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Relevant bibliographies by topics / Planted motif search

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Academic literature on the topic 'Planted motif search'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Planted motif search"

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PATHAK, SUDIPTA, SANGUTHEVAR RAJASEKARAN, and MARIUS NICOLAE. "EMS1: AN ELEGANT ALGORITHM FOR EDIT DISTANCE BASED MOTIF SEARCH." International Journal of Foundations of Computer Science 24, no. 04 (2013): 473–86. http://dx.doi.org/10.1142/s0129054113500159.

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Motifs are biologically significant patterns found in DNA/protein sequences. Given a set of biological sequences, the problem of identifying the motifs is very challenging. This problem has been well studied in computational biology. Identifying motifs through experimental processes is extremely expensive and time consuming. This is one of the factors influencing computational biologists to come up with novel computational methods to predict motifs. Several motif models have been proposed in the literature and for each model numerous algorithms have been devised. Three popular motif models are (l, d)-motif search or Planted Motif Search (PMS), Simple Motif Search (SMS), and Edit-distance based Motif Search (EMS). For PMS and SMS several algorithms have been proposed and implemented. On the other hand, even though some algorithms exist in the literature for the problem of EMS, no implementations of these algorithms are known. This is mainly because the proposed algorithms are complex. In this paper we present an elegant algorithm for EMS. We have implemented this algorithm and compared it against 14 other algorithms in terms of sensitivity and specificity. Our experimental results indicate that the new algorithm is very competitive in practice.

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ZHANG, YIPU, HONGWEI HUO, and QIANG YU. "A HEURISTIC CLUSTER-BASED EM ALGORITHM FOR THE PLANTED (l, d) PROBLEM." Journal of Bioinformatics and Computational Biology 11, no. 04 (2013): 1350009. http://dx.doi.org/10.1142/s0219720013500091.

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The planted motif search problem arises from locating the transcription factor binding sites (TFBSs) which are crucial for understanding the gene regulatory relationship. Many attempts in using expectation maximization for TFBSs discovery are successful in past. However, identifying highly degenerate motifs and reducing the effect of local optima are still an arduous task. To alleviate the vulnerability of EM to local optima trapping, we present a heuristic cluster-based EM algorithm, CEM, which refines the cluster subsets in EM method to explore the best local optimal solution. Based on experiments using both synthetic and real datasets, our algorithm demonstrates significant improvements in identifying the motif instances and performs better than current widely used algorithms. CEM is a novel planted motif finding algorithm, which is able to solve the challenging instances and easy to parallel since the process of solving each cluster subset is independent.

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Theepalakshmi, P., та U. Srinivasulu Reddy. "Freezing firefly algorithm for efficient planted (ℓ, d) motif search". Medical & Biological Engineering & Computing 60, № 2 (2022): 511–30. http://dx.doi.org/10.1007/s11517-021-02468-x.

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Davila, J., S. Balla, and S. Rajasekaran. "Fast and Practical Algorithms for Planted (l, d) Motif Search." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 4 (2007): 544–52. http://dx.doi.org/10.1109/tcbb.2007.70241.

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Xu, Yun, Jiaoyun Yang, Yuzhong Zhao, and Yi Shang. "An improved voting algorithm for planted (l,d) motif search." Information Sciences 237 (July 2013): 305–12. http://dx.doi.org/10.1016/j.ins.2013.03.023.

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Lala, Septem Riza, Farrah Dhiba Tyas, Setiawan Wawan, Hidayat Topik, and Fahs Mahmoud. "Parallel random projection using R high performance computing for planted motif search." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1352–59. https://doi.org/10.12928/TELKOMNIKA.v17i3.11750.

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Motif discovery in DNA sequences is one of the most important issues in bioinformatics. Thus, algorithms for dealing with the problem accurately and quickly have always been the goal of research in bioinformatics. Therefore, this study is intended to modify the random projection algorithm to be implemented on R high performance computing (i.e., the R package pbdMPI). Some steps are needed to achieve this objective, ie preprocessing data, splitting data according to number of batches, modifying and implementing random projection in the pbdMPI package, and then aggregating the results. To validate the proposed approach, some experiments have been conducted. Several benchmarking data were used in this study by sensitivity analysis on number of cores and batches. Experimental results show that computational cost can be reduced, which is that the computation cost of 6 cores is faster around 34 times compared with the standalone mode. Thus, the proposed approach can be used for motif discovery effectively and efficiently.

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Sun, Chunxiao, Hongwei Huo, Qiang Yu, Haitao Guo, and Zhigang Sun. "An Affinity Propagation-Based DNA Motif Discovery Algorithm." BioMed Research International 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/853461.

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The planted(l,d)motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.

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Xiao, Peng, Soumitra Pal, and Sanguthevar Rajasekaran. "Randomised sequential and parallel algorithms for efficient quorum planted motif search." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 105. http://dx.doi.org/10.1504/ijdmb.2017.086457.

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Xiao, Peng, Soumitra Pal, and Sanguthevar Rajasekaran. "Randomised sequential and parallel algorithms for efficient quorum planted motif search." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 105. http://dx.doi.org/10.1504/ijdmb.2017.10007475.

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Riza, Lala Septem, Tyas Farrah Dhiba, Wawan Setiawan, Topik Hidayat, and Mahmoud Fahsi. "Parallel random projection using R high performance computing for planted motif search." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 3 (2019): 1352. http://dx.doi.org/10.12928/telkomnika.v17i3.11750.

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Book chapters on the topic "Planted motif search"

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Davila, Jaime, Sudha Balla, and Sanguthevar Rajasekaran. "Space and Time Efficient Algorithms for Planted Motif Search." In Computational Science – ICCS 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758525_110.

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Anya, Li. "Research on Algorithms for Planted (l,d) Motif Search." In Applications and Techniques in Information Security. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2907-4_12.

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Hasan, Mohammad, and Pintu Chandra Shill. "A Comparative Analysis for Generating Common d-Neighborhood on Planted Motif Search Problem." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19958-5_78.

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"Approximation Algorithms for the Primer Selection, Planted Motif Search, and Related Problems." In Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781420010749-86.

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Balla, Sudha, Jaime Davila, and Sanguthevar Rajasekaran. "Approximation Algorithms for the Primer Selection, Planted Motif Search, and Related Problems." In Handbook of Approximation Algorithms and Metaheuristics. Chapman and Hall/CRC, 2007. http://dx.doi.org/10.1201/9781420010749.ch75.

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Chatukuta, Patience, Angela Sibanda-Makuvise, Tshegofatso Dikobe, et al. "The Knowledge Landscape of Adenylyl Cyclases in model plant Arabidopsis thaliana." In Recent Advances in Strategic Model Plants [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1002359.

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One strategy for improving responses and adaptation systems of plants to stress is to target molecules involved in signaling and transduction of the stimuli effected by stresses. One such molecule is adenylyl cyclase (AC) – an enzyme that catalyzes the conversion of adenosine 5′-triphosphate (ATP) to the second messenger, 3′,5′-cyclic adenosine monophosphate (cAMP). cAMP, in turn, transduces signals in response to the various biotic and abiotic stress factors. Surprisingly, as far as five decades ago, attempts to isolate ACs and/or detect cAMP from the research model plant, Arabidopsis thaliana, were inconclusive or a matter of serious debates due to the absence of appropriate techniques or advanced technologies. This chapter, therefore, herein takes the reader on a journey from the 1970s to the present day, unraveling the challenges encountered, developments made, and successes realized in efforts and attempts to identify and characterize ACs in A. thaliana. The chapter covers from the early age of unsuccessful attempts to the more recent and successful advanced technologies such as the motif search approach, omics analysis and homologous cloning. Perspectives on the direction that future knowledge-building around this important group of plant proteins are also shared.

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Conference papers on the topic "Planted motif search"

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Merlin, Jerlin C., and Hieu Dinh. "Poster: Randomized algorithms for planted Motif Search." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182654.

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Mohanty, Satarupa, Suneeta Mohanty, and Sharmistha Roy. "Exact Planted (l, d) Motif Search Algorithms: A Review." In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE). IEEE, 2018. http://dx.doi.org/10.1109/rice.2018.8509078.

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Abbas, Mostafa M., and Hazem M. Bahig. "Performance and analysis of modified voting algorithm for planted motif search." In 2009 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2009. http://dx.doi.org/10.1109/aiccsa.2009.5069407.

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Xiao, Peng, Soumitra Pal, and Sanguthevar Rajasekaran. "qPMS10: A randomized algorithm for efficiently solving quorum Planted Motif Search problem." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822598.

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Al-Shaikhli, Hasnaa, and Elise de Doncker. "qSMF: an Approximate Algorithm for Quotum Planted Motif Search on ChIP-Seq Data." In 2019 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2019. http://dx.doi.org/10.1109/eit.2019.8834006.

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Durge, Aditi, and Deepti Shrimankar. "MRQPMS: Design of a Map Reduce Bioinspired Model for Solving Quorum Planted Motif Search for High-Speed Deployments." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011616500003414.

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Reports on the topic "Planted motif search"

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Pawlowski, Wojtek P., and Avraham A. Levy. What shapes the crossover landscape in maize and wheat and how can we modify it. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600025.bard.

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Meiotic recombination is a process in which homologous chromosomes engage in the exchange of DNA segments, creating gametes with new genetic makeup and progeny with new traits. The genetic diversity generated in this way is the main engine of crop improvement in sexually reproducing plants. Understanding regulation of this process, particularly the regulation of the rate and location of recombination events, and devising ways of modifying them, was the major motivation of this project. The project was carried out in maize and wheat, two leading crops, in which any advance in the breeder’s toolbox can have a huge impact on food production. Preliminary work done in the USA and Israeli labs had established a strong basis to address these questions. The USA lab pioneered the ability to map sites where recombination is initiated via the induction of double-strand breaks in chromosomal DNA. It has a long experience in cytological analysis of meiosis. The Israeli lab has expertise in high resolution mapping of crossover sites and has done pioneering work on the importance of epigenetic modifications for crossover distribution. It has identified genes that limit the rates of recombination. Our working hypothesis was that an integrative analysis of double-strand breaks, crossovers, and epigenetic data will increase our understanding of how meiotic recombination is regulated and will enhance our ability to manipulate it. The specific objectives of the project were: To analyze the connection between double-strand breaks, crossover, and epigenetic marks in maize and wheat. Protocols developed for double-strand breaks mapping in maize were applied to wheat. A detailed analysis of existing and new data in maize was conducted to map crossovers at high resolution and search for DNA sequence motifs underlying crossover hotspots. Epigenetic modifications along maize chromosomes were analyzed as well. Finally, a computational analysis tested various hypotheses on the importance of chromatin structure and specific epigenetic modifications in determining the locations of double-strand breaks and crossovers along chromosomes. Transient knockdowns of meiotic genes that suppress homologous recombination were carried out in wheat using Virus-Induced Gene Silencing. The target genes were orthologs of FANCM, DDM1, MET1, RECQ4, and XRCC2.

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