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1. http://creativecommons.org/licenses/by-sa/2.0/

2. Bioinformatics Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website:http://web.udl.es/usuaris/pg193845/testsite/ Course Website:http://web.udl.es/usuaris/pg193845/Bioinformatics_2009/

3. Language of the course • Mine: English • Slides: English • Webpage: English • Yours: Whichever you choose as long as I understand it. ALWAYS ASK WHEN YOU DON’T UNDERSTAND SOMETHING!!

4. Web Page of the course http://web.udl.es/usuaris/pg193845/Bioinformatics_2009/ • There, you will find all the information about your tasks, links to bioinformatics resources, and the lecture

5. Goals of this course • Give you an integrated view of how to use computers and informatics to gain a systemic understanding of biological systems at the molecular level. • Integrate bioinformatics, mathematical modelling and other areas of computational biology to save lab work and address problems that can not yet be solved at the lab.

6. Course Plan • First part of the course (2 weeks): Broad introduction to bioinformatics and computational biology in molecular biology. • Second part of the course: Problems for you to solve in group at home, + in-depth lectures about the different subjects you need to solve the problems.

7. Evaluation Plan • 5 tasks in groups of four. At the end of each task you deliver a paper as a group. (overall, all tasks will account for 50% of final grade). • Final paper presenting the whole story together (20%). • Individual discussion of the final paper with me (20%). • Class participation (10%). • CAUTION: YOU NEED TO HAVE AT LEAST 5/10 IN EACH TASK, IN THE FINAL PAPER AND IN THE DISCUSSION.

8. Index • Why bioinformatics? • Ontologies & Classification schemes • Databases and servers

9. Why Bioinformatics? Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website:http://web.udl.es/usuaris/pg193845/testsite/

10. What obvious problems do large scale sets create? • Imagine the 6 500 000 000 human beings born within the last 130 years and still alive. • By and large a majority of them has had and education. • What problems need solving to ensure that education? 1 – Organize Knowledge 2 – Organize its transmission Knowledge

11. First problem: organizing knowledge • We do not need to know all there is to know in order to be productive in society • Furthermore we can not learn everything at the same time. • Problem: How to organize knowledge into bite-sized packages that can be consecutively parceled out, and from which one can build upon?

12. Organizing knowledge Communication (Read, write, count) Humanities Sciences …

13. Second problem: organizing the transmission of knowledge • The school system is a way in which the most people can be trained with the least societal effort Not effective

14. School and Books are the servers and databases of educating people Database New Server: You Server Users

15. Understanding biological systems WewereWRONG!!!!! I need more data!!! How do I plan whatto do now? Hey, it’sraining!!! Whydon’twe try and figure outhowallthelittle molecular pieces in a cellworktogether?!?!?!

16. The “omics” revolution in molecular biology • Over many decades, a huge amount of biological data has accumulated. • Unlike the “KNOWLEDGE” we discussed before, this data is not well organized and the connections between the different parcels of data are obscure. • The omics revolution has compounded this problem 1000 fold because data now accumulates faster than ever.

17. What is the “omics” revolution in molecular biology? • The omics revolution is a period of about ten years in which several different technologies that can be applied to study the complement molecular landscape of cells!!! • Genomics • Proteomics • Metabolomics • Et caeteromics

18. The “omics” revolution in molecular biology • (We!!) Biologists want the data to make sense and they want it now!!!

19. Understanding biological systems I need more data!!! Whydon’ttheygiveitto me

20. Comparison between the two problems PeopleorganizedtheKnowledgetransmissionsystem and itsconnectionsovermilenia of trial and error. Itisimpossibleforpeopletoorganizethebiologicalknowledgebroughtaboutbyomics in the 10 yearsthathavepassedsincethebeginning of theomics era.

21. Why? • Data is not well classified. • Data is not well connected. • Data is not well understood. • Not enough people to do it in a short amount of time.

22. New types of servers and databases are required for very fast organization and data mining Database Server Users BIOINFORMATICS!!

23. What is Bioinformatics? • Development and application of computational/informatic tools to the solution of biological problems • The Standard of internet Bioinformatics: LAM P E R L I N U X P A C H E Y S Q L H P Y T H O N Operating system Programing language(s) Internet server Database server

24. The standards are changing • JAVA facilitates that the servers launch a smaller number of processes by using the client’s machines for calculus and allowing for a larger number of simultaneous connections. • TOMCAT “talks” very well with JAVA. LTM J A V A I N U X O M C A T Y S Q L Operating system Programing language(s) Internet server Database server

25. What does a computer need to be effective? • Well classified data • Ontologies, Classification schemes • Well organized data • Databases, servers • Good users

26. Index • Why bioinformatics? • Ontologies & Classification schemes • Databases and servers

27. Ontologies and classification schemes for data Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website:http://web.udl.es/usuaris/pg193845/testsite/

28. Biological Classification Schemes • What is an Ontology (in the Biological sense)? A set of definitions of controlled vocabularies with hierarchical relationships to one another, that can easily be dealt with by computers

29. What are Bio-Ontologies? Biological Ontologies (Bio-ontologies) can be defined as a complex hierarchical structure in which biological concepts are described by their meanings (definitions) and relationships to each other. There are many Bio-Ontologies available and in use by databases. The Plant Ontology, along with other ontologies such as the Gene Ontology, are included in the open source Open Biological Ontologies project at Sourceforge. http://obofoundry.org/

30. The Gene Ontology The most well-known example of a bio-ontology is the Gene Ontology (GO; http://www.geneontology.org) which describes three biological domains: cellular component (where the gene product locates), molecular function (what the gene product does) and biological process (the cellular, developmental or physiological events the gene product is involved in). GO are used to describe gene products. Because these descriptions are independent of species-specific nomenclature and uniformly applied, it is possible to make meaningful and efficient comparisons of genes across diverse taxa.

31. Three “Super Categories of GO • Molecular Function (what) • Tasks performed at the molecular level • Biological Process (why) • How it pertains to the organism • Cellular Component (where) • Its location

32. Example • Gene Name: BRCA1 • Molecular Function: protein binding • Biological Process: DNA Replication and Chromosome Cycle • Cellular Component: nucleus

33. Structure of GO • How to define the relationship between concepts? • Example: How to relate the terms: “cell” “nucleus” “membrane”

34. How is GO Annotated? • Manual • Humans sifting through primary literature • Electronic • Assign GO Terms using already existing information in databases.

35. Evidence Code for GO Annotation IEA Inferred from Electronic Annotation ISS Inferred from Sequence Similarity IEP Inferred from Expression Pattern IMP Inferred from Mutant Phenotype IGI Inferred from Genetic Interaction IPI Inferred from Physical Interaction IDA Inferred from Direct Assay RCA Inferred from Reviewed Computational Analysis TAS Traceable Author Statement NAS Non-traceable Author Statement IC Inferred by Curator ND No biological Data available Detailed info available from: http://www.geneontology.org/doc/GO.evidence.html

36. How to use GO in data analysis • Simple Queries • Find over-represented GO categories in a list of genes • Search Biological “Themes” • Binning • Obtain a broad view of the distribution of major GO terms in a list of genes. • Clustering Genes on GO terms • Group together functionally related genes based on GO terms.

37. GO Tools • NetFlix – Get GO Annotation • AmiGO – Browser and Simple Queries • GoTermMapper – Binning(Go Slim) • GeneToolBox – • Finding over-represented GO categories • Clustering based on similar GO terms • Query for Gene with Similar Function.

38. GO is not very good • EC numbers • Protein classification schemes • TF classification schemes • Transport proteins classification schemes • Etc.

39. The EC number database

40. The BRENDA database

41. The TF classification database

42. The signal transduction classification database

43. The transport proteins classification database All these classifications are reminiscente of the Dewey classification system for books!!!! (Remember public libraries?)

44. A general protein classification database

45. What does a computer need to be effective? • Well classified data • Ontologies, Classification schemes • Well organized data • Databases, servers

46. Index • Why bioinformatics? • Ontologies & Classification schemes • Databases and servers

47. Databases & Servers Prof:Rui Alves ralves@cmb.udl.es 973702406 Dept Ciencies Mediques Basiques, 1st Floor, Room 1.08 Website:http://web.udl.es/usuaris/pg193845/testsite/

48. What is a Database? • A database is a collection of data organized in such a way that it is easy to store in a computer and to mine by appropriate software • A database is usually organized as a set of tables in which information about an object is stored • The tables are related to each other in different ways.

49. What does database technology allow? • Making information useful • Avoiding "accidental disorganisation” • Making information easily accessible and integrated with the rest of our work

50. S(tructured)Q(uery)L(anguange) • ANSI (American National Standards Institute) standard computer language for accessing and manipulating database systems. • SQL statements are used to retrieve and update data in a database. • Includes: • Data Manipulation Language (DML) • Data Definition Language (DDL)