This course has two primary goals:
1) Students will learn where and how to obtain information from public biological databases and how to assess the quality of that information.
2) Students will learn to design, implement, populate and query databases to support their own research, which often requires the integration of  public data with new data.

By the end of the course the student should be able to:  understand datamodels, under stand the basics of using a DBMS,  be able to integrate external and novel scientific data,  understand and employ SQL as a research tool.

This course is essentially a Research Methods course, in which students learn to model and instantiate, in a relational database management system, a practical database to support their own scientific research; students also learn methods for access, and methods for the retrieval and integration of data from relevant public databases that do not depend simply on prepared forms.

The large public repositories use relational schemas, often supplemented with XML schemas. Using these systems at a low level, (i.e. not screen scraping) often requires an understanding of the data representations and how to perform joins and to use SQL for data retrieval. Biological data is often very complex and many valid representations are possible.  This variation is reflected in diversity of database system designs and representations which can lead to difficulties in integration. By the end of this course students will be introduced to strategies for coping with this challenge. As students are introduced to SQL and other programming tools for creating biological databases to support their own research, they will also be expected to learn how to understand and make effective use of existing public repositories.

Particular topics will include formats and schemas in important bioinformatics databases (Genbank, EMBL, PDB, TAIR, SGD, ArrayExpress, GEO), XML schema and XML exchange methods, using generic database tools to browse and manage databases (Pgadmin), the types of models used in designing the preeminent bioinformatics databases, and how ontologies (such as GO and MAGE) affect database design and queries.

As a part of the course, the student will create, populate, and query a database supporting a research area of biological interest (the focus changes each year and in the past has included non-microarray gene expression data and SNP data).