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.
The restoration is unlikely to solve the riddles of Stonehenge's original purpose or the precise reasons behind its alignment to the rising of the sun at the summer solstice.
Something is out of alignment, to be sure.
A suggested way to solve this problem is to consider alignment as a random variable, a strategy developed into the statistical alignment procedure [ 49, 50].
References [22] and [23] suggest the method that minimizes entropy of average range profile, and the eigenvector method can be utilized to solve the range alignment and phase correction of random pulses.
Such tools, recently published, enable a user to solve classical problems related to RNA research: constructing 'structural' multiple alignments, inferring complete structures and structural motifs from RNA alignments, or searching structural homology in genomic databases.
Because structure prediction and alignment of RNAs depend on each other, Sankoff's approach solves the alignment and folding problem simultaneously.
