The Mathematical Institute, University of Oxford, Eprints Archive

A Monte Carlo Approach to the Fluctuation Problem in Optimal Alignments of Random Strings

Amsalu, Saba and Hauser, Raphael and Matzinger, Heinrich (2012) A Monte Carlo Approach to the Fluctuation Problem in Optimal Alignments of Random Strings. Technical Report. Unspecified. (Submitted)



The problem of determining the correct order of fluctuation of the optimal alignment score of two random strings of length $n$ has been open for several decades. It is known [12] that the biased expected effect of a random letter-change on the optimal score implies an order of fluctuation linear in √$n$. However, in many situations where such a biased effect is observed empirically, it has been impossible to prove analytically. The main result of this paper shows that when the rescaled-limit of the optimal alignment score increases in a certain direction, then the biased effect exists. On the basis of this result one can quantify a confidence level for the existence of such a biased effect and hence of an order √$n$ fluctuation based on simulation of optimal alignments scores. This is an important step forward, as the correct order of fluctuation was previously known only for certain special distributions [12],[13],[5],[10]. To illustrate the usefulness of our new methodology, we apply it to optimal alignments of strings written in the DNA-alphabet. As scoring function, we use the BLASTZ default-substitution matrix together with a realistic gap penalty. BLASTZ is one of the most widely used sequence alignment methodologies in bioinformatics. For this DNA-setting, we show that with a high level of confidence, the fluctuation of the optimal alignment score is of order Θ(√$n$). An important special case of optimal alignment score is the Longest Common Subsequence (LCS) of random strings. For binary sequences with equiprobably symbols the question of the fluctuation of the LCS remains open. The symmetry in that case does not allow for our method. On the other hand, in real-life DNA sequences, it is not the case that all letters occur with the same frequency. So, for many real life situations, our method allows to determine the order of the fluctuation up to a high confidence level.

Item Type:Technical Report (Technical Report)
Subjects:O - Z > Statistics
O - Z > Operations research, mathematical programming
O - Z > Probability theory and stochastic processes
H - N > Numerical analysis
Research Groups:Numerical Analysis Group
ID Code:1624
Deposited By: Lotti Ekert
Deposited On:24 Nov 2012 08:56
Last Modified:29 May 2015 19:19

Repository Staff Only: item control page