Basic Bioinformatics Algorithms - 12 August 2011

This section contains commonly used bioinformatics algorithms.

Needleman–Wunsch algorithm: find global alignment between two sequences
Smith–Waterman algorithm: find local sequence alignment
Basic Local Alignment Search Tool also known as BLAST: an algorithm for comparing primary biological sequence information
Dynamic time warping: measure similarity between two sequences which may vary in time or speed
Hirschberg's algorithm: finds the least cost sequence alignment between two sequences, as measured by their Levenshtein distance

Chou–Fasman method: The Chou–Fasman method are an empirical technique for the prediction of secondary structures in proteins, originally developed in the 1970s.
Nussinov Algorithm: Nussinov algorithm is an algorithm to predict possible RNA secondary structure (folding), discovering parts that have complementary sequences. Example Of Nussinov Algorithm.
Kabsch algorithm: calculate the optimal alignment of two sets of points in order to compute the root mean squared deviation between two protein structures.

Baum–Welch algorithm: compute maximum likelihood estimates and posterior mode estimates for the parameters of a hidden markov model
Forward-backward algorithm a dynamic programming algorithm for computing the probability of a particular observation sequence
Viterbi algorithm: find the most likely sequence of hidden states in a hidden markov model

UPGMA: UPGMA (Unweighted Pair Group Method with Arithmetic Mean) is a simple agglomerative or hierarchical clustering method used in bioinformatics for the creation of phenetic trees (phenograms).
Neighbor Joining: In bioinformatics, neighbor joining is a bottom-up clustering method for the creation of phenetic trees (phenograms), created by Naruya Saitou and Masatoshi Nei.

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