Editing Sequence alignment (section)

===Techniques inspired by computer science===
[[File:A profile HMM modelling a multiple sequence alignment.png|thumb|A profile HMM modelling a multiple sequence alignment]]

A variety of general [[Optimization (mathematics)|optimization]] algorithms commonly used in computer science have also been applied to the multiple sequence alignment problem. [[Hidden Markov model]]s have been used to produce probability scores for a family of possible multiple sequence alignments for a given query set; although early HMM-based methods produced underwhelming performance, later applications have found them especially effective in detecting remotely related sequences because they are less susceptible to noise created by conservative or semiconservative substitutions.<ref name=karplus>{{cite journal | journal=Bioinformatics | volume=14 | issue=10 | pages= 846–856| year=1998 |author1=Karplus K |author2=Barrett C |author3=Hughey R. | title=Hidden Markov models for detecting remote protein homologies | pmid=9927713 | doi = 10.1093/bioinformatics/14.10.846 | doi-access=free | citeseerx=10.1.1.57.2762 }}</ref> [[Genetic algorithm]]s and [[simulated annealing]] have also been used in optimizing multiple sequence alignment scores as judged by a scoring function like the sum-of-pairs method. More complete details and software packages can be found in the main article [[multiple sequence alignment]].

The [[Burrows–Wheeler transform]] has been successfully applied to fast short read alignment in popular tools such as [[Bowtie (sequence analysis)|Bowtie]] and BWA. See [[FM-index]].