Computational genomics involves comparing sequences based on "similarity" for detecting evolutionary and functional relationships. Until very recently, available portions of the human genome sequence (and that of other species) were fairly short and sparse. Most sequencing effort was focused on genes and other short units; similarity between such sequences was measured based on character level differences. However with the advent of whole genome sequencing technology there is emerging consensus that the measure of similarity between long genome sequences must capture the rearrangements of large segments found in abundance in the human genome. In this paper, we abstract the general problem of computing sequence similarity in the presence of segment rearrangements. This problem is closely related to computing the smallest grammar for a string or the block edit distance between two strings. Our problem, like these other problems, is NP hard. Our main result here is a simple O(1) factor approximation algorithm for this problem. In contrast, best known approximations for the related problems are factor Ω(log n) off from the optimal. Our algorithm works in linear time, and in one pass. In proving our result, we relate sequence similarity measures based on different segment rearrangements, to each other, tight up to constant factors.
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