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3.1: What Have We Learned?

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    In this section we explored alignment algorithms beyond global alignment. We began by reviewing our use of dynamic programming to solve global alignment problems using the Needleman-Wunsch algorithm. We then the explored alternatives of local (Smith-Waterman) and semi-global alignments. We then discussed using hash function to match exact strings in linear time (Karp-Rabin) as well as doing a neighborhood search, investigating similar sequences in probabilistic linear time (pigeonhole principle, combs, 2-hit blast, random projections). We have also addressed using pre-processing for linear time string matching, as well as the probabilistic background for sequence alignment.

    This page titled 3.1: What Have We Learned? is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.