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Multiple sequence alignment (MSA) is the process or the result of sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. These alignments are used to infer evolutionary relationships via phylogenetic analysis and can highlight homologous features between sequences.
Multiple sequence alignment is an extension of pairwise alignment to incorporate more than two sequences at a time. Multiple alignment methods try to align all of the sequences in a given query set. Multiple alignments are often used in identifying conserved sequence regions across a group of sequences hypothesized to be evolutionarily related.
The rest of this article is focused on only multiple global alignments of homologous proteins. The first two are a natural consequence of most representations of alignments and their annotation being human-unreadable and best portrayed in the familiar sequence row and alignment column format, of which examples are widespread in the literature.
Sequence Alignment Map (SAM) is a text-based format originally for storing biological sequences aligned to a reference sequence developed by Heng Li and Bob Handsaker et al. [1] It was developed when the 1000 Genomes Project wanted to move away from the MAQ mapper format and decided to design a new format.
Multiple Sequence Alignment of the protein sequences to the left. Colors are used to display similarities among the sequences. Regular multiple sequence alignment – 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 ...
Homology search tools may take an individual nucleic acid or protein sequence as input, or use statistical models generated from multiple sequence alignments of known related sequences. Statistical models such as profile-HMMs , and RNA covariance models which also incorporate structural information, [ 27 ] can be helpful when searching for more ...
Multiple I state can occur consecutively, corresponding to multiple residues between consensus columns in an alignment. M, I and D states are connected by state transition probabilities, which also vary by position in the sequence alignment, to reflect the different frequencies of insertions and deletions across sequence alignments. [5]
The multiple sequence alignment problem is generally based on pairwise sequence alignment and currently, for a pairwise sequence alignment problem, biologists can use a dynamic programming approach to obtain its optimal solution. However, the multiple sequence alignment problem is still one of the more challenging problems in bioinformatics.