Suboptimal Local Alignments Across Multiple Scoring Schemes

Sequence alignment algorithms have a long standing tradition in bioinformatics. In this paper, we formulate an extension to existing local alignment algorithms: local alignments across multiple scoring functions. For this purpose, we use the Waterman-Eggert algorithm for suboptimal local alignments as template and introduce two new features therein: 1) an alignment of two strings over a set of score functions and 2) a switch cost function δ for penalizing jumps into a different scoring scheme within an alignment.

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