1. Structure Databases DNA/Protein structure-function analysis and prediction Lecture 6 Bioinformatics Section, Vrije Universiteit, Amsterdam

2. The dictionary definition Main Entry: da·ta·base Pronunciation: 'dA-t&-"bAs, 'da- also 'dä- Function: noun Date: circa 1962 Main Entry: da·ta·base Pronunciation: 'dA-t&-"bAs, 'da- also 'dä- Function: noun Date: circa 1962 : a usually large collection of data organized especially for rapid search and retrieval (as by a computer) : a usually large collection of data organized especially for rapid search and retrieval (as by a computer) - Webster dictionary

3. WHAT is a database? A collection of data that needs to be: Structured Structured Searchable Searchable Updated (periodically) Updated (periodically) Cross referenced Cross referencedChallenge: To change “meaningless” data into useful information that can be accessed and analysed the best way possible. To change “meaningless” data into useful information that can be accessed and analysed the best way possible. For example: HOW would YOU organise all biological sequences so that the biological information is optimally accessible? You need an appropriate data management system (DBMS)

4. DBMS Internal organization Controls speed and flexibility Controls speed and flexibility A unity of programs that Store Store Extract Extract Modify Modify Database StoreExtractModify USER(S)

5. DBMS organisation types Flat file databases (flat DBMS) Simple, restrictive, table Simple, restrictive, table Hierarchical databases (hierarchical DBMS) Simple, restrictive, tables Simple, restrictive, tables Relational databases (RDBMS) Complex,versatile, tables Complex,versatile, tables Object-oriented databases (ODBMS) Complex, versatile, objects Complex, versatile, objects

6. Relational databases Data is stored in multiple related tables Data relationships across tables can be either many-to-one or many-to-many A few rules allow the database to be viewed in many ways Lets convert the “course details” to a relational database

7. Student 1 Chemistry Biology A B B A C ….. Student 2 Ecology Maths A D A A A …...... Course details FLAT DATABASE 2 Student 2 Ecology Biology A B A A A ….. Student 1 Chemistry English A A A A A …...... Name Depart. Course E1 E2 E3 P1 P2 Name Depart. Course E1 E2 E3 P1 P2 Student 1 Chemistry Maths C C B A A ….. Our flat file database

8. Normalize (1NF) … We remove repeating records (rows) sID Name dID 1 Student1 1 2 Student2 2 cID Course 1 Biology 2 Maths 3 English dID Department 1 Chemistry 2 Ecology 1 1 A B B A C ….. 1 1 A B B A C ….. 2 2 A D A A A ….. 2 2 A D A A A …...... 2 1 A B A A A ….. 2 1 A B A A A ….. 1 3 A A A A A ….. 1 3 A A A A A …...... sID cID E1 E2 E3 P1 P2 sID cID E1 E2 E3 P1 P2 1 2 C C B A A ….. 1 2 C C B A A ….. Primary keys Foreign keys

9. sID Name dID 1 Student1 1 2 Student2 2 cID Course 1 Biology 2 Maths 3 English gID Grade 1 A 2 B 3 C dID Department 1 Chemistry 2 Ecology wID Project 1 E1 2 E2 3 E3 4 P1 5 P2 sID cID gID wID 1 1 1 1 1 1 2 2 1 1 2 3 1 1 1 4 1 1 3 5 2 1 1 1 2 1 1 2 2 1 2 3 2 1 1 4 2 1 1 5... Normalize (2NF) … We remove redundant fields (columns)

10. Relational Databases What have we achieved? No repeating information No repeating information Less storage space Less storage space Better reality representation Better reality representation Easy modification/management Easy modification/management Easy usage of any combination of records Easy usage of any combination of recordsRemember the DBMS has programs to access and edit this information so ignore the human reading limitation of the primary keys

11. Accessing database information A request for data from a database is called a query Queries can be of three forms: Choose from a list of parameters Choose from a list of parameters Query by example (QBE) Query by example (QBE) Query language Query language

12. Query Languages The standard SQL (Structured Query Language) originally called SEQUEL (Structured English QUEry Language) SQL (Structured Query Language) originally called SEQUEL (Structured English QUEry Language) Developed by IBM in 1974 Developed by IBM in 1974 Introduced commercially in 1979 by Oracle Corp. Introduced commercially in 1979 by Oracle Corp. RDMS (SQL), ODBMS (Java, C++, OQL etc) RDMS (SQL), ODBMS (Java, C++, OQL etc)

13. Distributed databases From local to global attitude Data appears to be in one location but is most definitely not A definition: Two or more data files in different locations, periodically synchronized by the DBMS to keep data in all locations consistent An intricate network for combining and sharing information Administrators praise fast network technologies!!! Users praise the internet!!!

14. So why do biologists care?

15. Three main reasons Database proliferation Dozens to hundreds at the moment Dozens to hundreds at the moment In the next few years biological data analysis will be trifurcated Bio-webs : remote data analysis and mining Bio-webs : remote data analysis and mining Bio-grids : transparent high-end computing Bio-grids : transparent high-end computing Bio-semantic webs : biological knowledge Bio-semantic webs : biological knowledge More and more scientific discoveries result from inter-database analysis and mining

16. Biological databases Like any other database Data organization for optimal analysis Data organization for optimal analysis Data is of different types Raw data (DNA, RNA, protein sequences) Raw data (DNA, RNA, protein sequences) Curated data (DNA, RNA and protein annotated sequences and structures, expression data) Curated data (DNA, RNA and protein annotated sequences and structures, expression data)

17. Raw Biological data Nucleic Acids (DNA)

18. Raw Biological data Amino acid residues (proteins)

19. Curated Biological Data DNA, nucleotide sequencesProteins, residue sequences Gene boundaries, topology Gene structure Introns, exons, ORFs, splicing Expression data MCTUYTCUYFSTYRCCTYFSCD Extended sequence information Secondary structure Hydrophobicity, motif data

20. Curated Biological data 3D Structures, folds

21. Biological Databases The 2003 NAR Database Issue: http://nar.oupjournals.org/content/vol31/issue1/

22. Distributed information Pearson’s Law: The usefulness of a column of data varies as the square of the number of columns it is compared to.

23. A few biological databases Nucleotide Databases Alternative Splicing, EMBL-Bank, Ensembl, Genomes Server, Genome, MOT, EMBL-Align, Simple Queries, dbSTS Queries, Parasites, Mutations, IMGT Genome Databases Human, Mouse, Yeast, C.elegans, FLYBASE, Parasites Protein Databases Swiss-Prot, TrEMBL, InterPro, CluSTr, IPI, GOA, GO, Proteome Analysis, HPI, IntEnz, TrEMBLnew, SP_ML, NEWT, PANDIT Structure Databases PDB, MSD, FSSP, DALI Microarray Database ArrayExpress Literature Databases MEDLINE, Software Biocatalog, Flybase Archives Alignment Databases BAliBASE, Homstrad, FSSP

24. Structural Databases Protein Data Bank (PDB) http://www.rcsb.org/pdb/ Structural Classification of Proteins (SCOP) http://scop.berkeley.eduhttp://scop.mrc-lmb.cam.ac.uk/scop/

25. PDB 3D Macromolecular structural data Data originates from NMR or X-ray crystallography techniques Total n o of structures 29.326 (25/01/2005) If the 3D structure of a protein is solved... they have it

26. PDB content

27. PDB information The PDB files have a standard format Key features Informative descriptors

28. Lets give it a go on the WWW

29. SCOP Structural Classification Of Proteins 3D Macromolecular structural data grouped based on structural classification Data originates from the PDB Current version (v1.65) 20619 PDB Entries (1 August 2003). 54745 Domains

30. SCOP levels bottom-up 1.Family: Clear evolutionarily relationship Proteins clustered together into families are clearly evolutionarily related. Generally, this means that pairwise residue identities between the proteins are 30% and greater. However, in some cases similar functions and structures provide definitive evidence of common descent in the absence of high sequence identity; for example, many globins form a family though some members have sequence identities of only 15%. 2.Superfamily: Probable common evolutionary origin Proteins that have low sequence identities, but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies. For example, actin, the ATPase domain of the heat shock protein, and hexakinase together form a superfamily. 3.Fold: Major structural similarity Proteins are defined as having a common fold if they have the same major secondary structures in the same arrangement and with the same topological connections. Different proteins with the same fold often have peripheral elements of secondary structure and turn regions that differ in size and conformation. In some cases, these differing peripheral regions may comprise half the structure. Proteins placed together in the same fold category may not have a common evolutionary origin: the structural similarities could arise just from the physics and chemistry of proteins favouring certain packing arrangements and chain topologies.

31. Lets give it a go on the WWW

32. CATH Class, derived from secondary structure content, is assigned for more than 90% of protein structures automatically. Architecture, which describes the gross orientation of secondary structures, independent of connectivities, is currently assigned manually. Topology level clusters structures according to their toplogical connections and numbers of secondary structures. The Homologous superfamilies cluster proteins with highly similar structures and functions. The assignments of structures to toplogy families and homologous superfamilies are made by sequence and structure comparisons.

33. Lets give it a go on the WWW

34. DSSP Dictionary of secondary structure of proteins The DSSP database comprises the secondary structures of all PDB entries DSSP is actually software that translates the PDB structural co-ordinates into secondary structure elements A similar example is STRIDE

35. WHY bother??? Researchers create and use the data Use of known information for analyzing new data New data needs to be screened Structural/Functional information Extends the knowledge and information on a higher level than DNA or protein sequences

36. In the end …. Computers can figure out all kinds of problems, except the things in the world that just don't add up. James Magary We should add: For that we employ the human brain, experts and experience.

37. Bio-databases: A short word on problems Even today we face some key limitations There is no standard format There is no standard format Every database or program has its own format There is no standard nomenclature There is no standard nomenclature Every database has its own names Data is not fully optimized Data is not fully optimized Some datasets have missing information without indications of it Data errors Data errors Data is sometimes of poor quality, erroneous, misspelled

38. What to take home Databases are a collection of data Need to access and maintain easily and flexibly Need to access and maintain easily and flexibly Biological information is vast and sometimes very redundant Distributed databases bring it all together with quality controls, cross-referencing and standardization Computers can only create data, they do not give answers