The algorithm aligns ZF repeat unit sequences by maximizing the alignment score, as is typical for nucleotide and protein sequence alignment.
Yet, the alignment shows less coincidences, and the HMMER algorithm introduced several long gaps to maximize the alignment.
These methods use different heuristic algorithms for maximizing the alignment, return a score per alignment of two proteins, and the statistical significance of the pairing [10], [11].
Multiple sequence alignments were carried out in ClustalX [ 30], and edited manually to maximize the alignment.
All these alignments were cleaned and analyzed in order to maximize the alignment positions tested.
That is, given a sequence and a template, we can find their optimal alignment by maximizing the alignment potential function.
Integer quadratic programming is used to maximize the alignment score among all feasible combinations of matching scores.
For each reference sequence, the soybean predicted peptide among hits with significance < 1e-100 and percent identities > 90% over the alignment maximizing the alignment score was attributed as best match.
However, if the alignments of the flanking regions overlap, combining two local alignments becomes complicated, and an optimal jump from one local alignment to another must be found that is, a gap must be introduced to maximize the alignment score.
