Introduction to Bioinformatics 234525-236523

1. Introduction to Bioinformatics234525-236523 Lecturer: Dr. Yael Mandel-Gutfreund Teaching Assistance: Martin Akerman Sivan Bercovici Course web site : http://webcourse.cs.technion.ac.il/234525

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 projects will be given every second week • The homework will be done in pairs. • 5/5 homework projects submitted 2. A final project will be conducted and submitted in pairs

5. Grading • 30 % Homework assignments • 70% final project

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. from purely lab-based science to an information science Bioinformatics Bio = Informatics

10. 21ST centaury Genome Transcriptome Proteome Central Paradigm in Molecular Biology Gene (DNA) mRNA Protein

11. Genome • Chromosomal DNA of an organism • Coding and non-coding DNA • Genome size and number of genes does not necessarily determine organism complexity

12. Transcriptome • Complete collection of all possible mRNAs (including splice variants) of an organism. • Regions of an organism’s genome that get transcribed into messenger RNA. • Transcriptome can be extended to include all transcribed elements, including non-coding RNAs used for structural and regulatory purposes.

13. Proteome • The complete collection of proteins that can be produced by an organism. • Can be studied either as static (sum of all proteins possible) or dynamic (all proteins found at a specific time point) entity

14. From DNA to Genome First protein sequence Watson and Crick DNA model 1955 1960 First protein structure 1965 1970 1975 1980 1985

15. 1990 First bacterial genome Hemophilus Influenzae 1995 Yeast genome First human genome draft 2000

16. Complete Genomes Total 706 456 Eukaryotes 78 43 Bacteria 578 383 Archaea 50 29 2008 2007

17. 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%

18. What’s Next ? The “post-genomics” era Annotation Comparative genomics Structural genomics Functional genomics Goal: to understand the living cell

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

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

21. promoter TF binding site Transcription Start Site Ribosome binding Site ORF=Open Reading Frame CDS=Coding Sequence CCTGACAAATTCGACGTGCGGCATTGCATGCAGACGTGCATG CGTGCAAATAATCAATGTGGACTTTTCTGCGATTATGGAAGAA CTTTGTTACGCGTTTTTGTCATGGCTTTGGTCCCGCTTTGTTC AGAATGCTTTTAATAAGCGGGGTTACCGGTTTGGTTAGCGAGA AGAGCCAGTAAAAGACGCAGTGACGGAGATGTCTGATGCAA TATGGACAATTGGTTTCTTCTCTGAAT ................................. ..............TGAAAAACGTA

22. Comparative genomics Human ATAGCGGGGGGATGCGGGCCCTATACCC Chimp ATAGGGG - - GGATGCGGGCCCTATACCC Mouse ATAGCG - - - GGATGCGGCGC -TATACCA

23. Researchers have learned a great deal about the function of human genes by examining their counterparts in simpler model organisms such as the mouse. Conservation of the IGFALS (Insulin-like growth factor) Between human and mouse.

24. Functional genomics

25. Understanding the function of genes and other parts of the genome

26. A network of interactions can be built For all proteins in an organism A large network of 8184 interactions among 4140 S. Cerevisiae proteins

27. Structural genomics

28. Assigning the structures of all proteins protein complexes Evolutionary relationship fold Biologic processes Protein-ligand complexes Shape and electrostatics Active sites Functional sites

29. Resources and Databases The different types of data are collected in database • Sequence databases • Structural databases • Databases of Experimental Results All databases are connected

30. Sequence databases • Gene database • Genome database • SNPs database • Disease related mutation database

31. Gene database • Give information into gene functionality • Alternative splicing of genes • Alternative pattern of exons included to create gene product • EST

32. Genome Databases • Data organized by species • Clones assembled into contigous pieces ‘contigs’ or whole chromosomes • Information on non-coding regions • Relativity

33. Genome Browsers • Annotation adds value to sequence • Easy “walk” through the genome • Comparative genomics

34. Genome Browsers • UCSC Genome Browserhttp://genome.ucsc.edu/ • Ensembl Genome Browser(http://www.ensembl.org) • WormBase:http://www.wormbase.org/ • AceDB:http://www.acedb.org/ • Comprehensive Microbial Resource:http://www.tigr.org/tigr-scripts/CMR2/CMRHomePage.spl • FlyBase:http://flybase.bio.indiana.edu/

35. SNP database Single Nucleotide Polymorphisms (SNPs) • Single base difference in a single position among two different individuals of the same species • Play an important role in differentiation and disease

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. Disease Databases • Genes are involved in disease • Many diseases are well studied • Description of diseases and what is known about them is stored

40. 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

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

43. PubMed Literature Databases • MEDLINE publication database • Over 17,000 journals • 15 million citations since 1950 http://www.ncbi.nlm.nih.giv/PubMed Service of the National Library of Medicine

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

45. 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