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Aligning sequences with non-Affine Gap penalty: Plains Algorithm, a Practical Implementation, and its biological Applications in Comparative genomics

In this paper, we consider PLAINS, an algorithm that provides efficient alignment over DNA sequences using piecewise-linear gap penalties that closely approximate more general and meaningful gap-functions. The innovations of PLAINS are fourfold. First, when the number of parts to a piecewise-linear gap function is fixed, PLAINS uses linear space in the worst case, and obtains an alignment that is provably correct under its memory constraints, and thus has an asymptotic complexity similar to the currently best implementations of Smith-Waterman. Second, we score alignments in PLAINS based on important segment pairs; optimize gap parameters based on interspecies alignments, and thus, identify more significant correlations in comparison to other similar algorithms. Third, we describe a practical implementation of PLAINS in the Valis multi-scripting environment with powerful and intuitive visualization interfaces, which allows users to view the alignments with a natural multiple-scale color grid scheme. Fourth, and most importantly, we have evaluated the biological utility of PLAINS using extensive lab results; we report the result of comparing a human sequence to a fugu sequence, where PLAINS was capable of finding more orthologous exon correlations than similar alignment tools.