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
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 database management system (DBMS)
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)
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
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
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
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
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
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; introduced commercially in 1979 by Oracle Corp. Developed by IBM in 1974; introduced commercially in 1979 by Oracle Corp. Standard interactive and programming language for getting information from and updating a database. Standard interactive and programming language for getting information from and updating a database. RDMS (SQL), ODBMS (Java, C++, OQL etc) RDMS (SQL), ODBMS (Java, C++, OQL etc)
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 (A,B,C) An intricate network for combining and sharing information Administrators praise fast network technologies!!! Users praise the internet!!!
Data warehouse Periodically, one imports data from databases and store it (locally) in the data warehouse. Now a local database can be created, containing for instance protein family data (sequence, structure, function and pathway/process data integrated with the gene expression and other experimental data). Disadvantage: expensive, intensive, needs to be updated. Advantage: easy control of integrated data-mining pipeline.
Three main reasons Database proliferation Dozens to hundreds at the moment Dozens to hundreds at the moment More and more scientific discoveries result from inter-database analysis and mining Rising complexity of required data- combinations E.g. translational medicine: “from bench to bedside” (genomic data vs. clinical data) E.g. translational medicine: “from bench to bedside” (genomic data vs. clinical data)
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)
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/
3D Macromolecular structural data Data originates from NMR or X-ray crystallography techniques Total n o of structures 34.626 (17/01/2006) If the 3D structure of a protein is solved... they have it PDB
SCOP Structural Classification Of Proteins 3D Macromolecular structural data grouped based on structural classification Data originates from the PDB Current version (v1.69) 25973 PDB Entries (July 2005). 70859 Domains
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.
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 topology families and homologous superfamilies are made by sequence and structure comparisons.
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 (standardized) structure elements A similar example is STRIDE
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
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.
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 Error propagation resulting from computer annotation
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 Review-suggestion: “Integrating biological databases”, Stein, Nature 2003
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