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In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. [1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix.
Second normal form (2NF), in database normalization, is a normal form. A relation is in the second normal form if it fulfills the following two requirements: A relation is in the second normal form if it fulfills the following two requirements:
Codd introduced the concept of normalization and what is now known as the first normal form (1NF) in 1970. [4] Codd went on to define the second normal form (2NF) and third normal form (3NF) in 1971, [5] and Codd and Raymond F. Boyce defined the Boyce–Codd normal form (BCNF) in 1974. [6]
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.
Thus, sequence analysis can be used to assign function to coding and non-coding regions in a biological sequence usually by comparing sequences and studying similarities and differences. Nowadays, there are many tools and techniques that provide the sequence comparisons (sequence alignment) and analyze the alignment product to understand its ...
Alignment of cDNA sequences to a genome. Nucleotide DECIPHER: Alignment of rearranged genomes using 6 frame translation: Nucleotide FLAK Fuzzy whole genome alignment and analysis: Nucleotide GMAP Alignment of cDNA sequences to a genome. Identifies splice site junctions with high accuracy. Nucleotide Splign Alignment of cDNA sequences to a genome.
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.
The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences. Instead of looking at the entire sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure .