Bioinformatics Databases: Fundamental Concepts of Database Technology & Data Organization Kristen Anton Director of BioInformatics Dartmouth Medical School Dartmouth Medical School

How can data be organized? Paper (i.e. in notebooks) Flat files –Collection of data records –Minimal structure, no metadata –Application program must contain relationship information Database –Hierarchical –Network –Relational

Dartmouth Medical School

How can data be organized? Paper (i.e. in notebooks) Flat files –Collection of data records –Minimal structure, no metadata –Application program must contain relationship information Database –Hierarchical –Network –Relational

Dartmouth Medical School What is a relational database? A database composed of relations and conforming to a set of principles governing how such relations are supposed to behave (“Codd’s 12 Rules”). There are many database systems that use tables but don’t conform to all of the principles. These are often called “semirelational” systems. from Understanding SQL, Martin Gruber

Dartmouth Medical School Practically speaking... A database is a body of information stored in two dimensions (rows and columns) –Rows are records –Columns are attributes of those record entities (usually!) The groups of rows and columns, or tables, are largely independent of each other The power of the database lies in the relationships that you construct among the tables A database is self-describing: it contains metadata, which is a description of its own structure

A set of programs which define, administer and process databases and their associated applications A scalable DBMS can run on multiple platforms (varying sizes) A DBMS that supports interoperability uses industry-standard language and standard ways of exchanging data What is a Database Management System (DBMS)? Examples: Oracle, Sybase, 4D, MS Access … Dartmouth Medical School

Features of a Relational Database Rows (records) are in no particular order Columns (fields) are ordered, numbered and named; names should indicate content of the field Primary key uniquely identifies each row - ensures that no row is empty, and that every row is different from every other row Two-step commit process Dartmouth Medical School

Features of a Relational Database A view is a subset of the database that an application (or user) can process The database schema is the structure of the entire database A constraint is a condition you apply to an attribute of a table Dartmouth Medical School

Relationships between tables One-to-One, Many-to-One, Many-to-Many A “join” is an operation that combines data from multiple tables into a singe result table E-R (entity-relationship) diagram is the basic graphic to describe the structure of a database SELECT Sequence.sname, KnownGenes.gname, KnownGenes.length FROM Sequence, KnownGenes WHERE KnownGenes.length = Sequence.length

Dartmouth Medical School E-R Diagram

The tool for communicating with relational databases: SQL Standard Query Language (SQL) A query is a question you ask the database, and SQL retrieves the appropriate answer set Interactive SQL (command line) vs. RAD tool/GUI Standardization issue: ANSI (American National Standards Institute) Dartmouth Medical School

Data Types Types of data indicate functions that are possible between related fields Each field is assigned one data type (imposes structure on data) Examples: text (CHAR, VARCHAR), number (INT, DEC); date, time, money binary Standardization issue: ANSI (American National Standards Institute) Dartmouth Medical School

Designing a database is not trivial The value is not in the data, but in the structure Design to facilitate the retrieval and interpretation of the data Dartmouth Medical School A word about database design:

Relationships ease extraction and/or reporting of data from the system Redundancy Concept of attributes in rows instead of columns Dartmouth Medical School Design database for data extraction: think it through

Dartmouth Medical School Design database for data extraction: think it through

Dartmouth Medical School Design database for data extraction: think it through

Reusable ‘core’ modules, with customizable components Standard business logic framework controls transactions (middle layer) Metadata-based back-end data storage (facilitates data sharing) Dartmouth Medical School Example: BioInformatics Core Technology

Dartmouth Medical School BioInformatics Core Technology

Data Security: High Priority Dartmouth Medical School HIPAA, FIPS (VA), IRB requirements …

Life science has become a field which generates an enormous amount of un-integrated data. Dartmouth Medical School How can methods for data organization help to solve this problem?

Dartmouth Medical School What is Data Integration? Creating a system which allows the extraction of a piece or set of information (query result) across multiple domains (possibly disparate data sources - flat files, databases, spreadsheets, URLs...)

Dartmouth Medical School Sample integration problem: Cancer Biomarker Discovery Clinical center collects blood samples from 1000 individuals with colon cancer Expression analysis reveals that protein ‘x’ is over-expressed in these samples, relative to controls Could this be a colon cancer biomarker?

Dartmouth Medical School Understanding transcription factors for protein ‘x’ production Show me all genes in the public literature that are putatively related to protein ‘x’, have more than 4-fold expression differential between affected and normal tissue and are homologous to known transcription factors. Q 1 : Find homologs Q 2 : Find genes with 4-fold differential Q 3 : Show me genes in public literature SEQUENCEEXPRESSIONLITERATURE (Q1  Q2  Q3)

Dartmouth Medical School Key components to integration Accessing without modifying original data sources Handling redundant, conflicting, missing, changing (versions) data Normalizing analytical data from different data sources Conforming terminology to industry standards Accessing the integrated data as a single repository Including metadata in repository

Dartmouth Medical School Approaches to Integration where are the key issues addressed? Federated database (poses constraints on original data sources; fragility in reliance on source systems) Data warehousing (ETL layer, original data sources untouched, required understanding of domain, sophisticated update/archive processes) Integrating data source profiles Indexed Flat Files Others….

Dartmouth Medical School Data Warehousing

Dartmouth Medical School Describes data types, relationships, histories, etc. Back-end (supports developers), front-end (supports users and application) Metadata one key to success Data value: 55

Dartmouth Medical School Data value: 55 Metadata values: Data element name: vehicle speed Describes data types, relationships, histories, etc. Back-end (supports developers), front-end (supports users and application) Metadata one key to success

Dartmouth Medical School Data value: 55 Metadata values: Data element name: vehicle speed Unit: miles per hour Describes data types, relationships, histories, etc. Back-end (supports developers), front-end (supports users and application) Metadata one key to success

Dartmouth Medical School Data value: 55 Metadata values: Data element name: vehicle speed Unit: miles per hour Description: the average velocity of a vehicle Describes data types, relationships, histories, etc. Back-end (supports developers), front-end (supports users and application) Metadata one key to success

Dartmouth Medical School Standards the final frontier Naming conventions Standard coordinate systems Unify interpretations of single object types Unify software solutions to the same problem (also data formats) Standards for metadata (incompatible or missing metadata)

Dartmouth Medical School Developing Standards for Life Sciences Research Discovery science does not lend well to constraints (especially system constraints) Decentralized data management infrastructure, competition Wildly varying skill levels for data and information management Several groups (Bio-Ontologies, HGNC, OMG, etc.) and national research initiatives (EDRN, caBIG, etc.) are taking the lead in the effort to create ‘workable’ standards.

New approach to integration: Cancer Biomarker Discovery Network of distributed data ‘silos’ (does not perturb data sources) Centralized query and ‘business logic’ servers, accessed through web interface CORBA framework ‘manages’ XML profile definitions across the web A profile is a set of resource definitions implemented in XML for data sources residing in one or more distributed systems Dartmouth Medical School