Search results for "nucleosome positioning."

showing 9 items of 9 documents

A one class KNN for signal identification: a biological case study

2009

The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes harder whenever only one pattern is well-represented in the signal; in such cases, one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a multi layer model (MLM) that provides preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.

Computer sciencebusiness.industryFeature vectorPattern recognitionmulti layer methodone class classifierPreprocessorSegmentationnucleosome positioning.Artificial intelligenceK nearest neighbourbusinessClassifier (UML)Multi layer
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A multi-layer method to study genome-scale positions of nucleosomes

2009

AbstractThe basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and…

Feature extractionNucleosome positioningGenomicsSaccharomyces cerevisiaeComputational biologyHidden Markov Modelchemistry.chemical_compoundSettore BIO/10 - BiochimicaNucleosome positioning Hidden Markov Model Classification Multi-layer methodGeneticsHumansNucleosomeMulti-layer methodHidden Markov modelBase PairingMulti layerOligonucleotide Array Sequence AnalysisGeneticsBase SequenceSettore INF/01 - InformaticabiologyGenome HumanClassificationMarkov ChainsNucleosomesChromatinHistonechemistrybiology.proteinDNAGenomics
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Sliding-end-labelling

1986

Abstract A method, termed ‘sliding-end-labelling’, has been devised to avoid a frequent artifact in nucleosome positioning by indirect end labelling, namely the appearing of DNA fragments originated by two nuclease cuts, one of them lying within the region covered by the probe. The method is applied to the nucleosome positioning in the yeast SUC2 gene for invertase.

Electrophoresis Agar GelNucleasebiologyBiophysicsNucleic Acid HybridizationDNA Restriction EnzymesSaccharomyces cerevisiaeCell BiologyBiochemistryNucleosomesChromatin Nucleosome positioning Indirect end labelling SUC2 gene (Saccharomyces cerevisiae)BiochemistryStructural BiologyLabellingGeneticsbiology.proteinMicrococcal NucleaseNucleosomeDNA FungalBiological systemMolecular BiologyFEBS Letters
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A one class classifier for Signal identification: a biological case study

2008

The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and lin…

business.industryComputer scienceFeature vectorOne-class classificationPattern recognitionSegmentationArtificial intelligencebusinessMulti Layer Method One Class classification Bioinformatics Nucleosome Positioning.Classifier (UML)Synthetic data
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A Deep Learning Model for Epigenomic Studies

2016

Epigenetics is the study of heritable changes in gene expression that does not involve changes to the underlying DNA sequence, i.e. a change in phenotype not involved by a change in genotype. At least three main factor seems responsible for epigenetic change including DNA methylation, histone modification and non-coding RNA, each one sharing having the same property to affect the dynamic of the chromatin structure by acting on Nucleosomes posi- tion. A nucleosome is a DNA-histone complex, where around 150 base pairs of double-stranded DNA is wrapped. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells, to form the Chromatin. Nucleosome positioning plays an imp…

0301 basic medicineSettore INF/01 - InformaticabiologyBase pairdeep learningGenomicsComputational biologyBioinformaticsChromatin03 medical and health sciences030104 developmental biologyHistoneclassificationDNA methylationbiology.proteinNucleosomeEpigeneticsnucleosome positioningEpigenomics2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
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Histone-mediated transgenerational epigenetics

2019

Abstract Epigenetic mechanisms operate at the interface between the environment and genome, by converting the environmental stimuli to phenotypic responses through changes in the chromatin landscape, which ultimately affects gene expression in the absence of alterations in DNA sequence. In this scenario, transgenerational inheritance occurs when epigenetic variations induced by environmental stimuli are transmitted through the germ line to succeeding generations that had never experienced those stimuli. There is an ever-growing list of reports indicating that histones are fundamental players in these processes in a variety of organisms. In this chapter, we provide a perspective on histone-d…

GeneticsHistonebiology.proteinInheritance (genetic algorithm)NucleosomeSettore BIO/11 - Biologia MolecolareEpigeneticsHistone-based epigenetic inheritanceHistone inheritance in diseaseHistone posttranslational modificationsHistone variantsNucleosome positioningPerpetuation of maternal histonesRetention of paternal nucleosomeTransgenerational transmission of environmental informationBiologyGenomePhenotypeGermlineChromatin
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Acetylated nucleosome assembly on telomeric DNAs

2003

Abstract The role of histone N-terminal domains on the thermodynamic stability of nucleosomes assembled on several different telomeric DNAs as well as on ‘average’ sequence DNA and on strong nucleosome positioning sequences, has been studied by competitive reconstitution. We find that histone tails hyperacetylation favors nucleosome formation, in a similar extent for all the examined sequences. On the contrary, removal of histone terminal domains by selective trypsinization causes a decrease of nucleosome stability which is smaller for telomeres compared to the other sequences examined, suggesting that telomeric sequences have only minor interactions with histone tails. Micrococcal nuclease…

Nucleosome assemblyBiophysicsBinding CompetitiveBiochemistryHistonesKluyveromycesHistone H1Histone methylationAnimalsHumansMicrococcal NucleaseNucleosomeHistone codeHistone octamerChemistrynucleosomeChlamydomonasOrganic Chemistryhistone acetylationhistone acetylation; nucleosome; nucleosome positioning; telomeres; thermodynamic stabilityAcetylationDNATelomeretelomeresLinker DNANucleosomesProtein Structure TertiaryBiochemistryChromatosomeBiophysicsthermodynamic stabilityThermodynamicsnucleosome positioningBiophysical Chemistry
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A Fuzzy One Class Classifier for Multi Layer Model

2009

The paper describes an application of a fuzzy one-class classifier (FOC ) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM ) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.

Settore INF/01 - InformaticaComputer sciencebusiness.industryFeature vectorPattern recognitionHide markov modelcomputer.software_genreFuzzy logicComputingMethodologies_PATTERNRECOGNITIONMulti Layer Method Nucleosome Positioning BioinformaticsPreprocessorSegmentationData miningArtificial intelligencebusinesscomputerClassifier (UML)Multi layer
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A new Multi-Layers Method to Analyze Gene Expression

2007

In the paper a new Multi-Layers approach (called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good rec…

Regulation of gene expressionbiologySettore INF/01 - InformaticaComputer scienceMicroarray analysis techniquesSaccharomyces cerevisiaeChromosomeComputational biologybiology.organism_classificationBioinformaticsSynthetic dataBioinformatics Nucleosome positioning Multi layer methods.ChromatinIdentification (information)chemistry.chemical_compoundchemistrySettore BIO/10 - BiochimicaGene expressionNucleosomeHidden Markov modelDNA
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