Abstract
BackgroundAutomated software tools for multiple alignment often fail to produce biologically meaningful results. In such situations, expert knowledge can help to improve the quality of alignments.ResultsHerein, we describe a semi-automatic version of the alignment program DIALIGN that can take pre-defined constraints into account. It is possible for the user to specify parts of the sequences that are assumed to be homologous and should therefore be aligned to each other. Our software program can use these sites as anchor points by creating a multiple alignment respecting these constraints. This way, our alignment method can produce alignments that are biologically more meaningful than alignments produced by fully automated procedures. As a demonstration of how our method works, we apply our approach to genomic sequences around the Hox gene cluster and to a set of DNA-binding proteins. As a by-product, we obtain insights about the performance of the greedy algorithm that our program uses for multiple alignment and about the underlying objective function. This information will be useful for the further development of DIALIGN. The described alignment approach has been integrated into the TRACKER software system.
Highlights
Automated software tools for multiple alignment often fail to produce biologically meaningful results
Multiple sequence alignment is a crucial prerequisite for biological sequence data analysis, and a large number of multi-alignment programs have been developed during the last twenty years
Most methods use a welldefined objective function assigning numerical quality score to every possible output alignment of an input sequence set and try to find an optimal or near-optimal alignment according to this objective function
Summary
We describe a semi-automatic version of the alignment program DIALIGN that can take pre-defined constraints into account. Our software program can use these sites as anchor points by creating a multiple alignment respecting these constraints. This way, our alignment method can produce alignments that are biologically more meaningful than alignments produced by fully automated procedures. As a by-product, we obtain insights about the performance of the greedy algorithm that our program uses for multiple alignment and about the underlying objective function. This information will be useful for the further development of DIALIGN. The described alignment approach has been integrated into the TRACKER software system
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