A simulated annealing algorithm for finding consensus sequences.
BACKGROUND
A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules.


RESULTS
This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.
Keith
Jonathan M
JM
Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.
Adams
Peter
P
Bryant
Darryn
D
Kroese
Dirk P
DP
Mitchelson
Keith R
KR
Cochran
Duncan A E
DA
Lala
Gita H
GH
D000465:Algorithms; D003198:Computer Simulation; D016384:Consensus Sequence / Q000235:genetics*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*