Introduction to Bioinformatics

1. Introduction to Bioinformatics Lecturer: Prof. Yael Mandel-Gutfreund Teaching Assistance: Rachelly Normand Alona Rabner Leon Anavi Course web site : http://webcourse.cs.technion.ac.il/236523

2. What is Bioinformatics?

3. Course Objectives • To introduce the bioinfomatics discipline • Tomake the students familiar with the major biological questions which can be addressed by bioinformatics tools • To introduce the major tools used in the field

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. • 4/4 homework assignments will be submitted+ Project proposal 2. A final project will be conducted in pairs * Project will be presented as a poster –poster day 21.3

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

6. What is Bioinformatics?

7. 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.

8. Information explosion in biology Human genome length =3*109

9. The revolution in molecular biology High Throughput Technologies • -Next Generation DNA sequencing > Whole genomes sequencing • -Metagenomics >Sequencing DNA from the • environment • -Microbiome Analysis > Sequencing microbial • communities • - RNA sequencing > RNA expression analysis • - Chip-seq > Protein-DNA interactions • - CLIP-seq > Protein-RNA interactions • - Mass Spectrometry > Protein Expression analysis

10. The huge amount of biological data resulting from novel technologies requires sophisticated computational tools Thinking computationally about biological process may lead to more accurate models, which in turn can be used to improve the design of algorithms Navlakha an Bar-Joseph 2011

11. Building models from parts lists Lazebnik, Cancer Cell, 2002

12. Building models from parts lists

13. What do we do in Bioinformatics? • - Analyze and interpret the various types of biological data: • Genomic Sequences (DNA) • Transcriptomic Sequences (RNA) • Proteomic sequences (Proteins) • Protein Structures (Proteins) • RNA structure (RNA) • Develop new algorithms and tools • To assess the biological information, • Handel large datasets, • find relationships between data sources etc…

14. What of all this will we learn inthe course? > Pairwise and multiple alignment > Database search > Protein alignments > DNA Sequencing > Gene expression/ Clustering analysis > Phylogenetic analysis > Motif search > Structural bioinformatics (RNA and proteins) > Biological networks

15. Manny different applications.. • Basic Science • Main Goal: Understand the living cell • Find the function of a new protein • Find the genes/proteins that are unique to human • Medical applications • - Identify the mutations (SNPs) that cause genetic diseases • - Diagnosis ..find the features that characterize disease states • - Find and develop new and better drugs • ….. • Agriculture applications • Higher yield crop • Increase shelf life • ……

16. Basic Science Find the function of a new protein -Database search

17. Discover Function of a new protein

18. Basic Science Find the genes/proteins that are unique to human -Phylogenetic analysis

19. 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% How can we be so similar--and yet so different?

20. Where are we different ?? Where are we similar ??? VERY SIMAILAR Conserved between many organisms VERY DIFFERENT

21. Medical applications Identify the mutations (SNPs) that cause genetic diseases -Pairwise and multiple alignments -DNA sequencing

22. Sickle Cell Anemia Due to 1 swapping of an A for a T

23. 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

24. 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

25. Medical Applications Diagnosis ..find the features that characterize disease states -Gene expression/clustering analysis -Motif search

26. Samples were taken from patients with adenocarcinoma. hundreds of genes that differentiate between cancer tissues in different stages of the tumor were found. The arrow shows an example of a tumor cells which were not detected correctly by histological or other clinical parameters. Ramaswamy et al, 2003 Nat Genet 33:49-54

27. Medical applications Find and develop new and better drugs -DNA sequencing -Gene expression -Structural Bioinformatics -Biological networks

28. Bioinformatics can dramatically reduce the cost and time for developing a new drug Discovery VALIDATION Clinical trials Approval Pre-discovery Billions of $ EnvisagenicsInc. Putative drug-target candidates

29. Bioinformatics can dramatically reduce the cost and time for developing a new drug Discovery VALIDATION Clinical trials Approval Pre-discovery Millions of $ Good putative drug-target candidates EnvisagenicsInc. Putative drug-target candidates

30. Kafka (1883-1924) Orwell (1903-1950) Keats (1795-1821) Mozart (1756-1791) Schubert (1797-1828) Chopin (1810-1849)

31. Did you know? • Infectious diseases are still number 1 cause of premature death • (0-44 years of age) worldwide. • Annually kill >13 million people • (~33% of all deaths)

32. The ribosome is a target for approximately half of antibiotics characterized to date Antibiotics targets of the large ribosomal subunit

33. Using bioinformatics to find new target sites on the ribosome Bad site Good site

34. Manny different applications.. And beyond… Personalized medicine

36. How can bioinformatics contribute to Medicine? http://www.tedmed.com/talks/show?id=17961 MAKE THE DATA WORK FOR US