67,512 research outputs found
Sequence alignment, mutual information, and dissimilarity measures for constructing phylogenies
Existing sequence alignment algorithms use heuristic scoring schemes which
cannot be used as objective distance metrics. Therefore one relies on measures
like the p- or log-det distances, or makes explicit, and often simplistic,
assumptions about sequence evolution. Information theory provides an
alternative, in the form of mutual information (MI) which is, in principle, an
objective and model independent similarity measure. MI can be estimated by
concatenating and zipping sequences, yielding thereby the "normalized
compression distance". So far this has produced promising results, but with
uncontrolled errors. We describe a simple approach to get robust estimates of
MI from global pairwise alignments. Using standard alignment algorithms, this
gives for animal mitochondrial DNA estimates that are strikingly close to
estimates obtained from the alignment free methods mentioned above. Our main
result uses algorithmic (Kolmogorov) information theory, but we show that
similar results can also be obtained from Shannon theory. Due to the fact that
it is not additive, normalized compression distance is not an optimal metric
for phylogenetics, but we propose a simple modification that overcomes the
issue of additivity. We test several versions of our MI based distance measures
on a large number of randomly chosen quartets and demonstrate that they all
perform better than traditional measures like the Kimura or log-det (resp.
paralinear) distances. Even a simplified version based on single letter Shannon
entropies, which can be easily incorporated in existing software packages, gave
superior results throughout the entire animal kingdom. But we see the main
virtue of our approach in a more general way. For example, it can also help to
judge the relative merits of different alignment algorithms, by estimating the
significance of specific alignments.Comment: 19 pages + 16 pages of supplementary materia
A recursive paradigm for aligning observed behavior of large structured process models
The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split
the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft
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