Many alignment algorithm for DNA or protein sequences were proposed in 1980s, nonetheless, multiple sequence alignment is still a challenging question because of its computing intensity, which manifests with the advancement of the next-generation sequencing.
Many network alignment algorithms have been proposed in recent years and most of them focus on the pairwise alignment of PPI networks.
There are many network alignment algorithms published in literature, e.g., Græmlin [ 8], MRF based method [ 9], MNAligner [ 10], IsoRank [ 11, 12], IsoRankN [ 13], MI-GRAAL [ 14].
MUSCLE was chosen from among many different sequence alignment algorithms for development of the SCOPPI database.
Fourth, there are many other great alignment algorithms [ 2, 4- 12, 14, 19, 19, 20] that we did not compare.
Many different sequence alignment algorithms are available to perform such a complementarity search, but they are not optimal for the purpose of this process step by default.
Considering that pair-HMMs have been widely adopted by many modern multiple sequence alignment algorithms [ 9– 11], the proposed scheme has potentials to further improve the current state-of-the-art.
Many sequence alignment and protein structure alignment algorithms have been developed assuming linear data representations and as such are not able to detect circular permutations between proteins.
miRNAs are relatively short (~21 nt) which means that in many cases they can be aligned quite accurately using multiple alignment algorithms such as ClustalW [ 25] and MUSCLE [ 31].
This section illustrates the MP-Align alignment algorithm.
