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Both are text-based formats storing biological sequences and corresponding quality codes.
Such changes are akin to mutations in biological sequences.
Thus, these changes are similar to mutation of biological sequences which occur over successive generations.
The proposed algorithm is compared with recent specialized compression tools for biological sequences.
Motif discovery in biological sequences is of prime importance and a major challenge in computational biology.
We have taken advantage of fact that there are a few papers associated to the biological sequences stored in NCBI.
Sequences of DNA nucleotides are of great interest (as are sequences of amino acids and other biological sequences).
We have studied how to represent biological sequences and sequence-related genomics concepts using logical data structure.
The constantly growing pool of string data, especially biological sequences, requires us to build suffix trees for much larger strings.
It can compute a complete similarity score between two biological sequences which have less than 10,000 characters.
This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences.
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