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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:
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.
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.
This page is a subsection of the list of sequence alignment software. Multiple alignment visualization tools typically serve four purposes: Aid general understanding of large-scale DNA or protein alignments; Visualize alignments for figures and publication; Manually edit and curate automatically generated alignments; Analysis in depth
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.
A global alignment performs an end-to-end alignment of the query sequence with the reference sequence. Ideally, this alignment technique is most suitable for closely related sequences of similar lengths. The Needleman-Wunsch algorithm is a dynamic programming technique used to conduct global alignment. Essentially, the algorithm divides the ...
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 .