ArticleBMC bioinformatics2007
Local Renyi entropic profiles of DNA sequences.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Towards More Efficient Rényi Entropy Estimation.Entropy (Basel, Switzerland) · 2023Article
- Article
- AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.GigaScience · 2022Article
- The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.Entropy (Basel, Switzerland) · 2021Article
- Spatial constrains and information content of sub-genomic regions of the human genome.iScience · 2021Article
- Integrated entropy-based approach for analyzing exons and introns in DNA sequences.BMC bioinformatics · 2019Article
- Additive methods for genomic signatures.BMC bioinformatics · 2016Article
- Informational laws of genome structures.Scientific reports · 2016Article
- On the comparison of regulatory sequences with multiple resolution Entropic Profiles.BMC bioinformatics · 2016Article
- Sequence analysis by iterated maps, a review.Briefings in bioinformatics · 2014Review
- Information theory applications for biological sequence analysis.Briefings in bioinformatics · 2014Review
- DNA sequences at a glance.PloS one · 2013Article
- Pattern matching through Chaos Game Representation: bridging numerical and discrete data structures for biological sequence analysis.Algorithms for molecular biology : AMB · 2012Article
- Entropic Profiler - detection of conservation in genomes using information theory.BMC research notes · 2009Article
- Biological sequences as pictures: a generic two dimensional solution for iterated maps.BMC bioinformatics · 2009Article
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2 authors.
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Abstract
backgroundIn a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs.
resultsThe new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at http://kdbio.inesc-id.pt/~svinga/ep/.
conclusionThe ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures.
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