Linear-Time Algorithms for Computing Maximum-Density Sequence Segments with Bioinformatics Applications

TL;DR

This paper introduces efficient linear-time algorithms for finding maximum-density segments in biomolecular sequences, improving computational speed and handling more general cases than previous methods.

Contribution

It presents the first linear-time algorithms for the maximum-density segment problem with both lower and upper width bounds, extending previous work limited to unbounded U.

Findings

Achieved O(n) time complexity for unbounded U case.

Developed O(n + n log(U-L+1)) algorithm for bounded U and L.

Improved computational efficiency over previous algorithms.

Abstract

We study an abstract optimization problem arising from biomolecular sequence analysis. For a sequence A of pairs (a_i,w_i) for i = 1,..,n and w_i>0, a segment A(i,j) is a consecutive subsequence of A starting with index i and ending with index j. The width of A(i,j) is w(i,j) = sum_{i <= k <= j} w_k, and the density is (sum_{i<= k <= j} a_k)/ w(i,j). The maximum-density segment problem takes A and two values L and U as input and asks for a segment of A with the largest possible density among those of width at least L and at most U. When U is unbounded, we provide a relatively simple, O(n)-time algorithm, improving upon the O(n \log L)-time algorithm by Lin, Jiang and Chao. When both L and U are specified, there are no previous nontrivial results. We solve the problem in O(n) time if w_i=1 for all i, and more generally in O(n+n\log(U-L+1)) time when w_i>=1 for all i.