A tutorial of recent developments in the seeding of local alignment.
We review recent results on local alignment. We begin with a review of classical methods and early heuristic methods, and then focus on more recent work on the seeding of local alignment. We show that these techniques give a vast improvement in both sensitivity and specificity over previous methods, and can achieve sensitivity at the level of classical algorithms while requiring orders of magnitude less runtime.
Brown
Daniel G
DG
Department of Computer Science, University of Waterloo, Waterloo, ON, Canada, N2L 3G1, Canada. browndg@math.waterloo.ca
Li
Ming
M
Ma
Bin
B
D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods* / Q000639:trends; D017421:Sequence Analysis / Q000379:methods* / Q000639:trends; D017385:Sequence Homology