title: Alignment uncertainty and genomic analysis.
abstract: The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignment. However, in a comparative genomics study, the same statistical methods are applied repeatedly on thousands of genes, many of which will be difficult to align. Using genomic data from seven yeast species, we show that uncertainty in the alignment can lead to several problems, including different alignment methods resulting in different conclusions.
authors.affiliation: Section of Ecology, Behavior and Evolution, University of California, San Diego, La Jolla, CA 92093, USA.
authors.forename: Karen M
authors.initials: KM
authors.lastname: Wong
authors.forename: Marc A
authors.initials: MA
authors.lastname: Suchard
authors.forename: John P
authors.initials: JP
authors.lastname: Huelsenbeck
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001483:Base Sequence; D019295:Computational Biology; D019143:Evolution, Molecular; D016681:Genome, Fungal; D023281:Genomics; D015233:Models, Statistical; D009010:Monte Carlo Method; D016366:Open Reading Frames; D010802:Phylogeny; D012440:Saccharomyces / Q000235:genetics*; D012641:Selection, Genetic; D016415:Sequence Alignment / Q000379:methods*; D012984:Software; D035501:Uncertainty