Data Representation Data Representation in Bioinformaticsin Bioinformatics
S. Sudarshan
Computer Science and Engg. Dept.
I.I.T. Bombay
S. Sudarshan, IIT Bombay2
Data RepresentationData Representation
Goal: Represent data in an intuitive and convenient manner Without unnecessary replication of information
Making it easy to write queries to find required information
Supporting efficient retrieval of required information
Data Models Ad-hoc file formats (not really data models!)
XML (Extensible Markup Language)
Relational data model
Entity-relationship (ER) data model
Object-relational data model
Object-oriented data model
S. Sudarshan, IIT Bombay3
Data Representation in GenomicsData Representation in Genomics
Most common approach: Text Files E.g. GenBank: GenBank Example
Advantage: Easy to export data to others (integrating datasets is not my problem!)
Drawback: Makes it hard to integrate information from different sources
This is essential for many applications e.g. comparative studies
Multiplicity of formats makes interoperation difficult
Reading a particular file format requires a program designed to “parse” that file format
No standard query language
Complex queries needed to integrate data from different sources
Several efforts to create standard file formats are based on a “tag” language called XML
S. Sudarshan, IIT Bombay4
LOCUS AB020037 300 bp mRNA EST 11-MAY-1999DEFINITION AB020037 Phaseolus vulgaris library (Watanabe T)
cDNA, mRNA sequence. ACCESSION AB020037 VERSION AB020037.1 GI:4783241 KEYWORDS EST. SOURCE Phaseolus vulgaris. ORGANISM Phaseolus vulgaris
Eukaryota; Viridiplantae; Streptophyta; Embryophyta; … REFERENCE 1 (bases 1 to 300) AUTHORS Watanabe,T., Watanabe,T, …. TITLE Partial cDNA G.max calnexin homologue from P.vulgaris JOURNAL Unpublished (1999) FEATURES Location/Qualifiers source 1..300
/organism="Phaseolus vulgaris" /db_xref="taxon:3885" /clone_lib="Phaseolus vulgaris library (Watanabe T)"
BASE COUNT 92 a 50 c 82 g 76 t ORIGIN 1 gacctgcgat cttctacgaa tcattcgatg aggattttca agatcgttgg atcgtgtctc 61 agaaagagga atacagtggt gtctggaaac atgccaagag tgagggacat gatgatcatg 121 gtcttcttgt cagtgagaaa gcaagaaaat atgccatagt gaaggaactt gacaaggcag 181 tgagtctcag ggatggaact gttgttctcc agtttgaaac tcggcttcag aatggacttg 241 aatgtgaagg agcatatata aaatatctcc gaccacaggg atgctggatg ggaactctaa//
Genbank Example
S. Sudarshan, IIT Bombay5
XML: Extensible Markup LanguageXML: Extensible Markup Language Simple XML example
E.g. <faculty> <faculty-member facid=12349> <name> S.Sudarshan </name> <email> [email protected]</email> </faculty-member> <faculty-member facid=12987> <name> Pramod Wangikar</name> <email> [email protected]</email> </faculty-member> </faculty>
Each piece of text enclosed by matching tags <xyz> … </xyz> is called an element
Elements may have attributes (such as facid in the example above) DTD (Document Type Descriptor) specifies allowed element,
attributes of each element, and what elements may appear within each element (and how many times and in what order).
Each application defines a standard set of elements (including how they are nested) and attributes for each element
S. Sudarshan, IIT Bombay6
XML Representation (Cont.)XML Representation (Cont.)
Ad-hoc file representations are being replaced by standard XML representations (see e.g. http://i3c.open-bio.org) Examples:
Gene Expression Markup Language (GEML) (http://www.geml.org)
– (GEML 2.0 white paper: http://www.geml.org/docs/GEML2_0.pdf) Bioinformatic Sequence Markup Language (BSML) (
http://www.labbook.com/products/xmlbsml.asp), and many others
– Earlier GenBank example in in XML (BSML) Benefits
Standardization will simplify inter-operation and data sharing XML tagged datasets are easy to read and comprehend Parsing of datasets is simple with XML
Problems: Standards take time to develop (for human/political reasons) More than one standard may evolve People may not adopt standards, sticking to old formats Support for querying on XML data is still poor (but will improve)
S. Sudarshan, IIT Bombay7
Genbank Example in XML (BSML)Genbank Example in XML (BSML)
<?xml version="1.0" ?> <records> <record> <locus name="AB020037" bp="300" strands="" molecule="mRNA" geometry="linear" division="EST" date="11-MAY-1999"/> <definition> <![CDATA[ AB020037 Phaseolus vulgaris library (Watanabe T) Phaseolus vulgaris cDNA, mRNA sequence ]]> </definition> <accession name="AB020037"/> <version accession="AB020037.1" gi="4783241"/> <keywords> EST </keywords>
……..
…….
S. Sudarshan, IIT Bombay8
Present vs. FuturePresent vs. Future
XML databases are coming but not quite here yet In alpha versions at best Some relational database provide support for storing XML data, but no
support or poor support for quering complex XML data XML query language is still being standardized (XQuery) Initial XML query implementations likely to be poor compared to
relational query implementations which are mature Interesting query execution/optimization problems to be solved, even
ignoring bioinformatics
Relational data can be viewed as a special case of XML data Issues we describe in next few slides also applicable to XML
representation XML good for data exchange Can easily convert simple XML data to relations
Perhaps a few years down the road we can use XML for querying genomics data
S. Sudarshan, IIT Bombay9
What are Relations What are Relations
PramodSeshadri
UdaySudarshan
Name
[email protected]@em.com
[email protected]@iitb.ac.in
Chem. Engg.Mech. Engg.Elec. Engg.Comp. Sci.
Department
faculty
Attributes or columns
Tuples or rows
S. Sudarshan, IIT Bombay10
Relational RepresentationRelational Representation
The relational data model is widely used and supported by all the popular commercial database systems
Allows 1) information to be broken up into logical units, and then 2) recombined in different ways as required Great for queries involving information from multiple original sources Can easily gather related information
e.g. information about a particular gene from multiple datasets/experiments
Entity Relationship (E-R) Model: Higher level model than the relational model Often used for design, and then converted (automatically or
manually) into a relational schema Has several diagrammatical representations Widely used
S. Sudarshan, IIT Bombay11
Entities and RelationshipsEntities and Relationships
A database can be modeled as: a collection of entities,
relationship among entities.
An entity is an object that exists and is distinguishable from other objects.
Example: gene, protein, experiment, organism, person
Entities have attributes
An entity set is a set of entities of the same type that share the same properties.
Example: set of all persons, companies, trees, holidays
Relationships provide connections between two or more entities E.g. Which genes were involved in which experiment
S. Sudarshan, IIT Bombay12
Example ER Diagram for Microarray DataExample ER Diagram for Microarray Data Entities represented by boxes, (binary) relationships by lines with names
and optional attributes See www.bioinf.man.ac.uk for a more realistic version (the MaxD
database)
Experiment Experiment-Id Date Image
Experimenter Experimenter-Id Name E-mail Dept. Institution
Sample Sample-Id Organism Cell-type {Drug-Ids}
Array Array-Id Manufacturer Type Batch
Gene gene-id sequence……
Expt-Exptr
Expt-Sample
Expt-Array
Expression-valuevalue
* 1
Many-to-one
Notation
S. Sudarshan, IIT Bombay13
Schema Diagrams for MicroArray Data Schema Diagrams for MicroArray Data Schema diagrams show multiple relations and their interconnections
Lines link foreign key with referenced relation
Experiment Experiment-Id Date Experimenter-Id Sample-Id Array-Id Image
Experimenter Experimenter-Id Name E-mail Dept. Institution
Sample Sample-Id Organism Cell-type {Drug-Ids}
Array Array-Id Manufacturer Type Batch
Multivalued attribute
Gene Gene-Id sequence
Expression-Value Experiment-Id Gene-Id value
S. Sudarshan, IIT Bombay14
Modeling Protein Data (from Paton & Goble)Modeling Protein Data (from Paton & Goble)
S. Sudarshan, IIT Bombay15
Schema Diagrams vs. ER NotationSchema Diagrams vs. ER Notation
Don’t confuse ER diagrams with schema diagrams
Differences: In ER diagrams:
lines have names
There are no explicit foreign key attributes
In schema diagrams
Lines don’t have names, but represent foreign key relationships
Foreign key attributes must be explicitly represented
Relationships in ER diagrams get converted to separate relations and/or foreign key relationships (more on this later)
S. Sudarshan, IIT Bombay16
Query LanguagesQuery Languages Language in which user requests information from the database. Categories of languages
Procedural E.g. C/C++/Java Advantage: Powerful, can specify any query by programming Disadvantage: Interfacing directly to database is cumbersome
non-procedural Web forms! SQL Advantage:
– Can specify query “declaratively” and let database system figure out best way of finding answers
– Supports queries of medium complexity Specialized languages
More complex queries (e.g. data mining such as classification and clustering) implemented in procedural language, with SQL acting as interface to database
S. Sudarshan, IIT Bombay17
Problems of DiversityProblems of Diversity
Many different databases Multiple databases for each of genome, proteome, transcriptome,
metabolome (and perhaps any other *ome you choose to add!)
Need to cross-reference between these databases
Need an ontology to ensure consistent and unique names
Instability Names, data, even models keep changing
Modeling secondary information Annotations, typically text based
S. Sudarshan, IIT Bombay18
Problems in QueryingProblems in Querying
Querying What query languages to use? (AceDB (SGD), Icarus (SRS), SQL?)
OO API (Corba based interfaces proposed by OMG/EMBL)
Querying and text mining on annotations
Queries that combine multiple databases and paradigms E.g. genome, proteome and annotations (text data)
Browsing and visualization Generate hyperlinks in data automatically for browsing
Visualization for sequence data, protein structures, to depict correlations, etc
S. Sudarshan, IIT Bombay19
Problems of Scale and DistributionProblems of Scale and Distribution
Problems of scale Genome: hundreds of gigabytes to terabytes (1012 bytes)
Transcriptome (Microarray):
Each chip has 10,000 measurements + image
Millions of experiments
– on different species/individuals/cells/conditions …
– Total: 1 petabyte/annum (1015 bytes)
Bottom line: too big to hold everything locally
Ideally: provide integrated view of all data, and fetch actual data on demand
Limited access patterns Can usually access data only via predefined Web forms
S. Sudarshan, IIT Bombay20
Problems of Database RepresentationProblems of Database Representation
Efficiency and flexibility of use are often at odds E.g. the Expression-Value table in our schema can be huge
Array representation may be better but less convenient for users Alternative: use one attribute for each gene
– no database efficiently supports relations with thousands of attributes
– But this is natural to lay users Similarly: user may want one relation for each of millions of
experiments
Ideal: flexible view combined with efficient implementation
underneath, plus query languages that offer metadata capabilities
E.g. “for all relations whose name is in table N”
S. Sudarshan, IIT Bombay21
ReferencesReferences
Online information Heaps and heaps of sites, many with actual data
freely available data may be worth what you paid for it!
Tutorial on Information Management for Genome Level Bioinformatics, Paton and Goble, at VLDB 2001: http://www.dia.uniroma3.it/~vldbproc/#tut
European Molecular Biology Network http://www.embnet.org/
Univ. Manchester site (with relational version of Microarray data representation, and links to other sites)
http://www.bioinf.man.ac.uk
Database textbook with absolutely no bioinformatics coverage (shameless sales pitch ):
Database System Concepts 4th Ed by Silberschatz, Korth and Sudarshan (should come out in Indian edition in a few months)
End of TalkEnd of Talk
S. Sudarshan, IIT Bombay23
Relational Schema Design ProblemsRelational Schema Design Problems Many flat file formats have lots of columns:
E.g. Drug-effect
Drug1 Drug2 Drug3 … Drug-n Cancer1 Cancer2
Cancer3
….
Cancer-m
Beware: Such structures are nice for humans to read (are called crosstabs),
BUT Most databases cannot support relations with many columns! And querying data with such columns is more complicated
Solution: use a schema drug-effect(cancer-type, drug, effect)
Alternative solution: use arrays to represent some such information (supported by some databases)
S. Sudarshan, IIT Bombay24
Relational Schema Design Problems (Cont.)Relational Schema Design Problems (Cont.)
Another common mistake: having many relations with same attributes E.g. one relation for each cancer type, or one relation for each drug
Cancer1(…), Cancer2(…), …, Cancer-n(…)
Most databases can handle only hundreds or a few thousand relations efficiently
Querying becomes more complicated when there are many relations
Solution: Replace many relations with same attributes by a single relation with the same attributes, plus an extra attribute storing the name Cancer(Type, …)
S. Sudarshan, IIT Bombay25
Alternative E-R NotationsAlternative E-R Notations
Crow’s feet notation: Total participation (each entity participates in at least one relationship) is indicated by an extra bar
R1 R2
S. Sudarshan, IIT Bombay26
E-R Diagram For Our ExampleE-R Diagram For Our Example
Experimenter
Sample
Experiment
Array
Experiment-Id Image
Date
Image
Experimenter-Id Name
Dept.
Institution
Sample-Id
Cell Type
Organism
Drugs
Array-Id
Manufacturer
Type
Batch
Expt-Sample
Expt-Array
Expt-Exptr
GeneExpression-Value
Value Gene-Id
S. Sudarshan, IIT Bombay27
Relational Schema Design PrinciplesRelational Schema Design Principles
Redundancy E.g. Array-genes(.., fragment-seq, gene-seq, gene-mutations, …)
is better represented as
– Array-genes( fragment-seq, gene-id)
– Gene(gene-id, gene-seq, gene-mutations)
Otherwise data is replicated unnecessarity
– I.e. mutation information is stored multiple times
Redundancy can be useful for better query performance, but should be used in a thought-out manner, not by accident
Inability to express information E.g. if a gene is not stored in Array-genes we cannot store its
mutation information
S. Sudarshan, IIT Bombay28
Basic SQL QueriesBasic SQL Queries
Find the image for experiment number 1345
select imagefrom experimentwhere experiment-id = 1345
Find the experiment-id and image of all experiments involving e-coli
select experiment-id, imagefrom experiment, samplewhere experiment.sample-id = sample.sample-id
and sample.organism = ‘e-coli’ All combinations of rows from the relations in the from clause are
considered, and those that satisfy the where conditions are output
S. Sudarshan, IIT Bombay29
Complex Queries and ViewsComplex Queries and Views
A view consisting of experiments with number of active genes
create view expt-active-genes asselect experiment-id, count (gene-id) as active-
cnt from experiment, expression-valuewhere expression-value.experiment-Id =
experiment.experiment-Id and value > 2
group by branch-name
Find number of active genes in experiment E-123select active-cntfrom expt-active-geneswhere expirement-Id = ‘E-123’
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