed by a collaborative team from

If required, entire databases can be downloaded from our FTP site in a variety of formats, from flat files to MySQL dumps. Please be aware that these files can run to many gigabytes of data. To facilitate storage and download all databases are GNU Zip (gzip, *.gz) compressed. Please note: Ensembl supports downloading of many correlation tables via the highly customisable BioMart data mining tool. You may find exploring this web-based data mining tool easier than extracting information from our database dumps.

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods allowing scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form. Our visionis that all biomedical knowledge and data are disseminated on the Internet using principled ontologies, such that they are semantically interoperable and useful for improving biomedical science and clinical care. Our resources include the Open Biomedical Ontologies (OBO) library, the Open Biomedical Data (OBD) repositories, and tools for accessing and using this information in research. The Center collaborates with biomedical researchers conducting Driving Biological Projects to enable outside research and stimulate technology development in the Center. The Center undertakes outreach and educational activities (Biomedical Informatics Program) to train future researchers to use biomedical ontologies and related tools with the goal of enhancing scientific discovery.

CloudBurst: Highly Sensitive Short Read Mapping with MapReduce Michael Schatz Center for Bioinformatics and Computational Biology, University of Maryland Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping, and personal genomics. It is modeled after the short read mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments. In a large remote compute clouds with 96 cores, CloudBurst reduces the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome. CloudBurst is available open-source as a model for parallelizing other bioinformatics algorithms with MapReduce.

Hadoop comes bundled with launch scripts to simplify initializing an Amazon Elastic Compute Cloud (EC2) cloud for Hadoop. Once initialized, running CloudBurst is identical to running on a local cluster. If you use EC2 regularly with the same datasets (i.e. the human genome as a reference), you will probably want to copy the data once to Amazon Simple Storage Service (S3) so you can quickly copy the data from S3 to your cloud at low cost.

The Neuroscience Information Framework (NIF) is a dynamic inventory of web-based neurosciences data, resources, and tools that scientists and students can access via any computer connected to the Internet. An initiative of the NIH Blueprint for Neuroscience Research, the NIF will advance neuroscience research by enabling discovery and access to public research data and tools worldwide through an open source, networked environment.

Not a few biologists tend to consider wiki as a solution to manage and reorganize data by a community. However, in its basic functionality, wiki lacks a measure to check data consistency and is not suitable for a database. To circumvent this pitfall, installation of page dependency through in-line page searches is necessary. We also introduce two existing approaches that support in-line queries.

For large amounts of data and more detailed analysis, we recommend you use our publicly-accessible MySQL server, ensembldb.ensembl.org, which you can access as user 'anonymous'. A second server, martdb.ensembl.org provides public access to the BioMart databases.

GenBioWiki is the student home page for the Genetics, Bioinformatics, and Computational Biology (GBCB) program at Virginia Tech. Bioinformatics and computational biology provide a research platform to acquire, manage, analyze, and display large amounts of data, which in turn catalyze a systems approach to understanding biological organisms, as well as making useful predictions about their behavior in response to environmental and other perturbations. Moreover, bioinformatics is the study of biological systems and large biological data sets using analytical methods borrowed from computer science, mathematics, statistics, and the physical sciences. This transdisciplinary approach to research requires graduates with extensive cross-cultural professional and technical training and provides ample employment opportunities for Ph.D. graduates. [1]

The establishment of open access literature makes it possible for knowledge to be extracted from scholarly articles and included in other resources. BioLit aims to extract database identifiers and rich meta-data from open access articles in the life sciences and integrate that information with existing biological databases. We have begun prototyping this effort using a clone of the RCSB Protein Data Bank, a database of macromolecular structures.

With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to debunk several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.)

Processing (BETA) library to communicate with MySQL (or any other SQL) databases. note that due to java security restrictions this will not work with applets "out of the box" and that many remote mysql-servers will only allow local access ("localhost") or connections from trusted hosts. (see notes. ) also note that you should have some experience with SQL to put, change and retrieve data from the database.