Alignment of Minisatellite Maps Based on Run-Length Encoding Scheme

Abstract

Subsequent duplication events are responsible for the evolution of the minisatellite maps. Alignment of two minisatellite maps should therefore take these duplication events into account, in addition to the well-known edit operations. All algorithms for computing an optimal alignment of two maps, including the one presented here, first deduce the costs of optimal duplication scenarios for all substrings of the given maps. Then, they incorporate the pre-computed costs in the alignment recurrence. However, all previous algorithms addressing this problem are dependent on the number of distinct map units (map alphabet) and do not fully make use of the repetitiveness of the map units. In this paper, we present an algorithm that remedies these shortcomings: our algorithm is alphabet-independent and is based on the run-length encoding scheme. It is the fastest in theory, and in practice as well, as shown by experimental results. Furthermore, our alignment model is more general than that of the previous algorithms, and captures better the duplication mechanism. Using our algorithm, we derive a quantitative evidence that there is a directional bias in the growth of minisatellites of the MSY1 dataset.

Bellman’s GAP—a language and compiler for dynamic programming in sequence analysis

“…The latter provides a non-ambiguous version of the algorithm by Abouelhoda et al (2009), and is technically interesting because it combines a two-track problem (minisatellite aligment) with a non-trivial single-track sub-problem (reconstruction of duplication histories).…”

WAMI: a web server for the analysis of minisatellite maps

“…Given a cost for each operation, an optimal alignment is the one of minimum cost. An efficient algorithm for finding optimal map alignments is ARLEM [18]. …”

“…It is based on a compression technique to save redundant computations and its speed is not affected by any increase in the number of types. In [18], we reported that ARLEM is 18 to 24 times faster than the previously available algorithm MS_ALIGN, using real and artificial datasets. For further speed-up, the options for computing phylogeny, analyzing structure variations, and duplication dynamics run in parallel over a computer cluster of four nodes, where each node contains two Quadcore CPUs (2.5 GHz each) with 64 GB RAM.…”

“…To our surprise, most did not make use of the recent advancement in the bioinformatics methods developed for pairwise map comparison [17,18]. We think this situation is mainly due to the lack of both web-servers and open source tools performing the aforementioned tasks.…”

Efficient edit distance with duplications and contractions

“…Run-length encoding was also used by Bérard et al [8], who proposed an $O(n^3+|\Sigma \left|{ñ}^3\right)$ time and $O(n^2+|\Sigma \left|{ñ}^2\right)$ space algorithm. Abouelhoda et al [9] gave an algorithm with an alphabet size independent time and space complexities of $O(n^2+n{ñ}^2)$ and O ( n 2 ), respectively. A detailed comparison between the different problem models appears in Section “A comparison with previous works”.…”