Abstract
We describe a supervised learning approach to resolve difficulties in finding biologically significant local alignments. It was noticed that the O(n 2) algorithm by Smith-Waterman, the prevalent tool for computing local sequence alignment, often outputs long, meaningless alignments while ignoring shorter, biologically significant ones. Arslan et. al. proposed an O(n 2 log n) algorithm which outputs a normalized local alignment that maximizes the degree of similarity rather than the total similarity score. Given a properly selected normalization parameter, the algorithm can discover significant alignments that would be missed by the Smith-Waterman algorithm. Unfortunately, determining a proper normalization parameter requires repeated executions with different parameter values and expert feedback to determine the usefulness of the alignments. We propose a learning approach that uses existing biologically significant alignments to learn parameters for intelligently processing sub-optimal Smith-Waterman alignments. Our algorithm runs in O(n 2) time and can discover biologically significant alignments without requiring expert feedback to produce meaningful results.
Supported by a grant from Rensselaer Polytechnic Institute.
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Breimer, E., Goldberg, M. (2002). Learning Significant Alignments: An Alternative to Normalized Local Alignment. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_6
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DOI: https://doi.org/10.1007/3-540-48050-1_6
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