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Data Representation in Bioinformatics

Data Representation in Bioinformatics. S. Sudarshan Computer Science and Engg. Dept. I.I.T. Bombay. Data 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

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Data Representation in Bioinformatics

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  1. Data Representation in Bioinformatics S. Sudarshan Computer Science and Engg. Dept. I.I.T. Bombay

  2. Data 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

  3. Data 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

  4. Genbank Example 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 TITLE Partial cDNA G.max calnexin homologue from P.vulgaris JOURNAL Unpublished (1999) FEATURES Location/Qualifiers source 1..300 /organism="Phaseolus vulgaris" /db_xref="taxon:3885" /clone_lib="Phaseolus vulgaris library (Watanabe T)"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//

  5. XML: Extensible Markup Language • Simple XML example • E.g. <faculty> <faculty-member facid=12349> <name> S.Sudarshan </name> <email> sudarsha@cse.iitb.ac.in</email> </faculty-member> <faculty-member facid=12987> <name> Pramod Wangikar</name> <email> pramodw@che.iitb.ac.in</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

  6. 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)

  7. 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> …….. …….

  8. Present 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

  9. What are Relations Attributes or columns Name E-mail Department Pramod Seshadri Uday Sudarshan pw@yahoo.com sesh@em.com uday@msn.com sud@iitb.ac.in Chem. Engg. Mech. Engg. Elec. Engg. Comp. Sci. Tuples or rows faculty

  10. Relational 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

  11. Entities 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

  12. Experimenter Experimenter-Id Name E-mail Dept. Institution Experiment Experiment-Id Date Image Gene gene-id sequence …… Expression-value Sample Sample-Id Organism Cell-type {Drug-Ids} value Array Array-Id Manufacturer Type Batch 1 * Example 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) Expt-Exptr Expt-Sample Notation Expt-Array Many-to-one

  13. Gene Gene-Id sequence Schema Diagrams for MicroArray Data • Schema diagrams show multiple relations and their interconnections • Lines link foreign key with referenced relation Experimenter Experimenter-Id Name E-mail Dept. Institution Experiment Experiment-Id Date Experimenter-Id Sample-Id Array-Id Image Sample Sample-Id Organism Cell-type {Drug-Ids} Multivalued attribute Expression-Value Experiment-Id Gene-Id value Array Array-Id Manufacturer Type Batch

  14. Modeling Protein Data (from Paton & Goble)

  15. Schema 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)

  16. Query 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

  17. Problems 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

  18. Problems 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

  19. Problems 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

  20. Problems 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”

  21. References • 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)

  22. End of Talk

  23. Relational 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)

  24. 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, …)

  25. Alternative 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

  26. Gene-Id Name Cell Type Manufacturer Organism Sample-Id Experiment-Id Date Image Batch Type Array-Id E-R Diagram For Our Example Value Gene Expression-Value E-mail Experimenter-Id Dept. Experimenter Image Experiment Expt-Exptr Institution Expt-Sample Drugs Expt-Array Array Sample

  27. Relational 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

  28. Basic 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, samplewhereexperiment.sample-id = sample.sample-id andsample.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

  29. Complex 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 > 2group by branch-name • Find number of active genes in experiment E-123 select active-cntfrom expt-active-geneswhere expirement-Id = ‘E-123’

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