title: Multiple sequence alignment accuracy and evolutionary distance estimation.
abstract: BACKGROUND
  Sequence alignment is a common tool in bioinformatics and comparative genomics. It is generally assumed that multiple sequence alignment yields better results than pair wise sequence alignment, but this assumption has rarely been tested, and never with the control provided by simulation analysis. This study used sequence simulation to examine the gain in accuracy of adding a third sequence to a pair wise alignment, particularly concentrating on how the phylogenetic position of the additional sequence relative to the first pair changes the accuracy of the initial pair's alignment as well as their estimated evolutionary distance.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center for Evolutionary Functional Genomics, The Biodesign Institute, and the School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, USA. msr@asu.edu
authors.forename: Michael S
authors.initials: MS
authors.lastname: Rosenberg
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003205:Computing Methodologies; D030562:Databases, Protein; D005069:Evaluation Studies as Topic; D019143:Evolution, Molecular; D020869:Gene Expression Profiling; D016678:Genome; D006801:Humans; D011506:Proteins; D015203:Reproducibility of Results; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D020539:Sequence Analysis, Protein / Q000379:methods; D012689:Sequence Homology, Nucleic Acid; D012984:Software
chemical_list: D011506:Proteins