scholarly journals HeliCis: a DNA motif discovery tool for colocalized motif pairs with periodic spacing

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Erik Larsson ◽  
Per Lindahl ◽  
Petter Mostad
Keyword(s):  
Motif Discovery ◽  
Dna Motif ◽  
Dna Motif Discovery ◽  
Periodic Spacing

scholarly journals CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Giovanni Scala ◽  
Antonio Federico ◽  
Dario Greco
Keyword(s):  
Dna Methylation ◽  
Motif Discovery ◽  
Cpg Methylation ◽  
Functional Interpretation ◽  
Dna Motifs ◽  
Molecular Alterations ◽  
Link Type ◽  
Dna Motif ◽  
Quantitative Manner ◽  
Dna Motif Discovery

Abstract Background The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. Results Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. Conclusions CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs.

scholarly journals DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

2018 ◽  
Vol 32 (3) ◽  
pp. 759-768 ◽  
Author(s):  
Nung Kion Lee ◽  
Farah Liyana Azizan ◽  
Yu Shiong Wong ◽  
Norshafarina Omar
Keyword(s):  
Deep Learning ◽  
Motif Discovery ◽  
Learning Approach ◽  
Dna Motif ◽  
Feature Based ◽  
Dna Motif Discovery

EFFICIENT DNA MOTIF DISCOVERY USING MODIFIED GENETIC ALGORITHM

2013 ◽  
Vol 12 (03) ◽  
pp. 1350017
Author(s):  
ESSAM AL DAOUD
Keyword(s):  
Genetic Algorithm ◽  
Dna Sequences ◽  
Motif Discovery ◽  
Consensus Algorithms ◽  
Gene Position ◽  
Dna Motif ◽  
Implementation Time ◽  
Dna Motif Discovery ◽  
Standard Genetic Algorithm ◽  
New Distribution

In this study, a new genetic algorithm was developed to discover the best motifs in a set of DNA sequences. The main steps were: finding the potential positions in each sequence by using few voters (1–5 sequences), constructing the chromosomes from the potential positions, evaluating the fitness for each gene (position) and for each chromosome, calculating the new random distribution, and using the new distribution to generate the next generation. To verify the effectiveness of the proposed algorithm, several real and artificial datasets were used; the results are compared to the standard genetic algorithm, and Gibbs, MEME, and consensus algorithms. Although all the algorithms have low correlation with the correct motifs, the new algorithm exhibits higher accuracy, without sacrificing implementation time.

scholarly journals An Affinity Propagation-Based DNA Motif Discovery Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chunxiao Sun ◽  
Hongwei Huo ◽  
Qiang Yu ◽  
Haitao Guo ◽  
Zhigang Sun
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Simulated Data ◽  
Biological Data ◽  
Affinity Propagation ◽  
Local Optimum ◽  
Data Sets ◽  
Dna Motif ◽  
Challenging Tasks ◽  
Dna Motif Discovery

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.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

2019 ◽  
Vol 15 (1) ◽  
pp. 4-26
Author(s):  
Fatma A. Hashim ◽  
Mai S. Mabrouk ◽  
Walid A.L. Atabany
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Probabilistic Approach ◽  
Biological Data ◽  
Systemic Review ◽  
Local Optimum ◽  
Dna Motif ◽  
Functional Features ◽  
Dna Motif Discovery ◽  
Discovery Algorithms

Background: Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery. Methods: Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach. Results: Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum. Conclusion: Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

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