Efficient parallel algorithms for string editing and related problems
SIAM Journal on Computing
A space efficient algorithm for finding the best nonoverlapping alignment score
Theoretical Computer Science
An Algorithm for Locating Nonoverlapping Regions of Maximum Alignment Score
SIAM Journal on Computing
Las Vegas algorithms for gene recognition: suboptimal and error-tolerant spliced alignment
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
SIAM Journal on Computing
On the common substring alignment problem
Journal of Algorithms
Notes on searching in multidimensional monotone arrays
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
| Hi-index | 0.00 |
Gene structure prediction is one of the most important problems in computational molecular biology. A combinatorial approach to the problem, denoted Gene Prediction via Spliced Alignment, was introduced by Gelfand, Mironov and Pevzner [5]. The method works by finding a set of blocks in a source genomic sequence S whose concatenation (splicing) fits a target gene T belonging to a homologous species. Let S,T and the candidate exons be sequences of size O(n). The innovative algorithm described in [5] yields an O(n3) result for spliced alignment, regardless of filtration mode. In this paper we suggest a new algorithm which targets the case where filtering has been applied to the data, resulting in a set of O(n) candidate exon blocks. Our algorithm yields an $O(n^2 \sqrt{n})$ solution for this case.