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Modelling-Alignment for Non-random Sequences David R. Powell1,2,

Summary: Modelling-Alignment for Non-random Sequences
David R. Powell1,2,
, Lloyd Allison1
, and Trevor I. Dix1,2
School of Computer Science and Software Engineering,
Monash University, Australia 3800
Victorian Bioinformatics Consortium
{powell, lloyd, trevor}@csse.monash.edu.au
Abstract. Populations of biased, non-random sequences may cause standard
alignment algorithms to yield false-positive matches and false-negative misses.
A standard significance test based on the shuffling of sequences is a partial solu-
tion, applicable to populations that can be described by simple models. Masking-
out low information content intervals throws information away. We describe a
new and general method, modelling-alignment: Population models are incorpo-
rated into the alignment process, which can (and should) lead to changes in the
rank-order of matches between a query sequence and a collection of sequences,
compared to results from standard algorithms. The new method is general and
places very few conditions on the nature of the models that can be used with it. We


Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University


Collections: Computer Technologies and Information Sciences