Introduction to Bioinformatics

1. Introduction to Bioinformatics Lecturer: Prof. Yael Mandel-Gutfreund Teaching Assistance: Idit kosti Inbal Tal Edward Vitkin Course web site : http://webcourse.cs.technion.ac.il/236523

2. What is Bioinformatics?

3. Course Objectives • To introduce the bioinfomatics discipline • To make the students familiar with the major biological questions which can be addressed by bioinformatics tools • To introduce the major tools used for sequence and structure analysis and explainin general how they work (limitation etc..)

4. Course Structure and Requirements • Class Structure • 2 hours Lecture • 1 hour tutorial 2. Home work • Homework assignments will be given every second week • The homework will be done in pairs. • 5/5 homework assignments will be submitted 2. A final project will be conducted in pairs * Project will be presented as a poster –poster day 20.3

5. Grading • 20 % Homework assignments • 80 % final project (10% proposal, 20% supervisor evaluation 70% poster presentation)

6. Literature list • Gibas, C., Jambeck, P. Developing Bioinformatics Computer Skills. O'Reilly, 2001. • Lesk, A. M. Introduction to Bioinformatics. Oxford University Press, 2002. • Mount, D.W. Bioinformatics: Sequence and Genome Analysis. 2nd ed.,Cold Spring Harbor Laboratory Press, 2004. Advanced Reading Jones N.C & Pevzner P.A. An introduction to Bioinformatics algorithms MITPress, 2004

7. What is Bioinformatics?

8. What is Bioinformatics? “The field of science in which biology, computer science, and information technology merge to form a single discipline” Ultimate goal: to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.

9. 21ST centaury Genome Transcriptome Proteome Central Paradigm in Molecular Biology Gene (DNA) mRNA Protein

10. From DNA to Genome Watson and Crick DNA model 1955 1960 1965 1970 1975 1980 1985

11. 1990 First genome Hemophilus Influenzae 1995 Yeast genome First human genome draft 2000

12. Complete Genomes Total 1379 294 Eukaryotes 133 39 Bacteria 1152 235 Archaea 94 23 20102005

13. 1,000 Genomes Project: Expanding the Map of Human Genetics Researchers hope the effort will speed up the discovery of many diseases's genetic roots

14. 25000 genomes… What’s Next ? The “post-genomics” era Systems Biology Functional genomics Annotation Comparative genomics Main Goal: To understand the living cell

15. From ….25000 genomes To…Understanding living cells

16. Annotation CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATG CAA TAT GGA CAA TTG GTT TCT TCT CTG AAT ...... .............. TGAAAAACGTA

17. Identify the genes within a given sequence of DNA Identify the sites Which regulate the gene Annotation Predict the function

18. How do we identify a gene in a genome? A gene is characterized by several features (promoter, ORF…) some are easier and some harder to detect…

19. Using Bioinformatics approaches for Gene hunting Relative easy in simple organisms (e.g. bacteria) VERY HARD for higher organism (e.g. humans)

20. Comparative genomics

21. Perhaps not surprising!!! How humans are chimps? Comparison between the full drafts of the human and chimp genomes revealed that they differ only by 1.23%

22. So where are we different ?? Human ATAGCGGGGGGATGCGGGCCCTATACCC Chimp ATAGGGGGGATGCGGGCCCTATACCC Mouse ATAGCGGGATGCGGCGCTATACCA Human ATAGCGGGGGGATGCGGGCCCTATACCC Chimp ATAGGGG--GGATGCGGGCCCTATACCC Mouse ATAGCG---GGATGCGGCGC-TATACC-A

23. And where are we similar ??? VERY SIMAILAR Conserved between many organisms VERY DIFFERENT

24. Functional genomics

25. TO BE IS NOT ENOUGH In any time point a gene can be functional or not

26. From the gene expression pattern we can lean: What does the gene do ? When is it needed? What other genes or proteins interact with it? ….. What's wrong??

27. Systems Biology

28. Biological networks Jeong et al. Nature411, 41 - 42 (2001)

29. What can we learn from a network?

30. What can we learn from Biological Networks What can we learn about this protein • Is the protein essential for the organism ? • Is it a good drug targets?

31. What of all this will we learn in the course? The course will concentrate on the bioinformatics tools and databases which are used to : Annotate genes, Compare genes and genomes Infer the function of the genes and proteins Analyze the interactions between genes and proteins ETC….

32. Biological Databases The different types of data are collected in database • Sequence databases • Structural databases • Databases of Experimental Results All databases are connected

33. Sequence databases • Gene database • Genome database • Disease related mutation database • ………….

34. Genome Browsers Easy “walk” through the genome UCSC Genome Browserhttp://genome.ucsc.edu/

35. Disease related database

36. Sickle Cell Anemia • Due to 1 swapping an A for a T, causing inserted amino acid to be valine instead of glutamine in hemoglobin Image source: http://www.cc.nih.gov/ccc/ccnews/nov99/

37. Healthy Individual >gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

38. Diseased Individual >gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGTGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPVEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

39. Structure Databases • 3-dimensional structures of proteins, nucleic acids, molecular complexes etc • 3-d data is available due to techniques such as NMR and X-Ray crystallography

41. Databases of Experimental Results • Data such as experimental microarray images- gene expression data • Proteomic data- protein expression data • Metabolic pathways, protein-protein interaction data, regulatory networks • ETC………….

42. PubMed Literature Databases http://www.ncbi.nlm.nih.gov/pubmed/ Service of the National Library of Medicine

43. Putting it all Together • Each Database contains specific information • Like other biological systems also these databases are interrelated

44. PROTEIN PIR SWISS-PROT DISEASE LocusLink OMIM OMIA ASSEMBLED GENOMES GoldenPath WormBase TIGR MOTIFS BLOCKS Pfam Prosite GENOMIC DATA GenBank DDBJ EMBL ESTs dbEST unigene GENES RefSeq AllGenes GDB SNPs dbSNP GENE EXPRESSION Stanford MGDB NetAffx ArrayExpress PATHWAY KEGG COG STRUCTURE PDB MMDB SCOP LITERATURE PubMed