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Reconstructing evolutionary trees by maximum parsimony. Suppose we manage to sequence a particular gene across a whole bunch of different species. For concreteness, say there are n species, and the sequences are strings of length k over alphabet={A,C,G,T}. How can we use this information to reconstruct the evolutionary history of these species?

Evolutionary history is commonly represented by a tree whose leaves are the different species, whose root is their common ancestor, and whose internal branches represent speciation events (that is, moments when a new species broke off from an existing one). Thus we need to find the following:

• An evolutionary tree with the given species at the leaves.

• For each internal node, a string of length K: the gene sequence for that particular ancestor.

For each possible tree T annotated with sequencess(u)kat each of its nodes , we can assign a score based on the principle of parsimony: fewer mutations are more likely.

localid="1659249441524" score(T)=(u.v)E(T)(numberofpositionsonwhichs(u)ands(v)disagree)

Finding the highest-score tree is a difficult problem. Here we will consider just a small part of it: suppose we know the structure of the tree, and we want to fill in the sequences s(u) of the internal nodes u. Here’s an example with k=4 and n=5:


(a) In this particular example, there are several maximum parsimony reconstructions of the internal node sequences. Find one of them.

(b) Give an efficient (in terms of n and k ) algorithm for this task. (Hint: Even though the sequences might be long, you can do just one position at a time.)

Short Answer

Expert verified

(a) One of the reconstructions of the internal nodes sequences:


(b)Algorithm for the given task.

δi,j=1ifi=j0otherwiseL=left-subtreeTR=right-subtreeTST,C=mina,bSL,a+SR,b+δc,a+δc,b

Step by step solution

01

Step 1:Find one of the reconstruction

(a)

Consider the given tree,start mutation(change in sequence) from one position at a time, this also removes ambiguity of making sequence. Now according to mutation, there can be many evolution trees. One of the possible evolutionary tree is as follows,


Therefore, one of the reconstruction is obtained.

02

Step 2:Give an efficient algorithm

(b)

Consider one bit at time to define the algorithm. Define the state S(T,c), Where cA,C,G,T. To represent the problem domain,T is the subtree and the minimum score when the root node of T is c. So the following algorithm is,

δI,J=1ifi=j0otherwiseL=left-subtreeTR=right-subtreeTST,C=mina,bSL,a+SR,b+δc,a+δc,b

Since inner nodes has two sons, the number of inner nodes that can be obtained is the number of leaf nodes minus 1. The state number can be calculated in s=O(n) time.

And the transition cost of the state is O (1). Thus, the whole algorithm complexity is O (nk)

Therefore, the efficient algorithm has been obtained with O (nk) complexity.

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Most popular questions from this chapter

Optimal binary search trees. Suppose we know the frequency with which keywords occur in programs of a certain language, for instance:

begin5%do40%else8%end4%

if10%then10%while23%

We want to organize them in a binary search tree, so that the keyword in the root is alphabetically bigger than all the keywords in the left subtree and smaller than all the keywords in the right subtree (and this holds for all nodes). Figure 6.12 has a nicely-balanced example on the left. In this case, when a keyword is being looked up, the number of comparisons needed is at most three: for instance, in finding “while”, only the three nodes “end”, “then”, and “while” get examined. But since we know the frequency 196 Algorithms with which keywords are accessed, we can use an even more fine-tuned cost function, the average number of comparisons to look up a word. For the search tree on the left, it is

cost=1(0.04)+2(0.40+0.10)+3(0.05+0.08+0.10+0.23)=2.42

By this measure, the best search tree is the one on the right, which has a cost of Give an efficient algorithm for the following task. Input: n words (in sorted order); frequencies of these words: p1,p2,...,pn.

Output: The binary search tree of lowest cost (defined above as the expected number of comparisons in looking up a word).

Figure 6.12 Two binary search trees for the keywords of a programming language.

Alignment with gap penalties. The alignment algorithm of Exercise 6.26 helps to identify DNA sequences that are close to one another. The discrepancies between these closely matched sequences are often caused by errors in DNA replication. However, a closer look at the biological replication process reveals that the scoring function we considered earlier has a qualitative problem: nature often inserts or removes entire substrings of nucleotides (creating long gaps), rather than editing just one position at a time. Therefore, the penalty for a gap of length 10 should not be 10 times the penalty for a gap of length 1, but something significantly smaller.

Repeat Exercise 6.26, but this time use a modified scoring function in which the penalty for a gap of length k is c0 + c1k, where c0 and c1 are given constants (and c0 is larger than c1).

Here is yet another variation on the change-making problem (Exercise 6.17). Given an unlimited supply of coins of denominations x1,x2,x3.........xnwe wish to make change for a value v using at most k coins; that is, we wish to find a set ofkcoins whose total value is v. This might not be possible: for instance, if the denominations are 5 and 10 and k=6, then we can make change for 55 but not for 65. Give an efficient dynamic-programming algorithm for the following problem. Input: ; x1,x2,x3.........xn;k;v.Question: Is it possible to make change for v using at most k coins, of denominations x1,x2,x3.........xn?

A mission-critical production system has n stages that have to be performed sequentially; stage i is performed by machine Mi. Each machine Mi has a probability riof functioning reliably and a probability 1-riof failing (and the failures are independent). Therefore, if we implement each stage with a single machine, the probability that the whole system works is r1·r2···rn. To improve this probability we add redundancy, by having mi copies of the machine Mi that performs stage i. The probability that all mi copies fail simultaneously is only (1-ri)mi,so the probability that stage i is completed correctly is 1 − (1-ri)mi, and the probability that the whole system works isΠni=1(1-1-rimi).Each machine has a cost ci, and there is a total budget to buy machines. (Assume that B and ciare positive integers.) Given the probabilities r1·r2···rn, the costsc1,...,cn, and the budget find the redundanciesm1,...,mn that are within the available budget and that maximize the probability that the system works correctly.

Sequence alignment. When a new gene is discovered, a standard approach to understanding its function is to look through a database of known genes and find close matches. The closeness of two genes is measured by the extent to which they are aligned. To formalize this, think of a gene as being a long string over an alphabet ={A,C,G,T}. Consider two genes (strings) x=ATGCCand y=TACGCA. An alignment of x and y is a way of matching up these two strings by writing them in columns, for instance:

A-T-GCCTA-CGC

Here the “_” indicates a “gap.” The characters of each string must appear in order, and each column must contain a character from at least one of the strings. The score of an alignment is specified by a scoring matrixδof size (+1)×(+1), where the extra row and column are to accommodate gaps. For instance the preceding alignment has the following score:

δ(-T)+δ(A,A)+δ(T,-)+δ(G,G)+δ(C,C)+δ(C,A)

Give a dynamic programming algorithm that takes as input two strings X[1K n] and Y {1K m} and a scoring matrix δand returns the highest-scoring alignment. The running time should be O(mn) .

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