A Method for Aligning RNA Secondary Structures

Transcription

1 Method for ligning RN Secondary Structures Jason T. L. Wang New Jersey Institute of Technology J Liu, JTL Wang, J Hu and B Tian, BM Bioinformatics,

2 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (RMulti) ombining RSmatch with RNView onclusion and future work 2

3 Molecule building blocks Protein building blocks: 20 types of amino acid RN building blocks: Purine: denine, uanine Pyrimidine: ytosine, racil 3

4 RN structure elements RN sequence folds to form secondary/tertiary structure Majority of base connections involve two bases Watson-rick: or Non-canonical: or Basic structure elements of RN 4

5 Definition of structural components iven an RN sequence: : r 1 r 2 r 3 r n Two types of structural components [1] : Single bases (blue) Bonded base pairs (red) [1] Zuker, M. (1989) Science 5

6 Secondary structure constraint (1) Prohibited! No common base can be shared by any two pairs [2]. Bad: is shared by two pairs: - and - (a) OOD (b) BD [2] Hofacker, I.L. (2003) NR 6

7 Secondary structure constraint (2) hairpin Prohibited! hairpin element must have at least 3 bases on the loop part [3]. Bad: only two bases ( and ) present in the loop (a) OOD (b) BD [3] Zuker, M. (1991) NR 7

8 Secondary structure constraint (3) Pseudoknots are not included [4] (a) BD (b) OOD (nested structure) (c) OOD (branching) Prohibited! [4] Mathews, D.H. (1999) JMB 8

9 RN secondary structure representation schemes a. Bond annotation [5] b. rc representation [6] c. Tree representation [7] d. Nested parenthesis representation [8] [5] Shapiro, B. (1990) BIOS [6] Zhang, K. (1999) PM [7] Ma, B. (2002) TS [8] Hofacker, I.L. (2002) JMB 9

10 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (RMulti) ombining RSmatch with RNView onclusion and future work 10

11 Extended circle model circle 5 circle 4 circle 3 circle 2 circle 1 circle 7 circle 0 circle 6 circle 8 ircle model [9] : circle 0:,,,,, circle 1:,,, circle 7:,,,, circle 8:,,,,,, Sequential order between components: > > -> > -> - [9] Liu, J. (2005) BM Bioinformatics 11

12 Hierarchical organization circles are organized in a tree-like hierarchy circle 5 circle 4 circle 3 circle 2 circle 1 circle 7 circle 0 circle 6 circle 8 circle 3 circle 4 circle 5 circle 0 circle 1 circle 2 circle 6 circle 7 circle 8 12

13 Hierarchical relationship between two structural components (1) the same circle: e.g. each pair from,,, -, -,, - (2) descendant/ancestor circles: e.g. pair (, -) (3) cousin circles: e.g. pairs (, ), (-, -) and (, -) (1) (2) (3) circle 13

14 Partial structure induced by a structural component parent structure child structure 14

15 Structural alignment rules (1) 1 precedes 2 iff B 1 precedes B 2 where 1, 2, B 1,B 2 are structural components. 15

16 Structural alignment rules (2) RN 1 RN 2 (a) (a) Same loop relationship preserved: 1 is in the same loop as 2 iff B 1 is in the same loop as B 2 (b) ncestor/descendant relationship preserved: 1 is ancestor of 2 iff B 1 is ancestor of B 2 (b) (c) ousin relationship preserved: 1 is cousin of 2 iff B 1 is cousin of B 2 (c) 16

17 Example alignment First RN..((...(((...)))((.(...))).)).. Second RN..((..((...))(((...))).)).. ll structural alignment rules must be satisfied for a valid alignment In addition, a single base can not be aligned with a base pair lignment Result..((...(((...)))((.(.....))).)) ((.. ((... ))(( (...))).)).. 17

18 Dynamic programming algorithm: overview First structure Second structure DP scoring table The best alignment between partial structures of and - 18

19 ase 1 19

20 ase 2 20

21 ase 3 21

22 ase

23 ase

24 Example of matching score function Score function of matching two equal-length structural components: i.e. 1, if both a and b are single bases and a = g( a, b ) = 2, if both a and b are base pairs and a = b 0, otherwise ap penalty equals 0 Extending g to the whole set of matched component pairs, our goal is to maximize f(r 1, R 2 ) f ( R, R2 ) = g(, 1 a i bi i ) b 24

25 ell type 1 : single base vs. single base?..(...)....(...). ()..(...) (...). (B)..(...) (...). ()..(...) (...). 25

26 ell type 2: base pair vs. single base? first score second score?? 26

27 ell type 2: base pair vs. single base (first score) (...)?...(...). (...) (...). (... ) (...). 27

28 ell type 2: base pair vs. single base (second score)..(...)?...(...). ()..(...) (...). (B).. (...) (...). ().. (...) (...). 28

29 ell type 3: base pair vs. base pair..(...)?...(...) () (B) ()?? (b1)?? (b2) 29

30 ell type 3: base pair vs. base pair (first score) (...)? (...) () (B) () (...) (...) (... ) (...) (...) -- (... ) 30

31 ell type 3: base pair vs. base pair (2 nd & 3 rd score)..(...)? (...) (...)?...(...) (... ) (...) (...) (...) (...) (...) 31

32 ell type 3: base pair vs. base pair (final score)? ()..(...)..(...) --...(...)...(...) (B) ().. (...) (...)..(...) (...) (D).. (...) (...)..(...) (...) 32

33 nalysis of algorithm Time and space complexity Each score is calculated only once. Time is bounded by the number of score calculations needed to fill up the table. Each base pair will contribute to two or four score calculations. Single bases: N s ; base pairs: N p Total number of score calculations: N s2 +4N s N p +4N 2 p =O(N 2 ) N 2 s score calculations are contributed by two single bases 4N s N p score calculations are contributed by one single base and one base pair 4N p2 score calculations are contributed by two base pairs 33

34 Software RSmatch 34

35 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (RMulti) ombining RSmatch with RNView onclusion and future work 35

36 Motif example: detection/instantiation Motif structure is known IB ambiguity symbols: N: W: H: not 36

37 ap Penalty Example motif structure subject structure 37

38 Position independent scoring matrices Two scoring matrices ap penalty: -3 for each single base, -6 for each base pair, involved in the gap 38

39 Motifs used in the experiments (a) HSL3 (b) IRE HSL3 has a typical stem loop structure with two flanking tails IRE has specific stem-loop structure for gene regulation related to cell iron metabolism Wildcard n is allowed to match with 0 or 1 nucleotide IB code: M:, T/; Y:, T/; H: not ; R:, ; W:, T; 39

40 Experiments Performance measurements: sensitivity (recall) and specificity (precision) 19,986 human RefSeq mrn sequences were obtained from NBI; 39,972 TR regions were extracted Each TR sequence was chopped and folded into secondary structures using Vienna RN package, yielding ~575,000 structures ompare RSmatch with PatSearch [10] [10] Pesole. (2000) Bioinformatics 40

41 hop and fold TR sequences TR ORF TR ORF ORF: Open Reading Frame 41

42 Detecting HSL3 motif PatSearch: specificity (98.2%), sensitivity (87.1%). Several histone genes (i.e. NM_003542, NM_003548) were found by RSmatch, but not by PatSearch. 42

43 Detecting IRE motif se PatSearch to search 39,972 TR sequences for IRE motif and get 27 hit structures belonging to 18 TR sequences The 18 TR sequences were chopped and folded into 1,196 structures ompare RSmatch, Rsearch [11] and stemloc [12]. well-known IRE-containing structure (NM_000032) was used as the query (it does not have wildcard or ambiguity symbols since Rsearch and stemloc cannot handle them) [11] Klein, R.J. (2003) BM Bioinformatics [12] Holms, I. (2002) PSB 43

44 Experimental results for IRE motif 44

45 Dealing with complex structures 45

46 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (RMulti) ombining RSmatch with RNView onclusion and future work 46

47 Extension to multiple structural alignment search small database YES expand best alignment score (best alignment) < δ OR non-expandable NO pairwise match profile expand seed alignment seed alignment 47

48 Example expand expand 48

49 RMulti Webserver 49

50 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (Rmulti) ombining RSmatch with RNView onclusion and future work 50

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53 Outline Introduction Structural alignment of RN (preliminaries, RSmatch algorithm, software) Experiments (RN motif detection) Multiple structural alignment (RMulti) ombining RSmatch with RNView onclusion and future work 53

54 onclusion n efficient algorithm RSmatch to align and analyze RN secondary structures multiple structural alignment tool RMulti visualization tool combining RSmatch with RNView 54

55 Future Work Extending RSmatch to handle pseudoknots Large-scale genome-wide motif mining Indexing very large RN structure databases Improved multiple structural alignment of RN sequences RN classification and clustering RN-RN interactions and protein-rn interactions 55

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