A Modified Median String Algorithm for Gene Regulatory Motif Classification

Consensus string is a significant feature of a deoxyribonucleic acid (DNA) sequence. The median string is one of the most popular exact algorithms to find DNA consensus. A DNA sequence is represented using the alphabet Σ= {a, c, g, t}. The algorithm generates a set of all the 4l possible motifs or l...

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Vydáno v:Symmetry (Basel) Ročník 12; číslo 8; s. 1363
Hlavní autoři: Kaysar, Mohammad Shibli, Khan, Mohammad Ibrahim
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2020
Témata:
Algorithms
Alphabets
Deoxyribonucleic acid
DNA
Experiments
Markov analysis
Markov chains
Probability
Statistical analysis
Strings
ISSN:2073-8994, 2073-8994
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Shrnutí:Consensus string is a significant feature of a deoxyribonucleic acid (DNA) sequence. The median string is one of the most popular exact algorithms to find DNA consensus. A DNA sequence is represented using the alphabet Σ= {a, c, g, t}. The algorithm generates a set of all the 4l possible motifs or l-mers from the alphabet to search a motif of length l. Out of all possible l-mers, it finds the consensus. This algorithm guarantees to return the consensus but this is NP-complete and runtime increases with the increase in l-mer size. Using transitional probability from the Markov chain, the proposed algorithm symmetrically generates four subsets of l-mers. Each of the subsets contains a few l-mers starting with a particular letter. We used these reduced sets of l-mers instead of using 4ll-mers. The experimental result shows that the proposed algorithm produces a much lower number of l-mers and takes less time to execute. In the case of l-mer of length 7, the proposed system is 48 times faster than the median string algorithm. For l-mer of size 7, the proposed algorithm produces only 2.5% l-mer in comparison with the median string algorithm. While compared with the recently proposed voting algorithm, our proposed algorithm is found to be 4.4 times faster for a longer l-mer size like 9.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym12081363