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Emerging concepts and caveats of global sequence clustering are reviewed.
The method-reciprocal smallest distance algorithm (rsd -relies on global sequence alignment and maximum likelihood estimation of evolutionarsd -reliess tondetect orthologlobalween two genomesequence
Edges represent the global sequence similarity.
Fig. 1 The comparison of ligand cluster-based similarity and global sequence similarity.
The protein global sequence and ligand 2D structure search were supported by ClustalO and CDK.
The protein sequence similarity was converted from the global sequence distance obtained by ClustalO [14].
Two proteins were linked when their global sequence similarity was above a threshold.
Their global sequence identity is only of 3.9 % while the local sequence similarity shows a 34%% in a segment of 55 aligned residues including 19 gaps.
Functional similarity is challenging to identify when global sequence and structure similarity is low.
Another null model, based on global sequence similarity, exploits the BLAST alignment bitscores.
The inclusion of highly variable non-coding regions generally precludes global sequence alignment across land plants, or even angiosperms.
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