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We used two different approaches to alignment (direct optimization and homology-extension alignment) and tree building (parsimony and ML), to illustrate the effect of very different data treatments.
Recently, we proposed an alternative approach to alignment-free sequence comparison.
Approaches to range alignment by minimum entropy of synthetic range profile are discussed in [6 8].
The two approaches to local alignment are not mutually exclusive.
Approaches to whole-genome alignment typically reduce the alignment search space using anchoring heuristics [17] [22] or banded dynamic programming [23].
The structure-based sequence alignments are correlated with the PCAIN-based anchored alignments to estimate the efficacy of the PCAIN approach to sequence alignment (Figure 4C).
We used several approaches to assess our alignment accuracy and found similar results.
An alternative, more statistically justified approach to assess alignment uncertainty is the use of probabilistic evolutionary models for joint estimation of phylogeny and alignment.
An approach to lens alignment by the magnetic field reconstruction method was developed and successfully realized.
You will even look where your ideas intersect with an opposing approach to create alignment instead of competition.
Ward and Bussemaker [22] and Xie et al. [29] suggested using both alignment-based and alignment-independent approaches to identify evolutionary conserved regions.
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