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Sequence motif
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====''De novo'' motif recognition from protein==== In 2018, a [[Markov random field]] approach has been proposed to infer DNA motifs from [[DNA-binding domains]] of proteins.<ref name="pmid30267681">{{cite journal | vauthors = Wong KC | title = DNA Motif Recognition Modeling from Protein Sequences | journal = iScience | volume = 7 | pages = 198β211 | date = September 2018 | pmid = 30267681 | pmc = 6153143 | doi = 10.1016/j.isci.2018.09.003 | bibcode = 2018iSci....7..198W }}</ref> '''Motif Discovery Algorithms''' Motif discovery algorithms use diverse strategies to uncover patterns in DNA sequences. Integrating enumerative, probabilistic, and nature-inspired approaches, demonstrate their adaptability, with the use of multiple methods proving effective in enhancing identification accuracy. '''Enumerative Approach:'''<ref name="Avicenna 2019" /> Initiating the motif discovery journey, the enumerative approach witnesses algorithms meticulously generating and evaluating potential motifs. Pioneering this domain are Simple Word Enumeration techniques, such as YMF and DREME, which systematically go through the sequence in search of short motifs. Complementing these, Clustering-Based Methods such as CisFinder employ nucleotide substitution matrices for motif clustering, effectively mitigating redundancy. Concurrently, Tree-Based Methods like Weeder and FMotif exploit tree structures, and Graph Theoretic-Based Methods (e.g., WINNOWER) employ graph representations, demonstrating the richness of enumeration strategies. '''Probabilistic Approach:'''<ref name="Avicenna 2019" /> Diverging into the probabilistic realm, this approach capitalizes on probability models to discern motifs within sequences. MEME, a deterministic exemplar, employs Expectation-Maximization for optimizing Position Weight Matrices (PWMs) and unraveling conserved regions in unaligned DNA sequences. Contrasting this, stochastic methodologies like Gibbs Sampling initiate motif discovery with random motif position assignments, iteratively refining the predictions. This probabilistic framework adeptly captures the inherent uncertainty associated with motif discovery. '''Advanced Approach:'''<ref name="Avicenna 2019" /> Evolving further, advanced motif discovery embraces sophisticated techniques, with [[Bayesian modeling]]<ref>{{Cite journal |last1=Miller |first1=Andrew K. |last2=Print |first2=Cristin G. |last3=Nielsen |first3=Poul M. F. |last4=Crampin |first4=Edmund J. |date=2010-11-18 |title=A Bayesian search for transcriptional motifs |journal=PLOS ONE |volume=5 |issue=11 |pages=e13897 |doi=10.1371/journal.pone.0013897 |issn=1932-6203 |pmc=2987817 |pmid=21124986 |bibcode=2010PLoSO...513897M |doi-access=free }}</ref> taking center stage. LOGOS and BaMM, exemplifying this cohort, intricately weave Bayesian approaches and [[Markov model|Markov models]] into their fabric for motif identification. The incorporation of Bayesian clustering methods enhances the probabilistic foundation, providing a holistic framework for pattern recognition in DNA sequences. '''Nature-Inspired and Heuristic Algorithms:'''<ref name="Avicenna 2019" /> A distinct category unfolds, wherein algorithms draw inspiration from the biological realm. [[Genetic algorithm|Genetic Algorithms (GA)]], epitomized by FMGA and MDGA,<ref>{{Cite book |last1=Che |first1=Dongsheng |last2=Song |first2=Yinglei |last3=Rasheed |first3=Khaled |chapter=MDGA: Motif discovery using a genetic algorithm |date=2005-06-25 |title=Proceedings of the 7th annual conference on Genetic and evolutionary computation |chapter-url=https://doi.org/10.1145/1068009.1068080 |series=GECCO '05 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=447β452 |doi=10.1145/1068009.1068080 |isbn=978-1-59593-010-1|s2cid=7892935 }}</ref> navigate motif search through genetic operators and specialized strategies. Harnessing swarm intelligence principles, [[Particle swarm optimization|Particle Swarm Optimization (PSO)]], [[Artificial bee colony algorithm|Artificial Bee Colony (ABC)]] algorithms, and [[Cuckoo search|Cuckoo Search (CS)]] algorithms, featured in GAEM, GARP, and MACS, venture into pheromone-based exploration. These algorithms, mirroring nature's adaptability and cooperative dynamics, serve as avant-garde strategies for motif identification. The synthesis of heuristic techniques in hybrid approaches underscores the adaptability of these algorithms in the intricate domain of motif discovery. [[File:Sequence_motif_algorithm_figure.jpg|center|thumb|500x500px|This chart shows many different types of algorithms used in the discovery of sequence motifs and their categories]]
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