The steps include: (1)Perform pair-wise alignments of all of the sequences;(2)use the alis gnment scores to produce a phylogenetic tree (for an explanation of the neighbor-joining method that is used); and( 3)align the sequences sequentially, guided by the phylogenetic relationships indicated by the tree The initial alignments used to produce the guide tree may be obtained by a fast k-tuple or pattern finding approach similar to Fasta that is useful for many sequences, or a slower, full dynamic programming method may be used An enhanced dynamic programming alignment algorithm(Myers and Miller 1988 )is used to obtain optimal alignment scores
• The steps include: (1) Perform pair-wise alignments of all of the sequences; (2) use the alignment scores to produce a phylogenetic tree (for an explanation of the neighbor-joining method that is used); and (3) align the sequences sequentially, guided by the phylogenetic relationships indicated by the tree. • The initial alignments used to produce the guide tree may be obtained by a fast k-tuple or patternfinding approach similar to FASTA that is useful for many sequences, or a slower, full dynamic programming method may be used. • An enhanced dynamic programming alignment algorithm (Myers and Miller 1988) is used to obtain optimal alignment scores
A Calculation of sequence weights Weighting factor 0.2 A0.2+0.3/2=0.35 0.3 Weighting 0.1 B0.1+0.3/2=0.25 scheme used by 0.5 CLUSTALW c0.5 B. Use of sequence weights Column in alignment 1 Sequence A(weight a) Sequence B(weight b) Column in alignment 2 Sequence(weight c) Sequence D(weight d) Score for matching these two column in an msa [axc x score( K, L )+ a x d x score(K,v)+ b cx score(, L)+ b x d x score(, V)1/4
Weighting scheme used by CLUSTALW