Bioinformatics Sequence comparison 2 local pairwise alignment

Transcription

1 Bioinformatics Sequence comparison 2 local pairwise alignment David Gilbert Bioinformatics Research Centre Department of Computing Science, University of Glasgow

2 Lecture contents Variations on dynamic programming Gap penalties Substitution matrices To explain the reason that local alignments may be more appropriate than global ones. To describe the use of Dot-Plots in visualising an alignment To describe the Smith-Waterman method of finding and scoring an optimal local pairwise alignment To describe in outline the BLAST algorithm for database search 2

3 Solution to Week 1 xercise A C A C D A A 3

4 Percentage sequence identity number of identical residues x 1 = number of residues in smallest sequence Can differ if have gaps/no_gaps: compute for these sequences: TGCATA ATCTGAT -TGCAT-A- AT-C-TGAT Sequence similarity - change at amino-acid residue or nucleotide that preserves the physicochemical properties of the residue 4

5 β and α globin, without gaps β MVHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPK α VLSPADKTNVKAAWGKVGAHAGYGAALRMFLSFPTTKTYFPHFDLSHGSAQVKGHGK β VKAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFG α KVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAFTPA β KFTPPVQAAYQKVVAGVANALAHKYH α VHASLDKFLASVSTVLTSKYR Compute the identity% 5

6 β and α globin, with gaps CLUSTAL W (1.81) multiple sequence alignment β MVHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPK α --VLSPADKTNVKAAWGKVGAHAG----YGAALRMFLSFPTTKTYFPHFDLSHGSAQ β VKAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFG α VKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLP β KFTPPVQAAYQKVVAGVANALAHKYH α AFTPAVHASLDKFLASVSTVLTSKYR Compute the identity% 6

7 Blast output Human beta globin hits coyote! >SW:HBB_CANFA P256 HMOGLOBIN BTA CHAIN. Length = 146 Score = 276 bits (698), xpect = 2e-74 Identities = 131/146 (89%), Positives = 137/146 (93%) What happened? Query:2 VHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPKV 61 VHLT KS V+ LWGKVNVDVGGALGRLL+VYPWTQRFF+SFGDLSTPDAVM N KV Sbjct: 1 VHLTAKSLVSGLWGKVNVDVGGALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAKV 6 Query: 62 KAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFGK 121 KAHGKKVL +FSDGL +LDNLKGTFA LSLHCDKLHVDPNF+LLGNVLVCVLAHHFGK Sbjct: 61 KAHGKKVLNSFSDGLKNLDNLKGTFAKLSLHCDKLHVDPNFKLLGNVLVCVLAHHFGK 12 Query: 122 FTPPVQAAYQKVVAGVANALAHKYH 147 FTP VQAAYQKVVAGVANALAHKYH Sbjct: 121 FTPQVQAAYQKVVAGVANALAHKYH 146 Compute the identity% 7

8 Penalising gaps Gap = maximal consecutive run of spaces in an alignment (1 or more spaces) Simple penalty - each gap contributes a constant weight More complex - gap penalty proportional to gap length Large gap penalty few gaps (less substrings in alignment). Small penalty fragmented alignments. FASTA: GAPOPN: Penalty for the first residue in a gap (-12 for proteins, -16 for DNA). GAPXT: Penalty for additional residues in a gap (-2 for proteins, -4 for DNA). 8

9 Substitution matrices A C G T Unitary matrix: match=1, mismatch= sparse matrix (most elements are ) Poor diagnostic power all identical matches carry identical weighting We can enhance scoring potential of weak but biologically significant signals Scoring matrices - weight matches for non-identical residues according to observed substitution rates. More on this later! A C G T 9

10 Global and local alignment Global alignment - as per dynamic programming solution as explained Needleman & Wunsch algorithm (197) Local alignment - find local regions from each string which are similar: Corresponds to shorter, localised paths in the matrix. Justification - biological functional sites localised to short conserved regions (no indels/mutations). Smith-Waterman algorithm (1981) 1

11 Local alignment Start & end dynamic programming computation at any cells instead of (,) and (i,j) The matrix contains a maximum value that may not be at (i,j) [the end of the input sequences] represents the endpoint of an alignment s.t. no other pair of segments with greater similarity exists between the 2 sequences 11

12 Global vs local alignment Global, Needleman & Wunsch Local, Smith & Waterman 12

13 Local Pairwise Alignment Distantly related sequences i.e. proteins Uneven accumulation of mutations along sequence Similar segments in overall dissimilar sequences Rearrangement of gene segments in genome Related sub-sequences in unrelated genes Local similarity corresponds to Shared structural or functional motif Robust to mutations volutionarily important Global alignment may fail in such cases Island of similarity lost in random symbol matches 13

14 Local Pairwise Alignment Require to find similar segments in sequences Database search task : Find homologous sequences {d} to query q in database D In a reasonable time Present only homologous sequences (True Positives) Do not present non-homologous sequences (False Positives) First how to find local alignments? 14

15 Dot Matrices First technique to discover local similarities M by N matrix created symbols of q (length M) on one axis, symbols of d (length N) on the other Matrix populated with dots and spaces Dot in cell (i,j) indicates that q(i) = d(j) asy to understand visualisation Common substrings found easily contiguous diagonal dots 15

16 Dot plots A convenient way of comparing 2 sequences visually Use matrix, put 1 sequence on X-axis, 1 on Y-axis Cells with identical characters filled with a 1, non-identical with (simplest scheme - could have weights) Identical sequences will look like WHAT? Similar sequences will have a broken diagonal, plus some other lines Distantly related sequences - much noisier. Can generate an alignment Best path through dotplot given by dynamic programming algorithms: global alignment = Needleman & Wunsch local alignment = Smith & Waterman 16

17 H A K P K S A V T Dot plots H L T P K S V H T 17

18 Dot plots H L T P K S V H T H x x A K x P x x x x x K x S x A V x T x x Alignment HLTPKSVHT HAKPKSAVT 18

19 A dotplot 19

20 2 Try a dotplot and alignment M T F R D L L S V S F G P R P M T F R D L L S V S F G P R P D S S A G G

21 Try a dotplot and alignment Two sequences q = ANTGDSCTAWCDFGHIKPQWRTY d = TRDFGAACDFGHIKLHYTYTRTRRACDFGHIKHYGT 21

22 Dot Matrices asy to identify common recurring substring CDFGHIK Anti-diagonal identifies reversed substring TR Matrix image can be noisy Most of dots not associated with a common substring Matrices can be very large & unwieldy for typical protein sequences ~ 5 ~ 1 aa s 22

23 Smith-Waterman Method Require objective score of alignment Can employ dynamic programming method (Lecture 1) though requires some changes Consider following two sequences q = ACDCAD d = RDCDKL Unsure at what symbols (residues) highest scoring local alignments end all pairs should be considered Consider q 8 and d 6 23

24 Smith-Waterman Method Consider q 8 and d 6 i.e q = ACDCAD & d = RDCD Scoring.5 equality, -.3 inequality, -.5 gap q 8 d 6 A R C - D D - C C A D D c.s a.s Removing first two pairs in alignment will improve alignment score negative scores 24

25 Smith-Waterman Method q 3 8 D C A D d 2 6 D - C D c.s a.s

26 Smith-Waterman Method Removing prefixes with negative scores improves overall score To find best local alignment ending in q i & d j must find best starting point Fortunately a DP method can be employed In this case all negative values are replaced with zero Simple change to the global alignment DP 26

27 Smith-Waterman Method S i,j = max { S i-1,j + s(q i, -) S i,j-1 + s(-, d j ) S i-1,j-1 + s(q i, d j ) 27

28 Smith-Waterman Method Now initialise first row & columns with In this example: Score as.5 equality, -.3 inequality, -.5 gap, S i,j I/J R D C D K L A C D C A D

29 Smith-Waterman Method Initialise first row & columns with S i,j Find maximum partial alignment score S k,l Trace backwards, constructing alignment, until reach a cell with value 29

30 Smith-Waterman Method Find maximum partial alignment score S k,l Trace backwards, constructing alignment, until reach a cell with value I/J R D C D K L A C D C A D

31 Smith-Waterman Method Find maximum partial alignment score S k,l Trace backwards, constructing alignment, until reach a cell with value I/J R D C D K L A C D C A D

32 FASTA (Lipman & Pearson 1985) Fast approximation to the Smith-Waterman algorithm Local alignments - tries to find paths of regional similarity, rather than trying to find the best alignment between 2 sequences. Alignments can contain gaps. Rapid Heuristic - not guaranteed to find the best alignment between 2 sequences; it may miss matches. uses a strategy which is expected to find most matches, but sacrifices complete sensitivity in order to gain speed. A substitution matrix is used during all phases of protein searches 32

33 FASTA Identifies short words (k-tuples) common to both sequences (nucleotides k=1 or 2, amino-acids, k up to 6) Similar to focussing on diagonal matches in dynamic programming algorithm Uses heuristic to join k-tuples close on same diagonal If significant number of matches found, then uses dynamic programming to compute gapped alignment 33

34 FASTA algorithm 34

35 FASTA output HBB_CANFA P256 HMOGLOBIN BTA CHAIN. (146 aa) initn: 886 init1: 886 opt: 886 Z-score: bits: 28.6 (): 2.9e-54 Smith-Waterman score: 886; % identity (89.726% ungapped) in 146 aa overlap (2-147:1-146) MBOSS MVHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPK :::: :::: :..:::::::::::::::::::.:::::::::.::::::::::::.: : SW:HBB VHLTAKSLVSGLWGKVNVDVGGALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAK MBOSS VKAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFG :::::::::..:::::.::::::::: ::::::::::::::::.::::::::::::::: SW:HBB VKAHGKKVLNSFSDGLKNLDNLKGTFAKLSLHCDKLHVDPNFKLLGNVLVCVLAHHFG MBOSS KFTPPVQAAYQKVVAGVANALAHKYH ::::: ::::::::::::::::::::: SW:HBB KFTPQVQAAYQKVVAGVANALAHKYH

36 Database Search - BLAST SW complexity similar to DP for global alignment Not realistic for database search in terms of time Trade-off guarantee of finding best alignment with time expense Basic Local Alignment Search Tool BLAST (Alschul et al 199) mploys fast search to find small segments with similar score in both sequences xtend small segments (local alignments) Returns maximal scoring pairs (MSP) & MSP score & statistical significance of scores 36

37 Database Search - BLAST Query sequence split into words of defined length Query string q = AFGTULL with word length of L = 3 AFG, FGT, GTU, TUL, ULL Define a threshold alignment score T Find all word-pairs of length L with score T For amino acids there are 2 3 = 8 distinct words w of length L=3 e.g Find all w such that S(w, AFG) T 37

38 Database Search - BLAST Search database for all hits - sequences with exact matches to each w Indexing of sequences to create inverted file by employing hash table to index sequences fast xtend alignment of hits while score increases producing High Scoring Pair s Return sequences with HSP s which have significantly (statistically) higher scores than a threshold Smax Smax obtained empirically from random sequences 38

39 Database Search - BLAST Varying the threshold alignment score T Search time decreases as T is increased, fewer word pairs are found Sensitivity of search decreases as T is increased, word pairs overlooked (homologous sequences may be discarded) The score of the alignment Smax AND the associated statistical significance are required to assess whether homology is suggested 39

40 BLAST - Basic Local Alignment Tool Altschul et al 199 Given 2 sequences: Segment pair - pair of subsequences of the same length forming an ungapped alignment Computes all segment pairs If there is a MSP maximal segment pair (highest score of all pairs for 1 comparison) above some cutoff score C and C is significant then report hit Also reports those sequences where the score of MSP < C, but several segment pairs in combination which are significant. Reports score from highest scoring pairs & probability scores [ values] (expected by chance). Only produces ungapped alignments 4

41 BLAST Algorithm 41

42 Gapped BLAST (Altschul et al 1997) Seeks only 1 (not all) ungapped alignments with significant match Uses dynamic programming to extend residues in both directions to give gapped alignment 3x faster than ungapped BLAST 42

43 dited results (MBL) Database: embl: 958,67 sequences; 2,466,994,978 total letters Score Sequences producing significant alignments: (bits) Value M_HUM:HSBGL1 V497 Human messenger RNA for beta-globin M_HUM:AF AF Homo sapiens hemoglobin beta subuni M_HUM:HSHMOB M25113 Human sickle beta-hemoglobin mrna. 11. M_PAT:I32884 I32884 Sequence 9 from patent US M_HUM:HS22231 U2223 Human thalassemia beta globin gene, c e-114 M_OM:AGHBD M1961 Spider monkey (A.geoffroyi) delta-globin e-99 M_OM:CPHBB5CP J33 monkey (c.polykomos) beta-globin gene; e-99 M_OM:PPHBD M21825 Orangutan delta globin gene, complete cds e-93 M_OM:CPHBDPSC J335 Monkey (colobus) delta-globin pseudoge e-78 M_OM:LMHBB M15734 Lemur (brown) beta-globin gene, complete e-7 M_OM:TSHBD J4428 T.syrichta delta-globin gene, complete cds e-68 M_OM:OCU692 U692 Otolemur crassicaudatus epsilon-, gamm e-68 M_OM:LBGLOB Y347 Lepus europaeus adult beta-globin gene 266 1e-68 M_OM:GCDLGLB M6174 G.crassicaudatus beta globin gene, com e-68 M_OM:MOHBDPS J332 monkey (anubis) silent delta-globin gene e-67 M_OM:TSHBB J4429 T.syrichta beta globin gene, complete cds. 26 7e-67 M_PAT:A34698 A34698 Synthetic psxbeta+ sequence 258 3e-66 M_OM:OCBGLO V882 Rabbit (O. cuniculus) gene for beta-globin. 25 7e-64 M_OM:BTBG M63453 Bovine Beta globin gene and globin (PSI-3) e-55 43

44 dited results (Swiss-prot) Database: swissprot: 86,593 sequences; 31,411,157 total letters Sequences producing significant alignments: Score (bits) Value SW:HBB_HUMAN P223 HMOGLOBIN BTA CHAIN. (human) 36 2e-83 SW:HBB_GORGO P224 HMOGLOBIN BTA CHAIN. (gorilla) 35 4e-83 SW:HBB2_PANL P18988 HMOGLOBIN BTA-2 CHAIN. (lion) 32 3e-82 SW:HBB_HYLLA P225 HMOGLOBIN BTA CHAIN. (gibbon) 3 8e-82 SW:HBB_PRN P232 HMOGLOBIN BTA CHAIN. (Hanumam langur) 298 5e-81 SW:HBB_COLPO P19885 HMOGLOBIN BTA CHAIN. (Colobus) 295 3e-8 SW:HBB_CRA P228 HMOGLOBIN BTA CHAIN. (Green monkey) 295 3e-8 SW:HBB_MACFU P227 HMOGLOBIN BTA CHAIN. (Japanese macaque) 293 2e-79 SW:HBB_CALAR P18985 HMOGLOBIN BTA CHAIN. (Marmoset) 292 2e-79 SW:HBB_ATG P234 HMOGLOBIN BTA CHAIN. (Spider monkey) 292 2e-79 SW:HBB_MANSP P8259 HMOGLOBIN BTA CHAIN. (Mandrill) 291 4e-79 SW:HBB1_RAT P291 HMOGLOBIN BTA CHAIN, (Rat) 255 4e-68 SW:HBB_RIU P259 HMOGLOBIN BTA CHAIN. (Hedgehog) 252 2e-67 SW:HBB_PANPO P4244 HMOGLOBIN BTA CHAIN. (Bison) 251 5e-67 SW:HBB_BISBO P9422 HMOGLOBIN BTA CHAIN. (Leopard) 251 5e-67 44

45 Blast alignment Blast output >SW:HBB_CANFA P256 HMOGLOBIN BTA CHAIN. Length = 146 Score = 276 bits (698), xpect = 2e-74 Identities = 131/146 (89%), Positives = 137/146 (93%) Query: 2 VHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPKV 61 VHLT KS V+ LWGKVNVDVGGALGRLL+VYPWTQRFF+SFGDLSTPDAVM N KV Sbjct: 1 VHLTAKSLVSGLWGKVNVDVGGALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAKV 6 Query: 62 KAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFGK 121 KAHGKKVL +FSDGL +LDNLKGTFA LSLHCDKLHVDPNF+LLGNVLVCVLAHHFGK Sbjct: 61 KAHGKKVLNSFSDGLKNLDNLKGTFAKLSLHCDKLHVDPNFKLLGNVLVCVLAHHFGK 12 Query: 122 FTPPVQAAYQKVVAGVANALAHKYH 147 FTP VQAAYQKVVAGVANALAHKYH Sbjct: 121 FTPQVQAAYQKVVAGVANALAHKYH

46 FASTA vs BLAST BLAST faster than FASTA without significant loss of ability to find the similar database sequences. BLAST & FAST equivalent for highly similar sequences FASTA may be better for less similar sequences But can always make a full local alignment (Smith-Waterman algorithm) - highest potential of finding less similar sequences in a database search since the entire sequence lengths are compared. 46

47 MBL - some stats Total nucleotides (current 118,263,14,52) 31/1/6 Number of entries (current 65,933,89) 47

48 Smith-Waterman programs MPsrch dinburgh University Scanps2.3 Geoff Barton (BI; University of Dundee) Blitz (bic_sw) Compugen -- uses MPsrch & Scanps ( only) 48

49 Analyse gene families Multiple alignments reveal (subtle) conserved family characteristics characters sequences S1 Y D G G A V - A L S2 Y D G G A L S3 F G G I L V A L S4 F D - G I L V Q A V S5 Y G G A V V Q A L consensus y d G G AI VL V e A l 49

50 Multiple aligment - methods Simultaneous: N-wise alignment (adapted from pairwise approach) uses N-dimension matrix. Complexity is O(m 1 m 2 ) [2 sequences length m 1 & m 2 ] O(m n ) [n sequences of length m] Thus only good for short sequences. s 1 s 2 a 1 Manua1 (!) s 3 s 4 a 2 a 3 a 4 Progessive (heuristic) e.g. ClustalW: compute pairwise sequence identities construct binary tree (can output phylogenetic tree) align similar sequences in pairs, add distantly related ones later. s 5 5

51 Multiple sequence alignment (globins) CLUSTAL W (1.81) multiple sequence alignment Human VHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPKV 6 Gorilla VHLTPKSAVTALWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSTPDAVMGNPKV 6 Rabbit VHLSSKSAVTALWGKVNVVGGALGRLLVVYPWTQRFFSFGDLSSANAVMNNPKV 6 Pig VHLSAKAVLGLWGKVNVDVGGALGRLLVVYPWTQRFFSFGDLSNADAVMGNPKV 6 ***:.***.**.*******:****************************..:***.**** Human KAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFRLLGNVLVCVLAHHFGK 12 Gorilla KAHGKKVLGAFSDGLAHLDNLKGTFATLSLHCDKLHVDPNFKLLGNVLVCVLAHHFGK 12 Rabbit KAHGKKVLAAFSGLSHLDNLKGTFAKLSLHCDKLHVDPNFRLLGNVLVIVLSHHFGK 12 Pig KAHGKKVLQSFSDGLKHLDNLKGTFAKLSLHCDQLHVDPNFRLLGNVIVVVLARRLGH 12 ******** :**:** **********.*******:********:*****:* **::::*: Human FTPPVQAAYQKVVAGVANALAHKYH 146 Gorilla FTPPVQAAYQKVVAGVANALAHKYH 146 Rabbit FTPQVQAAYQKVVAGVANALAHKYH 146 Pig DFNPNVQAAFQKVVAGVANALAHKYH 146 :*.* ****:**************** 51

52 Multiple sequence alignments & phylogenetic trees Pair Score Human-Gorilla 99 Human-Rabbit 9 Gorilla-Rabbit 89 Human-Pig 84 Gorilla-Pig 84 Rabbit-Pig 83 ((Human:., Gorilla:.685) :.411, Rabbit:.5479, Pig:.1959); 52

53 What can we do with multiple alignments? Create (databases of) profiles derived from multiple alignments for protein families profile = multiple alignment + observed character frequencies at each position Search with a sequence against a database of profiles (e.g. PROSIT database) faster than sequence against sequence gives a more general result ( the input sequence matches globin profile ) Search with a profile against a database of sequences PSI-BLAST : can identify more distant relationships than by normal BLAST search 53

54 PSI-BLAST (position specific iterated BLAST) Single protein sequence Search database(blast) Profile?iterate until convergence Multiple alignment stimate statistical significance of local alignments 54

55 PSI-BLAST (Altschul et al 1997) (1) Start with 1 sequence (or profile) = probe (2) Search with BLAST and select top hits manually or automatically (3) Make multiple alignment & profile (4) stimate statistical significance of local alignments. If significance ok & you want to continue, then go to (1) using the profile, else exit 55

56 Dates & programs FASTA BLAST Gapped BLAST & PSI BLAST 56