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Specifically, a kernel target alignment approach has been considered as the fourth ranking method (it will be called KA for future references) [28].
The described alignment approach has been integrated into the TRACKER software system.
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Indeed, every multi-alignment approach has to address two major problems: (a) a procedure has to be defined that, in principle, associates a score to every possible alignment of any given sequence set and (b) an optimization algorithm has to be designed that finds optimal (or near-optimal) alignments relative to that scoring procedure.
Recently, some new alignment approaches have been developed such as POA [ 5], MUSCLE [ 6] or PROBCONS [ 7].
The two alignment approaches had no effect on topology and limited effect on nodal support (fig. 7 and supplementary fig. S3 A, Supplementary Material online).
Based on multiple alignments instead of pairwise alignments, our RNAz-based approach has a significantly increased sensitivity and specificity.
Traditionally the most popular approach has been the progressive alignment procedure [14], which exploits the fact that homologous sequences are evolutionarily related.
In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments.
This approach has been implemented in the program MSASA (Multiple Sequence Alignment by Simulated Annealing).
A general approach has been developed for on-site identification of the actual configuration (alignment) of multi-bearing rotor systems (e.g., a turbogenerator unit).
This approach has a constraint, as it follows a greedy strategy: it finds a valid alignment if one exists, but this might not be the best alignment.
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