dynamic programming -class4- · dynamic programming recall: thechange problem otherproblems:...

Dynamic ProgrammingPart I: Examples

Bioinfo I (Institut Pasteur de Montevideo) Dynamic Programming -class4- July 25th, 2011 1 / 77

Dynamic Programming

Recall: the Change ProblemOther problems: Manhattan Tourist Problem, LCS ProblemFinally: Sequence alignments

Manhattan Tourist Problem (MTP)

Imagine seeking a path (from sourceto sink) to travel (only eastward andsouthward) with the most number ofattractions (*) in the Manhattan grid

Manhattan Tourist Problem (MTP)

Imagine seeking a path (from sourceto sink) to travel (only eastward andsouthward) with the most number ofattractions (*) in the Manhattan grid

Manhattan Tourist Problem: Formulation

Goal: Find the longest path in a weighted grid.Input: A weighted grid G with two distinct vertices, one labeled “source"and the other labeled “sink"Output: A longest path in G from “source" to “sink"

MTP: An example

MTP: Greedy Algorithm Is Not Optimal

MTP: Simple Recursive Program

1 MT(n,m)2 if n = 0 or m = 03 return MT(n,m)4 x ←MT(n-1,m)+ length of the edge from (n − 1,m) to (n,m)

5 y← MT(n,m-1)+length of the edge from (n,m − 1) to (n,m)

6 return max{x,y}

What’s wrong with this approach?

MTP: Simple Recursive Program

1 MT(n,m)2 if n = 0 or m = 03 return MT(n,m)4 x ←MT(n-1,m)+ length of the edge from (n − 1,m) to (n,m)

5 y← MT(n,m-1)+length of the edge from (n,m − 1) to (n,m)

6 return max{x,y}

What’s wrong with this approach?

MTP: Dynamic Programming

Calculate optimal path score for each vertex in the graphEach vertex’s score is the maximum of the prior vertices score plus theweight of the respective edge in between

MTP: Recurrence

Computing the score for a point (i , j) by the recurrence relation:

si ,j = max{

si−1,j + weight of the edge between (i − 1, j)and (i , j)si ,j−1 + weight of the edge between (i , j − 1)and (i , j)

The running time is n ×m for a n by m grid(n = # of rows, m = # of columns)

Manhattan is not a perfect Grid

Represented as a DAG: Directed Acyclic Graph

The score at point B is given by:

si ,j = max{

si−1,j + weight of the edge between(i − 1, j)and(i , j)si ,j−1 + weight of the edge between(i , j − 1)and(i , j)

Manhattan Is Not A Perfect Grid

Computing the score for point x is given by the recurrence relation:

sx = max{

sy + weight of vertex(y , x)wherey ∈ Predecessors(x)

Predecessors(x): set of vertices that have edges leading to x.

The running time for a graph G (V ,E ) (V is the set of all vertices and E isthe set of all edges) is O(E ) since each edge is evaluated once.

Longest Path in DAG Problem

Goal: Find a longest path between two vertices in a weighted DAG

Input: A weighted DAG G with source and sink vertices

Output: A longest path in G from source to sink

Longest Path in DAG: Dynamic Programming

Suppose vertex v has indegree 3 and predecessors {u1, u2, u3}Longest path to v from source is:

sv = max

su1 + weight of edge from u1 to vsu2 + weight of edge from u2 to vsu3 + weight of edge from u3 to v

In general:

sv = maxu{su + weight of edge from u to v}

Traveling in the Grid

The only hitch is that one must decide on the order in which visit theverticesBy the time the vertex x is analyzed, the values sy for all itspredecessors y should be computed - otherwise we are in troubleWe need to traverse the vertices in some orderTry to find such order for a directed cycle

Topological ordering

2 different topological orderings of the DAG

Traversing the Manhattan Grid

3 different strategies:a) Column by columnb) Row by rowc) Along diagonals

Pseudo-code MTP: Dynamic Programming

1 ManhattanTourist(w↓,~w ,w↘i ,j ,n,m)2 for i ← 1 to n3 si ,0 ← si−1,0 + w↓i ,04 for j ← 1 to m5 s0,j ← s0,j−1 + ~w0,j

6 for i ← 1 to n7 for j ← 1 to m8

si ,j = max

si−1,j + w↓i ,jsi ,j−1 + ~wi ,j

si−1,j−1 + w↘i ,j9 return sn,m

Dynamic ProgrammingPart II: Edit Distance & Alignments

Aligning DNA Sequences

LCS Alignment without mismatches

LCS: Longest Common SubsequenceGiven two sequences

v = v1v2...vm and w = w1w2...wn

The LCS of v and w is a sequence of positions in

v : 1 < i1 < i2 < ... < it < m

and a sequence of positions in

w : 1 < j1 < j2 < ... < jt < n

such that it-th letter of v equals to jt-th letter of w and t is maximal

LCS: Example

Every common subsequence is a path in 2-D grid

LCS Problem as Manhattan Tourist Problem

Edit Graph for LCS Problem

Every path is a common subsequence.Every diagonal edge adds an extra element to common subsequence.LCS Problem: Find a path with maximum number of diagonal edges.

Computing LCS

Let vi= prefix of v of length i : v1...viand wj = prefix of w of length j : w1...wj

Every Path in the Grid Corresponds to an Alignment

Aligning Sequences without Insertions and Deletions:Hamming Distance

Given two DNA sequences v and w :

The Hamming distance: dH(v ,w) = 8 is large but the sequences are verysimilar

Aligning Sequences with Insertions and Deletions

By shifting one sequence over one position:

The edit distance: dL(v ,w) = 2Hamming distance neglects insertions and deletions in DNA

Levenshtein or edit distance

DefinitionThe Levenshtein distance or edit distance dL between two sequences X andY is is the minimum number of edit operations of type

Replacement,Insertion, orDeletion,

that one needs to transform sequence X into sequence Y :

dL(X ,Y ) = min{R(X ,Y ) + I (X ,Y ) + D(X ,Y )}

Using M for match, an edit transcript is a string over the alphabet I, D,R, M that describes a transformation of X to Y .

Levenshtein or edit distance

Example: Given two stringsX = YESTERDAYY = EASTERS

Here is a minimum edit transcript for the above example:

edit transcript= D M I M M M M R D D

X= Y E S T E R D A YY= E A S T E R S

The edit distance dL(X ,Y ) of X ,Y is 5.

Edit Distance vs Hamming Distance

Hamming distance alwayscompares i th letter of v with i th

letter of w

Hamming distance:

d(v ,w) = 8

Computing Hamming distance isa trivial task.

Edit distance may compare i th

letter of v with j th letter of w

Edit distance:

d(v ,w) = 2

Computing edit distance is anon-trivial task.

Edit Distance vs Hamming Distance

Hamming distance alwayscompares i th letter of v with i th

letter of w

Hamming distance:

d(v ,w) = 8

Computing Hamming distance isa trivial task.

Edit distance may compare i th

letter of v with j th letter of w

Edit distance:

d(v ,w) = 2

Computing edit distance is anon-trivial task.

Edit Distance: Example

What is the edit distance? 5?

Can it be done in 3 steps?

The Alignment Grid

Every alignment path is from sourceto sink

Alignment as a path in the Edit Graph

Alignments in Edit Graph

Every path in the edit graphcorresponds to alignment:

Alignment: Dynamic Programming

si ,j = max

si−1,j−1 + 1 if vi = wj↘si−1,j ↓si ,j−1 →

Dynamic Programming Example

Initialize 1st row and 1st columnto be all zeroesOr, to be more precise, initialize0th row and 0th column to be allzeroes

si ,j = max

si−1,j−1 : value from NW +1

if vi = wj ↖si−1,j : value from N ↑si ,j−1 : value from W ←

Alignment: Backtracking

Arrows indicate where the score originated from:

↖↑←

Backtracking Example

Find a match in row and column2i=2, j=2, 5 is a match (T).j=2, i=4, 5, 7 is a match (T).Since vi = wj , si ,j = si−1,j−1 + 1

s2,2 = [s1,1 = 1] + 1

s2,5 = [s1,4 = 1] + 1

s4,2 = [s3,1 = 1] + 1

s5,2 = [s4,1 = 1] + 1

s7,2 = [s6,1 = 1] + 1

Continuing with the dynamicprogramming algorithm gives thisresult.

si ,j = max

This recurrence corresponds to the Manhattan Tourist problem (threeincoming edges into a vertex) with all horizontal and vertical edgesweighted by zero.

si ,j = max

This recurrence corresponds to the Manhattan Tourist problem (threeincoming edges into a vertex) with all horizontal and vertical edgesweighted by zero.

LCS Algorithm1 LCS(v,w)2 for i ← 1 to n3 si ,0←04 for j ← 1 to m5 s0,j←06 for i ← 1 to n7 for j ← 1 to m8

si ,j = max

si−1,j−1 + 1 if vi = wjsi−1,jsi ,j−1

bi ,j =

↑ if si ,j = si−1,j← if si ,j = si ,j−1↖ if si ,j = si−1,j−1

10 returnBioinfo I (Institut Pasteur de Montevideo) Dynamic Programming -class4- July 25th, 2011 53 / 77

Now What?

LCS(v,w) created thealignment gridNow we need a way to readthe best alignment of v andwFollow the arrows backwardsfrom sink

Printing LCS: Backtracking

1 PrintLCS(b,v,i,j)2 if i = 0 or j = 03 return4 if bi ,j =↖5 PrintLCS(b,v,i-1,j-1)6 print vi

7 else8 if bi ,j =↑9 PrintLCS(b,v,i-1,j)10 else11 PrintLCS(b,v,i,j-1)

Now What?

Alignment:A T C G - T A C -A T - G T T A - T

Now What?

Alignment:A T C G - T A C -A T - G T T A - T

LCS Runtime

It takes O(nm) time to fill in the n ×m dynamic programming matrix.Why O(nm)? The pseudocode consists of a nested “for" loop inside ofanother “for" loop to set up a n ×m matrix.

Dynamic ProgrammingPart II: Sequence Alignment

Outline

Dot plotsGlobal AlignmentScoring MatricesLocal AlignmentAlignment with Affine Gap Penalties

Types of sequence alignments

Dot plotsNumber of sequences

I pairwise: compares two sequencesI multiple: compares several sequences

Portion of aligned sequenceI global: aligns the sequences over all their lengthI local: finds subsequences with the best similarity scores

AlgorithmsI Optimal methods: Needleman-Wunsch, Smith-WatermanI Heuristics: FASTA, BLAST

Dot plot

→ the simplest way to visualize the similarity between two protein/DNAsequences is to use a similarity matrix

Identification of insertions/deletionsIdentification of direct repeats or inversionsSteps to create a dot plot

I 2D matrixI One sequence on the topI One sequence on the leftI For each matrix cell, compare the symbols and draw a point if there is

a coincidence

Dot plot

→ the simplest way to visualize the similarity between two protein/DNAsequences is to use a similarity matrix

Identification of insertions/deletionsIdentification of direct repeats or inversionsSteps to create a dot plot

I 2D matrixI One sequence on the topI One sequence on the leftI For each matrix cell, compare the symbols and draw a point if there is

a coincidence

Dot matrix sequence comparison

A dot matrix analysis is primarily a method for comparing two sequences.An (n ×m) matrix relating two sequences of length n and m respectively isproduced by placing a dot at each cell for which the corresponding symbolsmatch. Here is an example for the two sequences:

IMISSMISSISSIPPI andMYMISSISAHIPPIE:

DefinitionLet S = s1s2 . . . sn and T = t1 . . . tm be two strings of length n and mrespectively. Let M be an n ×m matrix. Then M is a (simple) dot plot iffor i , j , 1 ≤ i ≤ n, 1 ≤ j ≤ m :

M[i , j ] ={

1 for si = tj0 else.

Note: The longest common substring within the two strings S and T isthen the longest matrix subdiagonal containing only 1s. However, ratherthan drawing the letter 1 we draw a dot.

Some of the properties of a dot plot arethe visualization is easy to understandit is easy to find common substrings, they appear as contiguous dotsalong a diagonalit is easy to find reversed substringsit is easy to discover displacementsit is easy to find repeats

Dot plots

Sequence length: n and mO(nm)

DNA Protein

Noise in Dot plot

LDL human receptorcompared to himself

The low density lipoprotein (LDL)receptor is a cell surface proteinthat plays a central role in themetabolism of cholesterolin humans and animals. Mutationsaffecting its structureand function give rise to one of the mostprevalenthuman genetic diseases, familialhypercholesterolemia.

Reducing the noise

To reduce the noise, a window size w and a stringency s are used and adot is only drawn at point (x , y) if in the next w positions at least scharacters are equal.

Example: Phage P22

w = 1, s = 1 w = 11, s = 7 w = 23, s = 15

Random similarity in Dot plots

When comparing DNA, there is a 14 probability of random matches

When comparing protein sequences there is a 120 probability of random

matchesHence, if coding DNA regions are analized: translate first, then align!You can always go back to DNA after alignment

w=3, stringency 2

DNA sequence

Simple dot plot, w = 1 w = 23, stringency = 16

Protein sequence

Simple dot plot, w = 1 w = 23, stringency = 6

Insertion Deletion & Inversion

Repeats

ABCDEFGEFGHIJKLMNO

Tandem duplication Tandem duplicationCompared to non duplicated Compared to itself

Palindromic repeat (intra chain)

5’ GGCGG 3’

Limitations of dot plots

No score to quantify identical or similar stringsRuntime is quadratic; more efficient algorithms to identify identicalsubstrings exist (eg. based on suffix trees)

dynamic programming -class4- · dynamic programming recall: thechange problem otherproblems:...

Documents

dynamic programming - virginia...

approximate dynamic programming for large-scale resource...

dynamic programming and finance applications · 2017. 1....

dynamic programming (ch. 15) dynamic programming

dynamic programming: example dynamic programming problems

dynamic programming dynamic programming is a general

advanced dynamic programming in...

1 dynamic programming 2012/11/20. p.2 dynamic programming...

lecture 5 dynamic programming. dynamic programming...

dynamic storytimes - dynamic children's programming

chapter 1 introduction and thechange management model

dynamic programming acm workshop 24 august 2011. dynamic...

dynamic programming - princeton university computer...

dynamic programming

dynamic programming - saylor academy€¦ · dynamic...

dynamic programming - mcgill...

lecture 3: planning by dynamic programming · lecture 3:...

dynamic programming - wordpress.com€¦ · dynamic...

approximate dynamic programming via linear...

dynamic programming and stochastic...