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Multiple sequence alignment accuracy and evolutionary distance estimation

DOI: 10.1186/1471-2105-6-278

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Abstract:

The maximal gain in alignment accuracy was found not when the third sequence is directly intermediate between the initial two sequences, but rather when it perfectly subdivides the branch leading from the root of the tree to one of the original sequences (making it half as close to one sequence as the other). Evolutionary distance estimation in the multiple alignment framework, however, is largely unrelated to alignment accuracy and rather is dependent on the position of the third sequence; the closer the branch leading to the third sequence is to the root of the tree, the larger the estimated distance between the first two sequences.The bias in distance estimation appears to be a direct result of the standard greedy progressive algorithm used by many multiple alignment methods. These results have implications for choosing new taxa and genomes to sequence when resources are limited.DNA sequence alignment is a common step in molecular evolutionary analysis. Aligned sequences are used for many purposes, including estimation of patterns of divergence, selection, the tempo and mode of evolutionary change, identification of functional elements and constraints, and phylogenetic history, just to name a few. Alignments are a hypothesis of site homology; as evolutionary distance among sequences increases, alignments are known to become less accurate [1-7]. The effect of alignment accuracy on downstream analysis in comparative genomics and bioinformatics is largely an unexplored topic, although some empirical studies have attempted to examine this with respect to functional element identification [8,9] and phylogenetic analysis [10-16].Multiple sequence alignment, the alignment of more than two sequences, is generally thought to lead to more accurate alignments than simple pair wise alignments [4]. There are numerous approaches to multiple alignment, although most are based in some way on a progressive alignment algorithm [17,18] where similar sequences are aligned first and

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