Malik Hammoutène - SSC. Sequence Analysis

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2 CONTENTS Introduction Analysis of individual sequences Secondary structure prediction Pairwise sequence comparison Database searching I: single heuristic algorithms Alignment and search statistics Multiple sequence alignment Multiple alignment and database searching Protein families and protein domains Conclusion

3 INTRODUCTION DNA STORES AND PASSES ON GENETIC INFORMATION FROM ONE GENERATION TO ANOTHER. THE STEPS OF THE LADDER ARE MADE OF PAIRS OF NITROGEN BASES: 1. ADENOSINE = A 2. GUANOSINE = G 3. CYTIDINE = C 4. THYMIDINE = T

4 INTRODUCTION DNA HELD BY HYDROGEN BONDS

5 INTRODUCTION STOP 2 START 2 STOP 1 START 1

6 INTRODUCTION RIBOSOME KNOWS TO WHICH AMINO ACID A CODON CORRESPONDS

7 INTRODUCTION PRIMARY STRUCTURE OF A PROTEIN = A CHAIN OF AMINO ACIDS

8 INTRODUCTION SECONDARY STRUCTURE -> 3RD DIMENSION

9 ANALYSIS OF INDIVIDUAL SEQUENCES AMINO ACIDS 1. NON-POLAR AND NEUTRAL 2. POLAR AND NEUTRAL 3. ACID AND POLAR 4. BASIC AND POLAR

10 ANALYSIS OF INDIVIDUAL SEQUENCES HYDROPHOBICITY - = HYDROPHILIC

11 ANALYSIS OF INDIVIDUAL SEQUENCES AMINO ACID AMIN ACID R-GROUP

12 ANALYSIS OF INDIVIDUAL SEQUENCES

13 SECONDARY STRUCTURE PREDICTION CHOU FASMAN Table 8 Chou & Fasman Secondary Structure Propensity of the Amino Acids Pα Pβ Pc Pα Pβ Pc A M C N D P E Q F R G S H T I V K W L Y

14 SECONDARY STRUCTURE PREDICTION GOR METHOD

15 SECONDARY STRUCTURE PREDICTION PHD

16 SECONDARY STRUCTURE PREDICTION TABLE OF PERFORMANCES CF GOR I LIM LEVIN PTIT JASEP7 GOR III ZHANG PHD Scores (%)

17 PAIRWISE SEQUENCE COMPARISON DOT PLOTS

18 PAIRWISE SEQUENCE COMPARISON DOT PLOTS UNFILTERED FILTERED

19 PAIRWISE SEQUENCE COMPARISON SEQUENCE ALIGNMENT GATTATACCA GATTACA GATTATACCA GATTA---CA GAP OF LENGTH 3 INSERTIONS AND DELETIONS ( INDEL ) ARE REPRESENTED BY GAPS IN ALIGNMENTS

20 PAIRWISE SEQUENCE COMPARISON WHAT ABOUT THIS: GCTACTAGTTCGCTTAGC GCTACTAGCTCTAGCGCGTATAGC WHICH ONE?? GCTACTAG-T-T--CGC-T-TAGC GCTACTAGCTCTAGCGCGTATAGC GCTACTAGTT------CGCTTAGC GCTACTAGCTCTAGCGCGTATAGC

21 PAIRWISE SEQUENCE COMPARISON NEEDLEMAN/WUNSCH ALGORITHM (EXAMPLE WITH NO PENALTY) SEQUENCE #1: GAATTCAGTTA; M = 11 (LETTERS) SEQUENCE #2: GGATCGA; N = 7 (LETTERS) SCORING SCHEME: Si,j = 1 IF POS I OF #1 IS THE SAME AS POS J OF #2 Si,j = 0 IF MISMATCH SCORE w = 0 (GAP PENALTY) STEPS: INITIALIZATION MATRIX FILL TRACEBACK

22 PAIRWISE SEQUENCE COMPARISON SEQUENCE #1: GAATTCAGTTA; M = 11 (LETTERS) SEQUENCE #2: GGATCGA; N = 7 (LETTERS) INITIALIZATION

23 PAIRWISE SEQUENCE COMPARISON SEQUENCE #1: GAATTCAGTTA; M = 11 (LETTERS) SEQUENCE #2: GGATCGA; N = 7 (LETTERS) MATRIX FILL FOR EACH POSITION IN THE MATRIX, Mi,j IS DEFINED TO BE THE MAXIMUM SCORE AT THE POSITION i,j: Mi,j = MAX[ Mi-1, j-1 +Si,j (match/mismatch in the diagonal), Mi,j-1 + w (gap in sequence #1), Mi-1,j + w (gap in sequence #2)]

24 PAIRWISE SEQUENCE COMPARISON SEQUENCE #1: GAATTCAGTTA; M = 11 (LETTERS) SEQUENCE #2: GGATCGA; N = 7 (LETTERS) TRACEBACK G_ A A T T C A G T T A G G _ A _ T C _ G A

25 PAIRWISE SEQUENCE COMPARISON GAP PENALTY GATCGCTACGCTCAGC A.C.C..C..T PERFECT SIMILARITY EVERYTIME!

26 PAIRWISE SEQUENCE COMPARISON ALIGNMENT GLOBAL ALIGNMENT G-ATES GRATED LOCAL ALIGNMENT DO NOT NEED TO ALIGN ALL THE BASES IN ALL SEQUENCES ALIGN BILLGATESLIKESCHEESE AND GRATEDCHEESE G-ATESLIKESCHEESE OR G-ATES & CHEESE GRATED-----CHEESE GRATED & CHEESE

27 SINGLE SEQUENCE HEURISTIC ALGORITHMS DATABASE SEARCHING > fasta myquery swissprot -ktup 2 search program query sequence sequence database optional parameters

28 SINGLE SEQUENCE HEURISTIC ALGORITHMS RESULTS THE BEST SCORES ARE The best scores are: initn init1 opt z-sc E(77110) gi sp P49789 FHIT_HUMAN BIS(5'-ADENOSYL) gi sp P49776 APH1_SCHPO BIS(5'-NUCLEOSYL) e-21 gi sp P49775 HNT2_YEAST HIT FAMILY PROTEI e-16 gi sp Q58276 Y866_METJA HYPOTHETICAL HIT e-07 gi sp Q11066 YHIT_MYCTU HYPOTHETICAL e-07 gi sp O07513 HIT_BACSU HIT PROTEIN e-06 gi sp Q04344 HNT1_YEAST HIT FAMILY PROTEI e-05 gi sp P75504 YHIT_MYCPN HYPOTHETICAL gi sp P32084 YHIT_SYNP7 HYPOTHETICAL gi sp P94252 YHIT_BORBU HYPOTHETICAL gi sp P47378 YHIT_MYCGE HYPOTHETICAL HIT gi sp P32083 YHIT_MYCHR HYPOTHETICAL gi sp P49773 IPK1_HUMAN HINT PROTEIN (PRO gi sp P70349 IPK1_MOUSE HINT PROTEIN (PRO gi sp P49774 YHIT_MYCLE HYPOTHETICAL HIT gi sp P16436 IPK1_BOVIN HINT PROTEIN (PRO gi sp P80912 IPK1_RABIT HINT PROTEIN (PRO gi sp P42856 ZB14_MAIZE 14 KD ZINC-BINDIN gi sp P42855 ZB14_BRAJU 14 KD ZINC-BINDIN gi sp P31764 GAL7_HAEIN GALACTOSE-1-PHOSP gi sp P16550 APA1_YEAST 5',5'''-P-1,P-4-TE gi sp P49348 APA2_KLULA 5',5'''-P-1,P-4-T gi sp P23228 HMCS_CHICK HYDROXYMETHYLGLUTA gi sp P06994 MDH_ECOLI MALATE DEHYDROGENA gi sp Q10798 DXR_MYCTU 1-DEOXY-D-XYLULOSE gi sp P05113 IL5_HUMAN INTERLEUKIN-5 PRECU gi sp P46685 IL5_CERTO INTERLEUKIN-5 PREC gi sp P15124 GLNA_METCA GLUTAMINE SYNTHETA gi sp P33937 NAPA_ECOLI PERIPLASMIC NITRA gi sp P10403 ENV1_DROME RETROVIRUS-RELATED gi sp P48415 SC16_YEAST MULTIDOMAIN VESIC gi sp O67501 IPYR_AQUAE INORGANIC PYROPHO

29 SINGLE SEQUENCE HEURISTIC ALGORITHMS FASTA (FAST-ALL) 1) FIND K-TUPLES IN THE TWO SEQUENCES 2) IDENTIFY THE 10 HIGHEST SCORING REGIONS 3) INTRODUCE GAPS 4) DETERMINE BEST SEGMENT OF SIMILARITY

30 SINGLE SEQUENCE HEURISTIC ALGORITHMS 1) FIND K-TUPS IN THE TWO SEQUENCES position protein 1 n c s p t a..... protein a c s p r k position in offset amino acid protein A protein B pos A - posb a c k - 11 n 1 - p r - 10 s t Note the common offset for the 3 amino acids c,s and p A possible alignment is thus quickly found - protein 1 n c s p t a protein 2 a c s p r k

31 SINGLE SEQUENCE HEURISTIC ALGORITHMS 2) IDENTIFY THE 10 HIGHEST SCORING REGIONS

32 SINGLE SEQUENCE HEURISTIC ALGORITHMS 3) INTRODUCE GAPS 4) DETERMINE BEST SEGMENT OF SIMILARITY

33 SINGLE SEQUENCE HEURISTIC ALGORITHMS BLAST (BASIC LOCAL ALIGNMENT SEARCH TOOL) 1) COMPILE THE LIST OF POSSIBLE WORDS 2) SCAN DATABASE FOR EXACT MATCHING 3) SCAN THROUGH THE LIST AND TRY TO EXTEND IT

34 SINGLE SEQUENCE HEURISTIC ALGORITHMS

35 ALIGNMENT AND SEARCH STATISTICS ALIGNMENT SCORE SUM OF THE WEIGHTS OF EVERY PAIR OF THE ALIGNMENT Z-SCORE (STANDARD DEVIATION FROM THE MEAN) Z-SCORE = (s-m)/e s = INITIAL SCORE m = MEAN OF THE RANDOM SCORES e = DEVIATION OF THE RANDOM SCORES

36 ALIGNMENT AND SEARCH STATISTICS EXTREME VALUE DISTRIBUTION (EVD) DENSITY FUNCTION:

37 ALIGNMENT AND SEARCH STATISTICS EXPECT VALUE E = NUMBER OF DATABASE HITS YOU EXPECT TO FIND BY CHANCE E = Kmne -λs Number K = scale for search space λ = scale for scoring system S = bitscore = (λs - lnk)/ln2 m = effective length of query n = effective length of database Score

38 MULTIPLE SEQUENCE ALIGNMENT EVOLUTION ALIGNMENT NYLS NKYLS NFS NFLS N-YLS NKYLS N-F-S N-FLS +K -L Y F NYLS

39 MULTIPLE SEQUENCE ALIGNMENT SUM-OF-PAIRS SCORE S(x, x) = 1 S(x, y) = -1 S(x, -) = -2 S(-, -) = 0 A A G 1 A - T -2 C C C 1 G G G 1 T T T 1 A A A 1 C G A - -1 A A T 1 T T T 1 A G A -1 S(A,G) = -1 S(SEQ1,SEQ2) = = = = = = = = = = = S(SEQ1,SEQ3) = 2 S(SEQ2,SEQ3) SUM-OF-PAIRS SCORE

40 MULTIPLE SEQUENCE ALIGNMENT DYNAMIC PROGRAMMING C G T - G -G T A A G FOR n SEQUENCES OF SIZE l: SPACE: O(ln) TIME: O(2 n )

41 MULTIPLE SEQUENCE ALIGNMENT PROFILE EXAMPLE s(a,1)p(a,1) + s(b,1)p(b,1) + s(c,1)p(c,1) + S(d,1)p(D,1) = S(A,1)

42 MULTIPLE SEQUENCE ALIGNMENT CLUSTAL 1) COMPUTE A SIMILARITY OF PAIRS MATRIX

43 MULTIPLE SEQUENCE ALIGNMENT CLUSTAL 2) COMPUTE A TREE

44 MULTIPLE SEQUENCE ALIGNMENT CLUSTAL 3) BUILD THE FINAL MULTIPLE ALIGNMENT

45 MULTIPLE ALIGNMENT AND DB SEARCHING PROFILE SEARCHING MULTIPLE SEQUENCE ALIGNMENT CONSTRUCTION OF A PROFILE COMPARAISON TO A DATABASE BEST SIMILARITIES FOUND

46 MULTIPLE ALIGNMENT AND DB SEARCHING HIDDEN MARKOV MODEL Emission Probabilities Transition probabilities

47 MULTIPLE ALIGNMENT AND DB SEARCHING HIDDEN MARKOV MODEL SCORING #1 - T G C T - - A G G vrs: #2 - A C A C - - A T C Regular Expression ([AT] [CG] [AC] [ACTG]* A [TG] [GC]): #1 = Member #2: Member HMM: #1 = Score of % #2 Score of 4.7%

48 PROTEIN FAMILIES AND PROTEIN DOMAINS PROSITE DATABASE

49 PROTEIN FAMILIES AND PROTEIN DOMAINS PFAM

50 CONCLUSION HIGHTHROUGHPUT COMPUTATIONAL ANALYSIS TOOL A BLESSING A PROBLEM

51 REFERENCES Bioinformatics Sequence and Genome Analysis David W. Mount Cold Spring Harbor Laboratory Press Notes and Powerpoint:

52 Thank you for your attention!