Parametric recomputing in alignment graphs
© Springer-Verlag Berlin Heidelberg 1994. DNA/protein sequence alignments in computational molecular biology depend heavily on the settings of penalties for substitutions, insertions/ deletions and gaps. Inappropriate choice of parameters causes irrelevant matches (“noise”) to be reported, thus obscuring biologically relevant matches. In practice, biologists frequently compare sequences in a few iterations, starting from a vague idea about appropriate parameters, then refining parameters to reduce noise. This procedure often helps to delineate biologically interesting similarities and to substantially reduce laborious analysis. This paper provides a computational underpinning for such iterative noise filtration in alignment graphs. Our main results assume that a preliminary “noisy” alignment, computed with reasonable but ad hoc parameters, is given; the problem is to modify the parameters to reduce noise. We present fast algorithms to refine penalty parameters and describe an application of these algorithms.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Parametric recomputing in alignment graphs.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
807 LNCS, 87-101.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/4012