Although several mathematical models have been developed to solve the alignment optimization problem, most of them emphasize either horizontal or vertical alignments, and only yield a suboptimal solution to the problem.
Heuristic methods aim to solve the alignment problem faster without providing a confidence value (Ay and Kahveci, 2010; Bruckner et al., 2009; Liao et al., 2009).
Seed and extend strategy is one of the most popular heuristic algorithms; here we use it to solve spliced alignment problem.
In this paper, we extended CNetQ, a novel network querying method based on the conditional random fields model, to solve network alignment problem, by adopting an iterative bi-directional mapping strategy.
Because structure prediction and alignment of RNAs depend on each other, Sankoff's approach solves the alignment and folding problem simultaneously.
We incorporated these ideas and additional refinements to solve two common alignment problems: glocal alignment, solved with OPTIMA, where an entire map is aligned to a subsequence of a second (typically in silico) map; and overlap alignment, solved with OPTIMA-Overlap, where the end of one map is aligned to the beginning of another.
This paper focuses on solving vertical alignment optimization problem using meta-heuristic algorithms.
With respect to the pairwise approach, the multi-view approach solved the alignment failures of the Cherub and the Hurricane datasets by exploiting the cumulative information available.
The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis.
While a compass built into a future iPhone model might solve the directional alignment issue, GPS within buildings will most likely remain a problem for quite some time.
