1. Applied Bioinformatics Dr. Jens Allmer Week 1 (Introduction)

2. Your Instructor Education –BSc: University of Münster 1996 –MSc: University of Münster 2002 –PhD: University of Münster 2006 Worked at –Izmir Institute of Technology (since 2008) –Izmir University of Economics, Turkey (Feb 2007 – Aug 2008) –University of Muenster, Germany (Jan 2006 – Feb 2007) –University of Pennsylvania, USA (Jan 2004 – Dec 2005) –University of Jena, Germany (Nov 2002 – Dec 2003)

3. Areas of Interest Bioinformatics –Sequences –Alignments Mass Spectrometry –De novo sequencing –Pattern matching Annotation –Integration –Automatic assessments General Automation and Productivity

4. Course Rules Attendance –Is essential and will be monitored strictly –if(absence > 12h) Then NA; Make-up Work –None

5. Course Rules Lecture starts on time –if late enter QUIETLY –if more then 5 min late DO NOT ENTER wait for break Breaks are 10 min max –if late after break enter QUIETLY –if more then 5 min late DO NOT ENTER wait for next break Early leave –Announce before course and leave if granted

6. Course Rules Project –Parts to be performed published on the website and/or as slides –Deadline 6pm on the day before the next class (you may submit early of course) –No extention –No make-up –No extra work Must be electronicly submitted to: jensallmer.iyte@analysis.urkund.com jensallmer.iyte@analysis.urkund.com –Must be named ????_first_last.eee or will not be accepted –Formats include: doc, ppt, odx, txt, html,... –Not allowed are formats that may not be edited by me like pdf, and similar formats that are not widespread –Must be significantly different from your classmates –Otherwise everyone involved will obtain zero for that assignment

7. Grading All information available on class website Grading individualized –Quizzes15% –Mind Maps10% –Midterm 125% –Midterm 225% –Project25%

8. Project Group Formation 0%(08.10. 18:00) –Group Size: 4 First Draft 25%(22.10. 18:00) Results 15%(19.11. 18:00) Second Draft 20%(03.12. 18:00) Presentation 10%(25.12. 18:00) Final Version 25%(31.12. 18:00)

9. Grading I am responsible to evaluate you –I am not responsible to pass everyone or give great grades Make it easy for me 1.Show up and participate 2.Do homeworks and pre-course preparations 3.Midterm and Final will be easy for you if you adhere to 1. and 2.

10. Course Structure –Start –10 min quiz –35 min lecture – 5 min mind mapping –10 min break –50 min practice –10 min break –40-50 min lecture –10 min break –30 min practice

11. Textbooks Primary audience Junior bio majors Course home page: http://www.biolnk.com/habf ISBN: 978-605-133-297-0 http://www.idefix.com/kitap/biyoenformatik-1-dizi-kiyaslamalari- jens-allmer/tanim.asp?sid=GUFFOI44R7FJ9CIR6STU

12. Textbooks Everything you currently need to know about Applied Bioinformatics in regard to practical problems you will encounter during everyday research.

13. Mathematics Statistics Computer Science Informatics Biology Molecular biology Medicine Chemistry Physics Bioinformatics

14. Bioinformatics is Multidisciplinary Computer Science Math Statistics Structural Biology Phylogenetics Drug Design Genomics Molecular Life Sciences

15. The Pyramid of Life (2000) 30,000 Genes 3,000 Enzymes 1400 Chemicals Metabolomics Proteomics Genomics B I O I N F O R M A T I C S

16. The Pyramid of Life 100,000 Proteins 30,000 Genes 1400 Chemicals Protein Interactions?

17. Bioinformatics (or Computational Biology) Not just the study of DNA or protein sequence data Inclusive definition – concerns the storage, display, reduction, management, analysis, extraction, simulation, modeling, fitting or prediction of biological, medical or pharmaceutical data

18. Basis of molecular life sciences Hierarchy of relationships (some exceptions): Genome Gene 1Gene 3Gene 2Gene X Protein 1Protein 2Protein 3Protein X Function 1Function 2Function 3Function X

19. How can one use bioinformatics to link diseases to genes? Positional cloning of genes 1.Find genetic markers associated with disease 2.Sequence DNA next to the markers 3.Compare DNA from afflicted individuals to DNA of normal individuals (database) 4.Find abnormalities 5.Predict gene function from sequence information Disease Map Gene Function

20. Bioinformatics in the old days Close to Molecular Biology: –(Statistical) analysis of protein and nucleotide structure –Protein folding problem –Protein-protein and protein-nucleotide interaction Many essential methods were created early on –Protein sequence analysis (pairwise and multiple alignment) –Protein structure prediction (secondary, tertiary structure) Evolution was studied and methods created –Phylogenetic reconstruction (clustering – e.g., Neighbor Joining (NJ) method) –Nowadays also part of Datamining

21. But then the big bang….

22. The Human Genome - 26 June 2000 Dr. Craig Venter Celera Genomics -- Shotgun method Francis Collins (USA)/Sir John Sulston (UK) Human Genome Project

23. Human DNA There are at least 3bn (3  10 9 ) nucleotides in the nucleus of almost all of the trillions (3.2  10 12 ) of cells of a human body (an exception is, for example, red blood cells which have no nucleus and therefore no DNA) – a total of ~10 22 nucleotides! Many DNA regions code for proteins, and are called genes (1 gene codes for 1 protein as a base rule, but the reality is a lot more complicated) –Name examples Human DNA may contain ~27,000 expressed genes –Problems? Deoxyribonucleic acid (DNA) comprises 4 different types of nucleotides: adenine (A), thiamine (T), cytosine (C) and guanine (G). These nucleotides are sometimes also called bases –Ambiguities?

24. Y-Chromosome 50% of the sequence consists of NNNNNNNNNNNNNNNNNNNNNNNNNNNNNN Not very meaningful –Explanation.... Same as in x chromosome –What about the N’s in chr 1?

25. Human DNA (Cont.) All people are different but the DNA of different people only varies for 0.2% or less So, only up to 2 letters in 1000 are expected to be different. Evidence in current genomics studies (Single Nucleotide Polymorphisms or SNPs) imply that on average only 1 letter out of 1400 is different between individuals. Over the whole genome, this means that 2 to 3 million letters would differ between individuals.

26. Modern bioinformatics is closely associated with genomics The aim is to solve the genomics information problem Ultimately, this should lead to biological understanding how all parts fit (DNA, RNA, proteins, metabolites) and how they interact (gene regulation, gene expression, protein interaction, metabolic pathways, protein signaling, etc.)

27. TERTIARY STRUCTURE (fold) Genome Expressome Proteome Metabolome Functional Genomics From gene to function Interactome?

28. Unknown Function How much of the genome is defined?

29. What is bioinformatics? E.g. Process the spots on a microarray, determine which genes are differentially expressed, link spots to sequence via a database, analyze the sequence using predictive tools, link the genes to related genes to form a network Comp sci Bio Math Stats Machine learning Database systems Data mining Image processing Modeling Graph theory Statistical analysis Sequence Structure Interactions Regulation Genomes Evolution PhysicsEnglish Bioinformatics Chem

30. What is a bioinformatician? Somebody who knows everything

31. What is a bioinformatician? facilitatorA facilitator –Typically has background in biology or CS, but is comfortable with concepts from other disciplines –Bring together ideas (or researchers) from different domains to solve a biological problem Conceptualize the problem –Use language appropriate to the domain Identify potential solutions –Understanding of different fields helps to identify possible approaches at a broad level Guide the development process –Create in-house or find potential collaborators to work on approaches in-depth Integrate results into overall solution –Software/method, results of biological analysis

32. How is Bioinformatics Used? Experimental proof is still the “Gold Standard”. Bioinformatics isn’t going to replace lab work anytime soon Bioinformatics is used to help “focus” the scientist on the bench top experiments

33. Bioinformatics Is application of computational tools in Biology Bioinformatics? Not really! In this course we will however only go into algorithmic details rarely (like today ;)

35. Mind Mapping Have you ever studied a subject or brainstormed an idea, only to find yourself with pages of information, but no clear view of how pieces fit together?  Mind mapping –Learn more effectively –Improves memorization –Enhances creativity –Speeds up analyses –Gives structure to complex ideas –Records information for future use Source: http://www.mindtools.com/pages/article/newISS_01.htm

36. An Example Mind Map for MicroRNAs

37. How to Mind Map 1.Identify the central topic write in center 2.Write major parts of the topic on lines in all directions 3.Repeat 2. with ever finer level of detail until satisfied Source: http://www.mindtools.com/pages/article/newISS_01.htm

38. Note Taking with Mind Maps Capture ideas organized into topics –What if the central topic which I chose is not the central topic? –Make a new mind map which captures the topic correctly Uses Cases –Note taking in class –Recapitulization after lecture –Analysis of a new topic –Structuring of any intended writing When –During acquisition of new knowledge (faster than writing) –For review 5m, 1h, 6h, 1d, 7d, 1m after note taking

39. Mind Mapping Tips 1.Use single words or very short phrases 2.Write clearly and readable 3.Use color! 4.Seperate ideas (color, lines, shading) 5.Draw symbols and images 6.Draw links among elements

40. A More Elaborate Mind Map Source: http://www.mindtools.com/pages/article/newISS_01.htm

41. At the Heart of Bioinformatics >scaffold_1152 GGTGCGGCCGTCCTCCAGCTGCTTGCCGGCGAAGATCAGGCGCTGCTGGT CCGGGGGGATGCCTGCATCCGGTGAGGAAACGCTCGTGTCAGACAAAGTG GGTGGGCGCAGGAAGCAGCAATCAACACAGCCCAGTGCAGCTGCAAAGCG CCCGCCTTACCACTGACCCGCCTGGCCACCCACCCCTACCCCCCGTAAGG AAAGAGCCCCGACTCACCCTCCTTGTCCTGAATCTTGGCCTTCACGTTCT CAATGGTGTCCGAAGACTCCACCTCGAGCGTGATGGTCTTGCCCGTCAGG GTCTTGACGAAGATCTGCATGCCACCGCGCAGGCGCAGCACCAGGTGCAG … Genomic >RF1_scaffold_1152 GAAVLQLLAGEDQALLVRGDACIR$GNARVRQSGWAQEAAINTAQCSC KAPALPLTRLATHPYPP$GKSPDSPSLS$ILARDVAHDFAKSSPR$YA PLIPQNLRC$SIEMKQPASLLSPIGEGACASHLQCLEKCLLP$GAIVY MIS$GSGRR$TSWVGIGGCNDGTEKRSEVDSRRGGKGNIHD >RF2_scaffold_1152 VRPSSSCLPAKIRRCWSGGMPASGEETLVS AATAAKPQTWSPTAWEF KVGGRRKQQSTQPSAAAKRPPYH$PAWPPTPTPRKERAPTHPPCPESW SRSQWCPKTPPRA$WSCPSGS$RRSACHRAGAAPGAGSTPSGCCSQPG CGRPPAACRRRSGAAGPGGCLCVGGGGEGACASHLQCLEGE … Translated

42. Your Task You may only compare 1 character at a time You may create helpful structures You should find the location of the pattern in the Sequence with a minimal number of comparisons Try it for yourself ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Sequence Pattern Your Task You may only compare 1 character at a time You may create helpful structures You should find the location of the Pattern in the Sequence with a minimal number of comparisons

43. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 1

44. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 2

45. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 3

46. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 4

47. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 6

48. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 7-16

49. Brute Force Approach ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 17-22

50. Boyer-Moore Algorithm ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 1 Preprocessing Good suffix matrix (m+1) Bad character matrix (m+1)

51. Boyer-Moore Algorithm ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 2

52. Boyer-Moore Algorithm ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 3-7

53. Boyer-Moore Algorithm ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 8

54. Boyer-Moore Algorithm ACGGTAGTATGTGATGTATGATCGCGAAAGAGG TGATGT Comparisons: 9-15

55. Questions

56. Define Algorithm

58. Website http://mbg305.allmer.dehttp://mbg305.allmer.de Slides Homework Additional materials and challenges Grades

59. Website To see your grades you need to login Some material may need login as well Currently –UserID = StudentID –Password = StudentID Change now –UserID = working email address –Password = whatever you will remember

60. Login to mbg305.allmer.de We will now assist you to log in and to add your email address and change your password.

61. Assignments –Research about Mind Maps E.g.: http://en.wikipedia.org/wiki/Mind-maphttp://en.wikipedia.org/wiki/Mind-map IYTE library –Make sure to read the lecture notes for next week (Available online on Wednesday) –Read Chapters 1 and 2 from our textbook