Bioinformatics Databases: Fundamental Concepts of Database Technology & Data Organization Kristen...

Bioinformatics Databases:Fundamental Concepts of

Database Technology & Data Organization

Kristen AntonDirector of BioInformaticsDartmouth Medical School

BioInformatics @ 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


What is a relational database?

A database composed of relations and conformingto 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


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 …BioInformatics @ 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


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



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


E-R Diagram

The tool for communicating withrelational 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)


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)


• 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


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


Design database for data extraction: think it through



Subject IDAddressCity/TownStateZipcodeDOB...

Subject IDSibling IDSibling Cancer TypeSibling Dx Date...

• Reusable ‘core’ modules, with customizable components

• Standard business logic framework controls transactions (middle layer)

• Metadata-based back-end data storage (facilitates data sharing)


Example: BioInformatics Core Technology


BioInformatics Core Technology

Sybase

Authntic.db

Events.db

Questions.db

Specimen.dbSpecific to

study

Others...

Specimen Trackingdefine/create/edit/destroyItem ()define/create/edit/destroyPkg()add/deleteItemFrmPkg()send/receivePkg()

Authntication

Event Track

Utilities

Questions

Spec Track

Auth Tools Spec Tools Quest Tools Event Tools Utility Tools

Database Access

Web Apps

ISQL/Reportscreate/edit/destroyReportQueryadd/edit/deleteQueryParam

GenericSQLMethodmake/get/destroyConnection()prepareTheCall/Statement()executeQuery/Update()

create/edit/retireUsergrant/revokeUserPermissions

HTML

HTML Tools

Data Security: High Priority


HIPAA, FIPS 140-2 (VA), IRB

requirements …

Life science has become a field which generates an enormous amount of un-integrated data.


How can methods for data organization help to solve this

problem?


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...)


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?


Understanding transcription factors for protein ‘x’ productionShow 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.

Q1: Find homologsQ2: Find genes with

4-fold differentialQ3: Show me genesin public literature

SEQUENCE EXPRESSION LITERATURE

(Q1 Q2 Q3)


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


Approaches to Integrationwhere 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….


Data Warehousing

E(Extraction)

T(Transformation)

L(Load)

Source Data Data Warehouse


• Describes data types, relationships, histories, etc.

• Back-end (supports developers), front-end (supports users and application)

Metadataone key to success

Data value: 55


Data value: 55Metadata values:

Data element name: vehicle speed






Data element name: vehicle speedUnit: miles per hour






Data element name: vehicle speedUnit: miles per hourDescription: the average velocity of a vehicle





Standardsthe 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)


Developing Standardsfor 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


Bioinformatics Databases: Fundamental Concepts of Database Technology & Data Organization Kristen...

Documents

Transcript of Bioinformatics Databases: Fundamental Concepts of Database Technology & Data Organization Kristen...