Computing maximum-scoring segments optimally

TL;DR: In this article, the space lower bound for range maximum-sum segment queries was improved to 1.89113n − ε(lg n) bits, for sufficiently large values of n.

TL;DR: The algorithm has the capability to process the input sequence in an online manner, which is an important feature for dealing with genome-scale sequences and shows how to exploit the sparsity of S and solve the maximum-density segment problem for S in O(m) time.

TL;DR: This work provides a linear-time algorithm for a special class of graphs corresponding to elastic-degenerate strings, one of pangenome representations, and provides a fixed-parameter tractable algorithm for directed acyclic graphs with a special path decomposition of a limited width.

TL;DR: Generalization from sequences to graphs allows the algorithm to be used on pangenome graphs representing several related genomes and can be seen as a common abstraction for several biological problems on pangenomes, including searching for CpG islands, ChIP-seq data analysis, analysis of region enrichment for functional elements, or simple chaining problems.

TL;DR: In this paper, the authors solved the problem in O(n) time by exploiting the sparsity of the input biomolecular sequences and solving the maximum-density segment problem in an online manner.

TL;DR: This paper describes a general dynamic programming-like algorithm specifically designed to optimize the number and length of segments with constrained length in a given protein sequence, and presents the detailed description of MaxSubSeq.

TL;DR: The algorithm is optimal for k = \Omega(n \log^2 n) and improves over the previously best known result for any value of the user-defined parameter k < 1, resulting in fast algorithms as well.

TL;DR: The K-maximum subarray problem is to find the K subarrays with largest sums, and the time complexity is improved from O(min K+n\log^2 n, n\sqrt{K}\}) to O(nlog K + K2) for K ≤ n.