ArticleBiology direct2006
A survey of motif discovery methods in an integrated framework.
Article in Biology direct, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
- Article
- BML: a versatile web server for bipartite motif discovery.Briefings in bioinformatics · 2022Article
- A survey on deep learning in DNA/RNA motif mining.Briefings in bioinformatics · 2021Article
- Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application toJournal of the Royal Society, Interface · 2020Article
- From biophysics to 'omics and systems biology.European biophysics journal : EBJ · 2019Review
- DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.BMC bioinformatics · 2018Article
- Sequence motif finder using memetic algorithm.BMC bioinformatics · 2018Article
- Evaluating tools for transcription factor binding site prediction.BMC bioinformatics · 2016Article
- A Comparative Analysis Between k-Mers and Community Detection-Based Features for the Task of Protein Classification.IEEE transactions on nanobioscience · 2016Article
- Sequence information gain based motif analysis.BMC bioinformatics · 2015Article
- MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures.BMC genomics · 2015Article
- Transcription factor motif quality assessment requires systematic comparative analysis.F1000Research · 2015Article
- Absence of a simple code: how transcription factors read the genome.Trends in biochemical sciences · 2014Review
- cisExpress: motif detection in DNA sequences.Bioinformatics (Oxford, England) · 2013Article
- DRIMust: a web server for discovering rank imbalanced motifs using suffix trees.Nucleic acids research · 2013Article
- Motif discovery and transcription factor binding sites before and after the next-generation sequencing era.Briefings in bioinformatics · 2013Article
- Transcription of Tnfaip3 is regulated by NF-κB and p38 via C/EBPβ in activated macrophages.PloS one · 2013Article
- Bayesian centroid estimation for motif discovery.PloS one · 2013Article
- Searching for transcription factor binding sites in vector spaces.BMC bioinformatics · 2012Article
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Authors and funding
2 authors.
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Abstract
backgroundThere has been a growing interest in computational discovery of regulatory elements, and a multitude of motif discovery methods have been proposed. Computational motif discovery has been used with some success in simple organisms like yeast. However, as we move to higher organisms with more complex genomes, more sensitive methods are needed. Several recent methods try to integrate additional sources of information, including microarray experiments (gene expression and ChlP-chip). There is also a growing awareness that regulatory elements work in combination, and that this combinatorial behavior must be modeled for successful motif discovery. However, the multitude of methods and approaches makes it difficult to get a good understanding of the current status of the field.
resultsThis paper presents a survey of methods for motif discovery in DNA, based on a structured and well defined framework that integrates all relevant elements. Existing methods are discussed according to this framework.
conclusionThe survey shows that although no single method takes all relevant elements into consideration, a very large number of different models treating the various elements separately have been tried. Very often the choices that have been made are not explicitly stated, making it difficult to compare different implementations. Also, the tests that have been used are often not comparable. Therefore, a stringent framework and improved test methods are needed to evaluate the different approaches in order to conclude which ones are most promising. REVIEWERS: This article was reviewed by Eugene V. Koonin, Philipp Bucher (nominated by Mikhail Gelfand) and Frank Eisenhaber.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.