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Bioinformatics and Computer Science

Bioinformatics and Computer Science. Ina Koch TFH Berlin, Master‘s course Bioinformatics http://www.tfh-berlin.de/bi/. Cottbus, 8 th of October 2004. Outline. Introduction SNP analysis in the human genome Dynamic programming as basis for sequence comparison

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Bioinformatics and Computer Science

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  1. Bioinformatics and Computer Science Ina Koch TFH Berlin, Master‘s course Bioinformatics http://www.tfh-berlin.de/bi/ Cottbus, 8th of October 2004

  2. Outline • Introduction • SNP analysis in the human genome • Dynamic programming as basis for sequence comparison • Summary and outlook

  3. Bioinformatics-Computational Biology • Data collection and storage - data base techniques, integrative, data bases • Data visualisation - computer graphics, molecule graphics • MicroArray analysis – pattern recognition, statistics • Data analysis • sequence - string algorithms, dynamic programming • structure - graph theory, AI, knowledge acquisition • networks - graph theory, Petri nets, computer algebra • Drug Design, Molecular Modelling - parallel algorithms

  4. Outline • Introduction • SNP analysis in the human genome • Dynamic programming as basis for sequence comparison • Summary and outlook

  5. SNP analysis in the human genome The average human being exhibits ~100 new mutations. The mutation of one nucleotide (point mutation) in the genome: SingleNucleotidePolymorphism-SNP, if it occurs with more than 1% in a population. non-synonymous:causes a mutation of the amino acid TTT - Phe  TTA - Leu synonymous:codes the same amino acid TTT - Phe  TTC - Phe

  6. SNPs – some numbers Two individuals: some millions nucleotide differences ~ 100,000 amino acid differences Within a population: 1/300 bp differences ~ half of the SNPs in coding regions are none-synonymous. In two equal chromosomes: 1/1000 bp differences (nucleotide-variety) Most frequent type: transition C  T (G  A) 2/3 of all SNPs

  7. Why SNPs are interesting? • Medical questions • CD - CV hypothesis • (Common Disease - Common Variant) • Example: ApoE*E4 allele of Alzheimer’s disease • How many SNPs are associated with diseases? • How can we identify these SNPs? • Ho many none-synonymous SNPs are damaging • the structure or function of the protein? • How can we identify these SNPs?

  8. A disease causing SNP The human Hemochromatosis protein (1A6Z) Frequency: ~ 6% in north Europe ~ 14% in Irland

  9. Search for SNPs in data bases Filter Data bases Results Keywords: ‘3D STRUCTURE‘ and ‘DISEASE MUTATION’ 1 551 disease causing mutations SWISS-PROT Keywords: ‘3D STRUCTURE‘ and ‘POLYMORPHISM’, but not ‘DISEASE MUTATION’ Allelic variants with >1% frequency in ‘normal’ humans OMIM 459 allelic variants HGBASE BLASTX search against HSSP 440neutral mutations between species dbSNP Search for close homologues (>95% similarity) in other species for all until now selec-ted proteins and mutations HSSP

  10. Prediction of function-damaging effect Active sites, binding sites Analysis of the multiple alignment Disulfide bridges Hydrophobicity in the protein core Solvent accessibility Interactions with hetero atoms

  11. Prediction rules • The amino acid variant is function-damaging, if • it is located in a region annotated in SWISS-PROT as • ACTIVE_SITE, BINDING_SITE, SITE, MOD_RES, DISULFID, • METAL or • 2. it is not compatible with the amino acid substitutions at the • same position of homologous proteins, or • 3. it is located inside of the protein core and causes a change • in the electrostatic potential, or • 4. it is located at the protein surface and changes the surface • accessibility of the protein, or • 5. it concerns a proline residue in a helix, or • 6. its minimal distance to hetero atoms (except water) < 6 Å.

  12. Results Control predictions on proteins with known function-damaging mutations total predicted absolute percent Disease causing 60 54 90 Mutations Function-damaging 54 43 80 mutations artificially generated

  13. Results total as function-damaging predicted absolute percent All Polymorphisms 459 156 34 Experimentally proved 245 79 32 Polymorphisms

  14. False-negative predictions isoleucine

  15. False-negative predictions serine

  16. False-negative predictions  K A L G I S P F H E  Homo sapiens  K S L G I S P F H E  Ovis aries  K G L G L S P F H E  Gallus gallus  K T F G I S P F H E  Sminthopsis macroura  K A L G V S P F H E  Petaurus breviceps  K K L G L T P F H E  Rana catesbiana  T N Q G S T P F H E  Sparus aurata  K K Q N L E S F F P  Escherichia coli  E S K J L D T F F P  Salmonella dublin  K A K N V E S F Y P  Caenorhabelzis elegans Part of the multiple alignment of the human transthyretin

  17. False-negative predictions

  18. False-negative predictions

  19. False-negative predictions

  20. Outline • Introduction • SNP analysis in the human genome • Dynamic programming as basis for sequence comparison • Summary and outlook

  21. Sequence Alignment • Search for evolutionary or functional similarity • Input: two nucleotide or amino acid sequences • Desired output: biologically meaningful similarity • Scoring of an alignment: Sum over all scores for each aligned pair and the gap penalties • Score for amino acid pairs: substitution matrices (PAM, BLOSUM) • Difficulty to set gap penalties • Search for the optimal global alignment

  22. Sequence Alignment Human alpha globin and human beta globin: true HBA_HUMAN GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKL G+ +VK+HGKKV A+++++AH+D++LS+LH KL HBB_HUMAN GNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKL Human alpha globin and leghaemoglobin from yellow lupin: true HBA_HUMAN GSAQVKGHGKKVADALTNAVAHV---D--DMPNALSALSDLHAHKL ++ ++++H+ KV + +A ++ +L+ L+++H+ K LGB2_LUPLU NNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATLKNLGSVHVSKG Human alpha globin and glutathione S-transferase: false HBA_HUMANGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSD----LHAHKL GS+ + G + +D L ++ H+ D+ A +AL D ++AH+ F11G11.2 GSGYLVGDSLTFVDLL--VAQHTADLLAANAALLDEFPQFKAHQE

  23. Dynamic Programming • Application to optimisation problems • Development of an dynamic programming algorithm (1) characterise the structure of an optimal solution (2) recursively define the value of an optimal solution (3) compute the value of an optimal solution in a bottom-up fashion (4) construct an optimal solution from computed information

  24. Dynamic Programming - Example MH E A G A W G H E E 0 -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P-8 –2 -1 -1 -2 -1 -4 -2 -2 -1 -1 A-16 -2 -1 5 0 5 -3 0 -2 -1 -1 W-24 -3 -3 -3 -3 -3 15 -3 -3 -3 -3 H-32 10 0 -2 -2 -2 -3 -2 10 0 0 E-40 0 6 -1 -3 -3 -1 -3 0 6 6 A-48 -2 -1 5 0 5 -3 0 -2 -1 -1 E-56 0 6 -1 -3 -3 -1 -3 0 6 6 Initialisation with BLOSUM50

  25. Dynamic Programming - Example (1) Optimal solution: the alignment with the highest score (2) Recursive solution: Three ways an alignment can be extended up to ( i, j ) (a) xi aligned to yj (b) xi aligned to a gap (c) yj aligned to a gap { F ( I - 1, j - 1 ) + s ( xi, yj ) M( i, j ) = max F ( i-1, j ) – d F (I, j-1 ) – d d: gap penalty, s (xi, yj): score of the pair (xi, yj)

  26. Dynamic Programming - Example MH E A G A W G H E E 0-8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P-8 –2 -1 -1 -2 -1 -4 -2 -2 -1 -1 A-16 -2 -1 5 0 5 -3 0 -2 -1 -1 W-24 -3 -3 -3 -3 -3 15 -3 -3 -3 -3 H-32 10 0 -2 -2 -2 -3 -2 10 0 0 E-40 0 6 -1 -3 -3 -1 -3 0 6 6 A-48 -2 -1 5 0 5 -3 0 -2 -1 -1 E-56 0 6 -1 -3 -3 -1 -3 0 6 6 Computation of M ( 1, 1 )

  27. Dynamic Programming - Example MH E A G A W G H E E 0 -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P-8 –2-9 -1 -2 -1 -4 -2 -2 -1 -1 A-16 -2 -1 5 0 5 -3 0 -2 -1 -1 W-24 -3 -3 -3 -3 -3 15 -3 -3 -3 -3 H-32 10 0 -2 -2 -2 -3 -2 10 0 0 E-40 0 6 -1 -3 -3 -1 -3 0 6 6 A-48 -2 -1 5 0 5 -3 0 -2 -1 -1 E-56 0 6 -1 -3 -3 -1 -3 0 6 6 Computation of M ( 1, 2 )

  28. Dynamic Programming - Example MH E A G A W G H E E 0 -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P-8–2-9-17 -2 -1 -4 -2 -2 -1 -1 A-16 -2 -1 5 0 5 -3 0 -2 -1 -1 W-24 -3 -3 -3 -3 -3 15 -3 -3 -3 -3 H-32 10 0 -2 -2 -2 -3 -2 10 0 0 E-40 0 6 -1 -3 -3 -1 -3 0 6 6 A-48 -2 -1 5 0 5 -3 0 -2 -1 -1 E-56 0 6 -1 -3 -3 -1 -3 0 6 6 Computation of M ( 1, 3 )

  29. Dynamic Programming - Example MH E A G A W G H E E 0 -8-16 -24 -32 -40 -48 -56 -64 -72 -80 P-8–2-9-17-25 -1 -4 -2 -2 -1 -1 A-16 -2 -1 5 0 5 -3 0 -2 -1 -1 W-24 -3 -3 -3 -3 -3 15 -3 -3 -3 -3 H-32 10 0 -2 -2 -2 -3 -2 10 0 0 E-40 0 6 -1 -3 -3 -1 -3 0 6 6 A-48 -2 -1 5 0 5 -3 0 -2 -1 -1 E-56 0 6 -1 -3 -3 -1 -3 0 6 6 Computation of M ( 1, 4 )

  30. Dynamic Programming - Example MH E A G A W G H E E 0 -8-16-24 -32 -40 -48 -56 -64 -72 -80 P-8–2 -9 -17 -25 -33 -42 -49 -57 -65 -73 A-16-10 -3 -4 -12 -20 -28 -36 -44 -52 -60 W-24-18 -11 -6 -7 -15 -5 -13 -21 -29 -37 H-32-14 -18 -13 -8 -9 -13 -7 -3 -11 -19 E-40-22 -8 -16 -16 -9 -12 -15 -7 3 -5 A-48-30 -16 -3 -11 -11 -12 -12 -15 -5 2 E-56-38 -24 -11 -6 -12 -14 -15 -12 -9 1 The completely calculated matrix

  31. Dynamic Programming - Example Computation of the optimal alignment path MH E A G A W G H E E 0 -8-16-24 -32 -40 -48 -56 -64 -72 -80 P-8–2 -9 -17-25 -33 -42 -49 -57 -65 -73 A-16-10 -3 -4 -12 -20 -28 -36 -44 -52 -60 W-24-18 -11 -6 -7 -15 -5-13 -21 -29 -37 H-32-14 -18 -13 -8 -9 -13 -7 -3 -11 -19 E-40-22 -8 -16 -16 -9 -12 -15 -7 3 -5 A-48-30 -16 -3 -11 -11 -12 -12 -15 -5 2 E-56-38 -24 -11 -6 -12 -14 -15 -12 -9 1 HEAGAWGHE-E --P-AW-HEAE The optimal alignment

  32. Dynamic Programming - Example • Needleman/Wunsch – algorithm for global alignment Needleman & Wunsch (1970) J. Mol. Biol.48:443-453. O(n3) • Gotoh – algorithm for global alignment O(n2) Gotoh (1982) J. Mol. Biol.162:705-708. • Smith/Watermann – algorithm for local alignment Smith & Waterman (1981) J. Mol. Biol.147:195-197.O(n2)

  33. Summary • SNP analysis as typical example for bioinformatics Sunyaev, Ramensky, Lathe III., Kondrashov, Bork, Human Molecular Genetics (2001) 10:591-597. data base parsing multiple sequence alignment rule based system molecular modelling • Application of dynamic programming to sequence alignment Gotoh algorithm for pair-wise global sequence alignment

  34. Outlook • Application of graph theory to protein structure analysis PTGL Protein Topology Graph Library http://sanaga.tfh-berlin.de/~ptgl/ptgl.html May, Barthel, Koch (2004) Bioinformatics, in press. Koch (2001) Theoretical Computer Science250:1-30. • Investigations of Alternative Splicing Boué, Vingron, Koch (2002) Bioinformatics, suppl.2, 18:S65-S75. Kriventseva, Koch, Apweiler, Vingron, Bork, Gelfand, Sunyaev (2003)Trends in Genetics19:124-128.

  35. Outlook • Modelling, analysis, and simulation of biological molecular networks using Petri net theory in co-operation with BTU Cottbus (Prof. M. Heiner) Voss, Heiner, Koch (2003) In Silico Biology3:0031. Heiner, Koch, Will (2004) BioSystems, Special Issue75(1-3):15-28. Heiner & Koch (2004) Proc. 25th ICAPTN, LNCS3099:216-237. Koch, Junker, Heiner (2004) Bioinformatics, in press. • Ongoing projects: 1. Human glycolysis with coloured Petri nets Thomas Runge 2. Metabolism in the human liver cell Daniel Schrödter 3. G1/S phase in the mammalian cell cycle Dr. Thomas Kaunath 4. Duchenne muscle dystrophy Stepfanie Grunwald

  36. Thank you!

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