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Experimental Results

We did a large simulation where more than 1000 MSAs with 16, 20 and 30 sequences were randomly generated by simulating evolution. The length of each sequences was around 200. The score of the generated MSA was noted. The unaligned sequences were then given to two different algorithms, ClustalW [11] and ProbModel [15]. The resulting MSAs were scored and then given to our new algorithm to improve the alignments. The difference in score of the final alignment and the original alignment was noted. We also counted how often our algorithm improved the MSAs. The results are depicted in Table [*]. In all cases, our gap algorithm could improve at least $64 \%$ of the MSAs.
Figure: The columns ProbModel and ClustalW show the percentage of MSAs that have improved. The column before is the percentage of MSAs that scored at least as well as the real alignment before the improvement, the column after is the percentage after the gap algorithm was applied.
\begin{tabular}{\vert c\vert c\vert c\vert c\ver... & 22 $\%$ & 40 $\%$\\ \hline

Chantal Korostensky