Algoritmi e strutture di dati 2
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1 Algoritmi e strutture di dati 2 Paola Vocca Lezione 5: Allineamento di sequenze Lezione 5 - Allineamento di sequenze 1
2 Allineamento sequenze Struttura secondaria dell RNA Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 2
3 RNA Secondary Structure RNA. String B = b 1 b 2 b n over alphabet {A, C, G, U}. Secondary structure. RNA is single-stranded so it tends to loop back and form base pairs with itself. This structure is essential for understanding behavior of molecule. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 3 G U C A G A A G C G A U G A U U A G A C A A C U G A G U C A U C G G G C C G Ex: GUCGAUUGAGCGAAUGUAACAACGUGGCUACGGCGAGA complementary base pairs: A-U, C-G
4 RNA Secondary Structure Secondary structure. A set of pairs S = {(b i b j )} that satisfy: o [Watson-Crick.] S is a matching and each pair in S is a Watson-Crick complement: A U, U A, C G, or G C. o [No sharp turns.] The ends of each pair are separated by at least 4 intervening bases. If (bi, b j ) S, then i < j 4. o [Non-crossing.] If (bi, bj) and (b k, b l ) are two pairs in S, then we cannot have i < k < j < l. Free energy. Usual hypothesis is that an RNA molecule will form the secondary structure with the optimum total free energy. approximate by number of base pairs Goal. Given an RNA molecule B = b 1 b 2 b n, find a secondary structure S that maximizes the number of base pairs. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 4
5 RNA Secondary Structure: Examples Examples. C G G U G G G C G G U C G C G C U A U A U A G U A U A U A base pair A U G U G G C C A U A U G G G G 4 C A U A G U U G G C C A U ok sharp turn crossing Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 5
6 RNA Secondary Structure: Subproblems First attempt. OPT(j) = maximum number of base pairs in a secondary structure of the substring b 1 b 2 b j. match b t and b n 1 t n Difficulty. Results in two sub-problems. o Finding secondary structure in: b 1 b 2 b t-1. o Finding secondary structure in: b t+1 b t+2 b n-1. OPT(t-1) need more sub-problems Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 6
7 Dynamic Programming Over Intervals Notation. OPT(i, j) = maximum number of base pairs in a secondary structure of the substring b i b i+1 b j. o Case 1. If i j 4. OPT(i, j) = 0 by no-sharp turns condition. o Case 2. Base b j is not involved in a pair. OPT(i, j) = OPT(i, j 1) o Case 3. Base b j pairs with b t for some i t < j 4. non-crossing constraint decouples resulting sub-problems OPT(i, j) = 1 + max{opt(i, t 1) + OPT(t + 1, j 1)} t take max over t such that i t < j 4 and b t and b j are Watson-Crick complements Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 7
8 Bottom Up Dynamic Programming Over Intervals Q. What order to solve the sub-problems? A. Do shortest intervals first. RNA(b 1,,b n ) { for k = 5, 6,, n-1 for i = 1, 2,, n-k j = i + k Compute M[i, j] } return M[1, n] using recurrence i j Running time. O(n 3 ). Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 8
9 Confronto fra sequenze Allineamento di sequenze Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 9
10 Sequence Alignment Applicazioni. o Alla basedel comando Unix diff. o Riconoscimento del parlato. o Biologia computazionale. La biologia computazionale spesso riguarda lo studio delle sequenze. Sequenze DNA Sequenze RNA Sequenze di proteine. Queste sequenze posso essere viste come stringhe sull alfabeto DNA & RNA: alfabeto di 4 lettere Proteine: Alfabeto di 20 lettere Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 10
11 Confronto fra sequenze Individuare similarità fra sequenze è importante inmolti contesti della biologia. Per esempio: Determinare geni/proteine con una origine comune Consente di predirne la funzione o la struttura. Individuare sottosequenze comuni in geni e/o proteine Identificare motifs comuni Individuare sequenze che si possono sovrapporre. Aiutare nell assemblaggio delle sequenze. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 11
12 Confronto di sequenze: Perché? E uno degli strumenti informatici maggiormente usati in biologia Le nuove sequenze vengono confrontate con le sequenze già presenti nelle base di dati. Sequenze simili spesso hanno una funzione od un origine simili. La selezione opera a livello di sistema, ma le mutazioni avvengono a livello di sequenza Le similarità sono riconoscibili nei secoli Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 12
13 Allineamento di sequenze Defn: An alignment of strings S, T is a pair of strings S, T (with spaces) s.t. 1. S = T, and ( S = length of S ) 2. removing all spaces leaves S, T Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 13
14 Alignment Scoring The score of aligning (characters or spaces) x & y is σ(x,y). Value of an alignment An optimal alignment: one of max value Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 14
15 Optimal Alignment: A Simple Algorithm for all subseqs A of S, B of T s.t. A = B do align A[i] with B[i], 1 i A align all other chars to spaces compute its value retain the max end output the retained alignment Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 15
16 Analisi Assumiamo S = T = n Il costo di valutare un allineamento è O n Il numero di allineamenti possibili sono 2n n o Prendi n caratteri di S, T insieme o Consideriamo i primi k di questi in S o Allinea questi k con k caratteri non scelti di T Tempo totale: n 2n n > 22n Per n > 3 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 16
17 Optimal Substructure (In More Detail) Optimal alignment ends in 1 of 3 ways: last chars of S&T aligned with each other last char of S aligned with space in T last char of T aligned with space in S ( never align space with space; σ(, ) < 0 ) In each case, the rest of S&T should be optimally aligned to each other. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 17
18 Optimal Alignment in O(n 2 ) via Dynamic Programming Input: S, T, S = n, T = m Output: value of optimal alignment Risolvibile tramite i problemi intermedi: V(i, j) = value of optimal alignment of S[1],, S[i] with T[1],, T[j] for all 0 i n, 0 j m. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 18
19 Base Cases Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 19
20 General Case Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 20
21 Calculating One Entry Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 21
22 Example Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 22
23 Example Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 23
24 Complexity Notes Tempo: O mn (sia per determinare il valore dell allineamento, che l allineamento stesso) Spazio: O mn (sia per determinare il valore dell allineamento, che l allineamento stesso) E facile determinare il valore della matrice in tempo O mn e spazio O min{m, n} E possibile calcolare sia il valore sia l allineamento in tempo O mn e spazio O min{m, n} (Prossimi lucidi) Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 24
25 Confronto fra sequenze Somiglianza fra stringhe Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 25
26 String Similarity How similar are two strings? o ocurrance o occurrence o c u r r a n c e - o c c u r r e n c e 6 mismatches, 1 gap Rispetto all allineamento di seguenze, questo è un caso più generale. osi considerano gap (allinemaenti con lo spazio, odisaccoppiamenti (mistmatch) oaccoppiamenti o c - u r r a n c e o c c u r r e n c e 1 mismatch, 1 gap o c - u r r - a n c e o c c u r r e - n c e 0 mismatches, 3 gaps Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 26
27 Edit Distance Edit distance. [Levenshtein 1966, Needleman-Wunsch 1970] o Gap penalty ; o mismatch penalty pq. o Cost = sum of gap and mismatch penalties. C T G A C C T A C C T - C T G A C C T A C C T C C T G A C T A C A T C C T G A C - T A C A T TC + GT + AG + 2 CA 2 + CA Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 27
28 Sequence Alignment Goal: Given two strings X = x 1 x 2... x m and Y = y 1 y 2... y n find alignment of minimum cost. Def. An alignment M is a set of ordered pairs x i y j such that each item occurs in at most one pair and no crossings. Def. The pair x i y j and x i y j cross if i < i, but j > j. cost( M ) xi y ( x, y ) M i j mismatch j i: x unmatched i j Ex: CTACCG vs. TACATG. Sol: M = x 2 y 1, x 3 y 2, x 4 y 3, x 5 y 4, x 6 y 6. gap j: y unmatched x 1 x 2 x 3 x 4 x 5 x 6 C T A C C - - T A C A T G G y 1 y 2 y 3 y 4 y 5 y 6 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 28
29 Designing the Dynamic Programming FACT. Let M be any Alignment of X and Y. IF (m, n) is not in M THEN either x is not matched in M or y m n is not matched in M. Proof. Otherwise, a cross would occur!!!! Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 29
30 Sequence Alignment: Problem Structure Def. OPT(i, j) = min cost of aligning strings x 1 x 2... xi and y 1 y 2... y j. o Case 1: OPT matches x i y j. pay mismatch for x i y j + min cost of aligning two strings x 1 x 2... x i 1 and y 1 y 2... y j 1 o Case 2a: OPT leaves x i unmatched. pay gap for x i and min cost of aligning x 1 x 2... x i-1 and y 1 y 2... y j o Case 2b: OPT leaves y j unmatched. pay gap for y j and min cost of aligning x 1 x 2... x i and y 1 y 2... y j-1 ì ï ï OPT(i, j) = í ï î ï jd if i = 0 ì a xi y j +OPT(i -1, j -1) ï min í d +OPT(i -1, j) otherwise ï î d +OPT(i, j -1) id if j = 0 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 30
31 Sequence Alignment: Algorithm Sequence-Alignment(m, n, x 1 x 2...x m, y 1 y 2...y n,, ) { for i = 0 to m M[i, 0] = i for j = 0 to n M[0, j] = j } for i = 1 to m for j = 1 to n M[i, j] = min( [x i, y j ] + M[i-1, j-1], + M[i-1, j], + M[i, j-1]) return M[m, n] Analysis. (mn) time and space. English words or sentences: m, n 10. Computational biology: m = n = 100, billions ops OK, but 10GB array? 31 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze
32 Sequence comparison Sequence Alignment in Linear Space Lezione 5 - Allineamento di sequenze 32
33 Sequence Alignment: Linear Space Q. Can we avoid using quadratic space? Easy. Optimal value in O(m + n) space and O(mn) time. o Compute OPT(i, ) from OPT(i-1, ). o No longer a simple way to recover alignment itself. Theorem. [Hirschberg 1975] Optimal alignment in O(m + n) space and O(mn) time. o Clever combination of divide-and-conquer and dynamic programming. o Inspired by idea of Savitch from complexity theory. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 33
34 Sequence Alignment: Linear Space Edit distance graph. o Let f(i, j) be shortest path from (0, 0) to (i, j). o Observation: f(i, j) = OPT(i, j). y 1 y 2 y 3 y 4 y 5 y x 1 a xi y j x 2 i-j x 3 m-n Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 34
35 Sequence Alignment: Linear Space Edit distance graph. o Let f(i, j) be shortest path from (0, 0) to (i, j). o Can compute f (, j) for any j in O(mn) time and O(m + n) space. (utilizzando la colonna precedente) j y 1 y 2 y 3 y 4 y y 6 x 1 x 2 i-j x 3 m-n Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 35
36 Sequence Alignment: Linear Space Edit distance graph. o Let g(i, j) be shortest path from (i, j) to (m, n). o Can compute by reversing the edge orientations and inverting the roles of (0, 0) and (m, n) 0-0 y 1 y 2 y 3 y 4 y 5 y 6 x 1 i-j a xi y j x 2 x 3 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 36 m-n
37 Sequence Alignment: Linear Space Edit distance graph. o Let g(i, j) be shortest path from (i, j) to (m, n). o Can compute g(, j) for any j in O(mn) time and O(m + n) space. y 1 j y 2 y 3 y 4 y 5 y x 1 i-j x 2 x 3 Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze m-n 37
38 Sequence Alignment: Linear Space Observation 1. The cost of the shortest path that uses (i, j) is f(i, j) + g(i, j). y 1 y 2 y 3 y 4 y 5 y x 1 i-j x 2 x 3 m-n Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 38
39 Sequence Alignment: Linear Space Proof. l ij the length of the shortest corner-to-corner path in G XY that passes through (i, j). o any such path must get from (0, 0) to (i, j) and from i, j to (m, n). o Its length is at least f(i, j) + g(i, j). o l ij > f(i, j) + g(i, j). o On the other hand, the corner-to-corner path that consists of a minimum-length path from (0, 0) to (i, j), followed by a minimum-length path from i, j to (m, n). o This path has length f(i, j) + g(i, j). o and so we have l ij f(i, j) + g(i, j). l ij = f(i, j) + g(i, j) Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 39
40 Sequence Alignment: Linear Space Observation 2. let q be an index that minimizes f(q, n/2) + g(q, n/2). Then, the shortest path from (0, 0) to (m, n) uses (q, n/2). n / 2 y 1 y 2 y 3 y 4 y 5 y x 1 i-j q x 2 x 3 m-n Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 40
41 Sequence Alignment: Linear Space Divide: find index q that minimizes f(q, n/2) + g(q, n/2) using DP. o Align x q and y n/ 2. Conquer: recursively compute optimal alignment in each piece. n / 2 y 1 y 2 y 3 y 4 y 5 y x 1 i-j q x 2 x 3 m-n Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 41
42 Sequence Alignment: Running Time Analysis Warmup Theorem. Let T m, n length at most m and n. T(m, n) = O(mn log n). = max running time of algorithm on strings of T ( m, n) 2T ( m, n / 2) O( mn) T ( m, n) O( mnlog n) Remark. Analysis is not tight because two sub-problems are of size (q, n/2) and (m q, n/2). In next slide, we save log n factor. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 42
43 Sequence Alignment: Running Time Analysis Theorem. Let T(m, n) = max running time of algorithm on strings of length at most m and n. T(m, n) = O(mn ). Pf. (by induction on n) o O(mn) time to compute f(, n/2) and g(, n/2) and find index q. o T(q, n/2) + T(m q, n/2) time for two recursive calls. o Choose constant c so that: T ( m, 2) cm T (2, n) cn T ( m, n) cmn T ( q, n / 2) T ( m q, n / 2) o Base cases: m = 2 or n = 2. o Inductive hypothesis: T(m, n) 2cmn. T ( m, n) T ( q, n / 2) T ( m q, n / 2) cmn 2cqn / 2 2c( m q) n / 2 cmn cqn cmn cqn cmn 2cmn Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 43
44 Sequence Comparison Local alignments & gaps Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 44
45 Variations Local Alignment Preceding gives global alignment, i.e. full length of both strings; Might well miss strong similarity of part of strings amidst dissimilar flanks Gap Penalties 10 adjacent spaces cost 10 x one space? Many others Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 45
46 Local Alignment: Motivations Interesting (evolutionarily conserved, functionally related) segments may be a small part of the whole Active site of a protein Scattered genes or exons amidst junk, e.g. retroviral insertions, large deletions Don t have whole sequence Global alignment might miss them if flanking junk outweighs similar regions Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 46
47 Local Alignment Optimal local alignment of strings S & T: Find substrings A of S and B of T having max value global alignment Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 47
48 The Obvious Local Alignment Algorithm Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 48
49 Local Alignment in O(nm) via Dynamic Programming Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 49
50 Base Cases Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 50
51 General Case Recurrences Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 51
52 Scoring Local Alignments Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 52
53 Finding Local Alignmentsv Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 53
54 Notes Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 54
55 Alignment With Gap Penalties Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 55
56 Alignment with Gaps AAC-AATTAAG-ACTAC-GTTCATGAC A-CGA-TTA-GCAC-ACTG-T-C-GA AACAATTAAGACTACGTTCATGAC--- AACAATT GTTCATGACGCA Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 56
57 Gap Penalties Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 57
58 Global Alignment with Affine Gap Penalties Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 58
59 Affine Gap Algorithm Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 59
60 Gaps Both alignments have the same number of matches and spaces but alignment II seems better. Definition: A gap is any maximal, consecutive run of spaces in a single string. The length of the gap will be the number of spaces in it. Example I has 11 gaps while example II has only 2 gaps. Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 60
61 Biological motivation Number of mutational events A single gap - due to single event that removed a number of residues. Each separate gap - due to distinct independent events. Protein structure Protein secondary structure consists of alpha helixes, beta sheets and loops Loops of varying size can lead to very similar structure Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 61
62 Alignment in Real Life One of the major uses of alignments is to find sequences in a database Such collections contain massive number of sequences (order of 10 6 ) Finding homologies in these databases with dynamic programming can take too long Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 62
63 Heuristic Search Instead, most searches relay on heuristic procedures these are not guaranteed to find the best match Sometimes, they will completely miss a highscoring match We now describe the main ideas used by some of these procedures Actual implementations often contain additional tricks and hacks Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 63
64 Basic Intuition Almost all heuristic search procedure are based on the observation that real-life matches often contain long strings with gapless matches These heuristic try to find significant gapless matches and then extend them Algoritmi e strutture di dati 2 Lezione 5 - Allineamento di sequenze 64
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