Sequence alignment

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Sequence alignment is a method used in bioinformatics to arrange the sequences of DNA, RNA, or protein to identify regions of similarity. These similarities may be a result of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the sequences to optimize the alignment and to identify the conservation and changes in different molecular sequences.

Types of Sequence Alignment[edit | edit source]

There are two main types of sequence alignment: global alignment and local alignment.

Global alignment, implemented in the Needleman-Wunsch algorithm, compares the entire sequence of two entities and is most useful when the sequences are of roughly equal length and are quite similar.

Local alignment, implemented in the Smith-Waterman algorithm, identifies regions of similarity within long sequences that are often widely divergent overall. Local alignment is often used in identifying domains within proteins and parts of a gene in genomes.

Methods of Sequence Alignment[edit | edit source]

There are several methods used to perform sequence alignment. These include:

  • Dot-matrix method - This is a simple but powerful approach used to identify similar regions in sequences.
  • Dynamic programming - This method guarantees to find an optimal alignment given a particular scoring system.
  • Heuristic methods - These methods are used for large-scale database searches, and include FASTA and BLAST.

Applications of Sequence Alignment[edit | edit source]

Sequence alignment is used in various applications, including:

  • Phylogenetic tree construction - Sequence alignment is used to infer evolutionary relationships between different organisms.
  • Protein structure prediction - Sequence alignment can help predict the structure of a protein based on the known structures of other proteins.
  • Drug discovery - Sequence alignment can identify potential drug targets by comparing the genomes of disease-causing organisms to those of humans.

See Also[edit | edit source]

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