Aligning multiple DNA or protein sequences is a fundamental step in the analyses of phytogeny, homology and molecular structure. Heuristic algorithms are applied because optimal multiple sequence alignment is prohibitively expensive. Heuristic alignment algorithms represent a practical trade-off between speed and accuracy, but they can be improved. We present EVALYN (EVolved ALYNments), a novel approach to multiple sequence alignment in which sequences are progressively aligned based on a guide tree optimized by a genetic algorithm. We hypothesize that a genetic algorithm can find better guide trees than traditional, deterministic clustering algorithms. We compare our novel evolutionary approach to CLUSTAL W and find that EVALYN performs consistently and significantly better as measured by a common alignment scoring technique. Additionally, we hypothesize that evolutionary guide tree optimization is inherently efficient and has less time complexity than the commonly-used neighbor-joining algorithm. We present a compelling analysis in support of this scalability hypothesis. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Sheneman, L., & Foster, J. A. (2004). Evolving better multiple sequence alignments. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 449–460. https://doi.org/10.1007/978-3-540-24854-5_45
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