Fast Statistical Alignment

• Published in: PLoS computational biology Vol. 5; no. 5; p. e1000392
• Main Authors: Bradley, Robert K., Roberts, Adam, Smoot, Michael, Juvekar, Sudeep, Do, Jaeyoung, Dewey, Colin, Holmes, Ian, Pachter, Lior
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2009; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Amino Acid Sequence; Animals; Annealing; Applications software; Artificial Intelligence; Base Sequence; Bioinformatics; Computational Biology/Comparative Sequence Analysis; Computer Science; Data Interpretation, Statistical; Databases, Genetic; Editing; Genetic algorithms; Genetics and Genomics/Bioinformatics; Genomes; Humans; Markov Chains; Mathematics; Models, Genetic; Molecular Sequence Data; Parameter estimation; Phylogenetics; Proteins; Sensitivity and Specificity; Sequence Alignment - methods; Sequence Analysis; Software; Statistical models; Studies; Teaching methods; United States; Amino Acid Sequence; Data Interpretation, Statistical; Markov Chains; Sensitivity & Specificity; Artificial Intelligence; Humans; Databases, Genetic; Molecular Sequence Data; Sequence Alignment; Algorithms; Animals; Base Sequence; Models, Genetic; Software; Sequence Analysis
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000392

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment--previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches--yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/.