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An overview of various algorithms and data structures used in computational biology, including exhaustive, greedy, recursion, dynamic programming, randomization, and heuristic algorithms, as well as hash tables, arrays, suffix tries, and search trees. Learn about their characteristics, advantages, and applications.
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Exhaustive algorithm accurate, A LOT of time and memory looks at every possibility greedy algorithm follows problem solving heuristic of making optimal choices at each stage. Hopefully finds the global optimum. not as thorough, uses shortcut recursion calls itself Dynamic Programming break problem into sub problems ex. NW, SW, BLAST Randomization uses random number to make decision stochastic randomness affects either runtime or output heuristic decision may not be optimal but is fast enough or close enough to optimal greedy algorithm exact vs approximate BLAST = approximiate deterministic vs stochastic Deterministic - same decision at every step, always same output given input Stochastic - depends on random but informed choices, can have different results from different runs NW global, exhaustive, pairwise SW local exhaustive pairwise
local, approximate, pairwise CLUSTALW global, approximate, multiple BOWTIE approximate, pairwise Hash table fixed length, fast Creating hashtable: time O(n), memory O(n) Searching hash table: time O(1), memory O(n) array Linear search, slow, errors Time O(n) Memory O(n) suffix trie time O(n) memory Search tree Memory, variable length, fast Used to store strings over an alphabet, nodes and leaves Hash memory