A word-oriented approach to alignment validation.
BACKGROUND
Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences.


RESULTS
Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families.


BACKGROUND
On request from the authors.
Beiko
Robert G
RG
ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland Brisbane, Qld 4072, Australia.
Chan
Cheong Xin
CX
Ragan
Mark A
MA
D000465:Algorithms; D001185:Artificial Intelligence; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D012660:Semantics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins