Bioinformatics part 7 How to perform Global alignment 1

This Bioinformatics lecture explains how to perform global sequence alignment with sample example problem. Watch this video to understand the process of sequence alignment in details.
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In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns.
Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming.
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If we use SmithWaterman algorithm to align two sequences. At the end the traceback result is of the form
A C C G T -
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PLS What is the interpretations of the sequences obtained after the traceback.

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During trace back why do we stop in -2 insted of 0 ? Because the rule was From the higeht value back into the 0
Backtracking:
Starting from the bottom-right corner of the matrix, follow the path that leads to the top-left corner while keeping track of the alignment.
If you move diagonally and the characters at the current positions match/mismatch, add the match/mismatch score to the similarity score.
If you move vertically or horizontally, add the gap penalty to the similarity score.

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