Biological Databases, Integration, and Semantic Web

Kei Cheung, Ph.D.

Yale Center for Medical Informatics

Genomics and Bioinformatics, December 4, 2006

Outline

• Database introduction– Overview– Query language

• Database integration– Issues

• Semantic Web approach to database integration– Overview of Semantic Web

Introduction• The Human Genome Project has transformed the

biological sciences into information sciences• Advances in the biological sciences depend on:

– creation of new knowledge– effective information management

• Future progress in biological research will be highly dependent on the ability of the scientific community to both deposit and utilize stored information on-line.

• The database challenge for the future will be to develop new ways to acquire, store and retrieve not only biological data, but also the biological context for these data.

Variety of Biological Databases

• Different data categories– DNA sequence, gene expression, protein

structure, pathway, etc

• Community vs. lab-specific vs. proprietary databases

• Mega vs. medium vs. boutique databases

• One thing in common: many of them are Web accessible

Food for thoughts

• Will a biological database different a biological journal?

What is a database?

• A database is a collection of records stored in a computer in a systematic way, so that a computer program can consult it to answer questions.

• The items retrieved in answer to queries become information that can be used to make decisions.

• The computer program used to manage and query a database is known as a database management system (DBMS) – E.g., Oracle, MS Access, MySQL

Database components

• The central concept of a database is that of a collection of records, or pieces of knowledge

• For a given database, there is a structural description of the type of facts held in that database: this description is known as a schema

• The schema describes the objects that are represented in the database, and the relationships among them.

Data Model

• There are a number of different ways of organizing a schema (i.e., of modeling the database structure): these are known as data models. – Relational model– Hierarchical model– Network model– Object oriented model

Query Language

• A query language is a computer languages used to create, modify, retrieve and manipulate data from databases

• SQL (Structured Query Language) is a well-known query language for relational databases– SQL is an ANSI standard language for RDBMS’s– Different RDBMS’s vendors may provide slightly

different SQL syntax or additional proprietary extensions that are applicable only to their systems

SQL

• CREATE TABLE• INSERT• SELECT• UPDATE• DELETE• CREATE VIEW

CREATE TABLE

CREATE TABLE <tablename> (<column1> <data type1> [<constraint1>], <column2> <data type2> [<constraint2>], <column3> <data type3> [<constraint3>],

…);

Example

CREATE TABLE sgd_features(sgd_id VARCHAR(20) NOT NULL PRIMARY KEY,feature_type VARCHAR(20) NOT NULL DEFAULT ‘ORF’,quality VARCHAR(20),feature_name VARCHAR(20),standard_name VARCHAR(20),chromosome INT(2) NOT NULL,start_coord INT(10) NOT NULL,end_coord INT(10) NOT NULL,strand CHAR(1) NOT NULL,description VARCHAR(500)

);

INSERT

INSERT INTO <table> (<column1>, …, <columnN>)VALUES (<value1>, …, <valueN>);

Example…INSERT INTO empinfo (sgd_id, feature_type, feature_name,chromosome, start_coord, stop_coord,strand, description)VALUES (‘S000006692’, ‘tRNA’, ‘tQ(UUG)C’, 3, 168368, 168297, ‘C’, ‘tRNA-Gln’);…

SELECT

SELECT [DISTINCT] <col1> [as <alias1>] [, <col2> [as <alias2>], ...]FROM <table1> [as <alias1>] [, <table2> [as <alias2>] , …]WHERE <Boolean conditions>;[additional clauses]

Typical Conditional Operators: =, >, >=, <, <=, <>, LIKE, IN

Additional Clauses: ORDER BY, GROUP BY, HAVING, LIMIT

Built-in functions: UPPER, LOWER, SUBSTRING, LENGTH, COUNT, MAX, MIN, AVG, etc

DISTINCT

SELECT DISTINCT chromosomeFROM sgd_featuresWHERE (feature_type=‘ORF’);

The answer is: 1,2,4,9,10,15,16,17

count function

SELECT COUNT(*)FROM sgd_featuresWHERE (start_coord < 300000) AND (feature_name LIKE ‘Y%’);

The answer is 6

string function

SELECT LENGTH(feature_name)FROM sgd_featuresWHERE (id=‘S000007274’);

The answer is 5

math function

SELECT MIN(start_coord) FROM sgd_featuresWHERE (strand=‘W’);

ORDER BY

SELECT sgd_id, feature_type, feature_name, chromosomeFROM sgd_featuresWHERE (feature_name like ‘Y%’)ORDER BY start_coord DESC;

GROUP BYThis allows aggregate function to be performed on the column(s)

SELECT feature_type, AVG(stop_coord-start_coord) as “avg_diff” FROM sgd_features WHERE (strand = ‘W’) GROUP BY feature_type;

HAVINGThis is the same as the WHERE clause except it is performed upon the data that have already retrieved from the database

SELECT feature_type, AVG(stop_coord-start_coord) as ‘avg_diff’FROM sgd_features WHERE (strand = ‘W’) GROUP BY feature_type;

HAVING (AVG(stop_coord-start_coord) > 1000);

LIMIT [start, ] rowsReturns only the specified number of rows.

SELECT * WHERE (feature_type=‘ORF’) LIMIT 3

Join

SELECT s.feature_name, s.feature_type,s.chromosome, g.bio_function, g.bio_process,g.cell_locationFROM sgd_features as s, gene_ont as gWHERE (s.sgd_id=g.sgdid);

sgd_features

gene_ont

UPDATE

UPDATE <table> SET <col1>=<val1> [,<col2>=<val2>, …][WHERE clause];

Example

UPDATE empinfo SET quality=‘Verified’WHERE sgd_id=‘S00000010’

DELETE

DELETE FROM <table> [WHERE clause];

Example

DELETE FROM sgd_features WHERE sgd_id IN (‘S00000010’, ‘S000003599’);

DELETE FROM sgd_features;(this deletes all data in the table)

CREATE VIEW

CREATE VIEW <viewname> [<col1>, <col2>, …]AS SELECT …;

Example (VIEW)

CREATE VIEW sgd_features_ORF_WAS SELECT *FROM sgd_features WHERE feature_type=‘ORF’ AND strand=‘W’;

Other Database Topics

• Normalization

• Query optimization

• Maintenance

Database Integration

Needs for database integration

• Biological data are more meaningful in context, no single DB supplies a complete context for a given biological research study

• New hypotheses are derived by generalizing across a multitude of examples from different DBs

• Integration of related data enables validation and consistency checking

Example• Find the genes for a metabolic pathway,

which are localized within a genome (e.g., find the clusters of genes involved in tryptophan biosynthesis, and in the histidine biosysthesis, in the E. coli genome)

• This query involves integrating data from pathway databases and genome mapping databases

Issues

Growth in the number of biological databases (published in NAR DB issue)

Database Size

Database Complexity(e.g., DNA Microarray Database)

Different Levels of Heterogeneity

• This problem arises from the fact that different databases are designed and developed independently to address local needs.

• There are two broad levels of heterogeneity– Syntactic heterogeneity– Semantic heterogeneity

Syntactic Heterogeneity

• Technological heterogeneity– Difference in hardware platforms, operating systems,

access methods (e.g., HTTP, ODBC, etc)– Difference in database engines, query languages,

data access interfaces, etc

• Different data formats/models – structured vs. un-structured data, files vs. databases,

etc– Object-oriented vs. relational data model

Semantic Heterogeneity

• Nomenclature problem– Gene/protein symbols/names (based on phenotype, sequence, function,

organisms, etc)• TSC1 • ABCC2 • ALDH• Sonic Hedgehog

– ID proliferation • Different ID schemes: 1OF1  (PDB ID) and P06478 (SwissProt ID) correspond to

Herpes Thymidine Kinase• Lexcial variation: GO1234, GO:1234, GO-1234

– Synonyms vs. homonyms• Dopamine receptor D2: DRD2, DRD-2, D2• Armadillo (fruitflies) vs. i-catenin (mice)• PSM1 (human) = PSM2 (yeast); PSM1 (yeast) = PSM2 (human)• PSA: prostate specific antigen, puromycin-sensitive aminopeptidase, psoriatric arthritis,

professional skaters association

• “Biologists would rather share their toothbrush than a gene name … Gene nomenclature is beyond redemption”, said Michael Ashburner

Other Semantic Heterogeities

• Inconsistent values– The same set of markers may be found in different

orders across different mapping databases (e.g., GDB and DB/12)

• Different units of measurement– Kb vs. bp

• Different data coding schemes– over-expressed vs. 2-fold change

Use of Standards to Address the Heterogeneity Issue

• Data specification standards (e.g. MIAME)

• Data representation standards – Syntax (e.g., XML, ASN.1)– Structure (e.g., MAGE-ML)

• Standard vocabulary/ontology (e.g., MGED ontology working group, gene ontology, etc)

Semantic Web

Semantic Web• It provides a standard framework that allows data to be

integrated and reused across application, enterprise, and community boundaries

• It is a web of data linked up in such a way as to be easily processable by machines, on a global scale.

• It is about two things:– It is about common formats for interchange of data– It is about language for recording how the data relates

to real world objects. • It is a collaborative effort led by the World Wide Web

Consortium or W3C with participation from a large number of researchers and industrial partners

Semantic Web for the Life Sciences

• “… the life sciences are a flagship area for the semantic web …” (Tim Berners-Lee)

• “… Today, boundaries that inhibit data sharing limit innovation in research and clinical settings alike, and impede the efficient delivery of care. Semantic web technologies give us a chance to solve this problem, resulting, ultimately, in faster drug targeting, more accurate reporting, and better patient outcomes.” (Susan Hockfield, President of MIT)

• Semantic Web Health Care and Life Sciences Interest Group (SW HCLSIG)– http://www.w3.org/2001/sw/hcls/

Component Technologies of Semantic Web

• Uniform Resource Identifier (URI)– A standard means of addressing resources on the Web – e.g., http://en.wikipedia.org/wiki/Protein_P53

• Ontology – Specification of a conceptualization of a knowledge domain

• Ontological Language– Resource Description Framework (RDF)– Web Ontology Language (OWL)– Both RDF and OWL have XML serialization

• Database and Tool– RDF databases: Sesame, Kowari, Oracle– OWL Reasoners: Racer, Pellet, FaCT

RDF Statement

A RDF statement consists of:• Subject: resource identified by a URI• Predicate: property (as defined in a name space identified by a

URI) • Object: property value (literal) or a resource

For example, the dbSNP Website is a subject, creator is apredicate, NCBI is an object.

A resource can be described by multiple statements.

<?xml version="1.0"?> <rdf:RDF xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:dc=“http://purl.org/dc/elements/1.1” xmlns:ex=“http://www.example.org/terms”>

<rdf:Description about=“http://www.ncbi.nlm.nih.gov/SNP”><dc:creator rdf:resource=“http://www.ncbi.nlm.nih.gov”></dc:creator><dc:language>en</dc:language>date>

</rdf:Description></rdf:RDF>

Graphical & XML Representation

http://www.ncbi.nlm.nih.gov/SNP

http://www.ncbi.nlm.nih.gov

http://purl.org/dc/elements/1.1/creator http://purl.org/dc/elements/1.1/language

en

RDF Schema (RDFS)

• RDF Schema terms:– Class– Property– type– subClassOf– range– domain

• Example:<Person,type,Class><has_parent,type,Property><Family_member,subClassOf,Person><“Joe Smith”, type, Family_member><has_parent, range, Family_member><has_parent, domain, Family_member>

Web Ontology Language (OWL)

• OWL builds on top of of RDF

• It semantically extends RDFS

• It is based on description logics

• Three species of OWL– OWL-Lite– OWL-DL– OWL-Full

Current Syntactic Web vs. Future Semantic Web

Form Vision to Implementation:Data Mashup

Data Mashup

• It refers to web resources that weave data from different web resources into a new service

• Disciplines like biosciences would benefit greatly from data mashups

• Data mashup is difficult to create without sharing data in a standard machine-readable format

Nature’s Data Mashup: A Google Earth Application

Tracking of Avian Flu

The End