Search results for "Microarray"

showing 10 items of 401 documents

Topological structure analysis of chromatin interaction networks.

2019

Abstract Background Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. Results It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, …

Chromatin interaction networksFunctionally related modulesComputer scienceCellStructure (category theory)Topologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeChromosome conformation capture03 medical and health sciences0302 clinical medicineGraph topologyStructural BiologyComponent (UML)medicineHumansGene Regulatory NetworksCell type specificityPromoter Regions GeneticMolecular Biologylcsh:QH301-705.5030304 developmental biologyConnected component0303 health sciencesApplied MathematicsResearchChromatinComputer Science ApplicationsChromatinHematopoiesisIdentification (information)medicine.anatomical_structurelcsh:Biology (General)Gene Expression RegulationTopological graph theorylcsh:R858-859.7DNA microarray030217 neurology & neurosurgeryAlgorithmsBMC bioinformatics
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SMART: Unique splitting-while-merging framework for gene clustering

2014

© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …

Clustering algorithmsMicroarrayslcsh:MedicineGene ExpressionBioinformaticscomputer.software_genreCell SignalingData MiningCluster Analysislcsh:ScienceFinite mixture modelOligonucleotide Array Sequence AnalysisPhysicsMultidisciplinarySMART frameworkConstrained clusteringCompetitive learning modelBioassays and Physiological AnalysisMultigene FamilyCanopy clustering algorithmEngineering and TechnologyData miningInformation TechnologyGenomic Signal ProcessingAlgorithmsResearch ArticleSignal TransductionComputer and Information SciencesFuzzy clusteringCorrelation clusteringResearch and Analysis MethodsClusteringMolecular GeneticsCURE data clustering algorithmGeneticsGene RegulationCluster analysista113Gene Expression Profilinglcsh:RBiology and Life SciencesComputational BiologyCell BiologyDetermining the number of clusters in a data setComputingMethodologies_PATTERNRECOGNITIONSplitting-merging awareness tactics (SMART)Signal ProcessingAffinity propagationlcsh:QGene expressionClustering frameworkcomputer
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Computation Cluster Validation in the Big Data Era

2017

Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis.

Clustering high-dimensional dataClass (computer programming)Clustering validation measureSettore INF/01 - InformaticaComputer sciencebusiness.industryBig dataInferenceMicroarrays data analysiscomputer.software_genreGap statisticTask (project management)ComputingMethodologies_PATTERNRECOGNITIONCURE data clustering algorithmConsensus clusteringHypothesis testing in statisticClustering Class Discovery in Data Algorithmsb Clustering algorithmFigure of meritConsensus clusteringData miningCluster analysisbusinesscomputer
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JANE: efficient mapping of prokaryotic ESTs and variable length sequence reads on related template genomes

2009

Abstract Background ESTs or variable sequence reads can be available in prokaryotic studies well before a complete genome is known. Use cases include (i) transcriptome studies or (ii) single cell sequencing of bacteria. Without suitable software their further analysis and mapping would have to await finalization of the corresponding genome. Results The tool JANE rapidly maps ESTs or variable sequence reads in prokaryotic sequencing and transcriptome efforts to related template genomes. It provides an easy-to-use graphics interface for information retrieval and a toolkit for EST or nucleotide sequence function prediction. Furthermore, we developed for rapid mapping an enhanced sequence align…

Computational biologyBiologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeUser-Computer InterfaceStructural BiologyDatabases Geneticlcsh:QH301-705.5Molecular BiologySequence (medicine)Expressed Sequence TagsWhole genome sequencingGeneticsInternetExpressed sequence tagGenomeBase SequencePhylumApplied MathematicsNucleic acid sequenceComputational BiologySequence Analysis DNAComputer Science Applicationslcsh:Biology (General)Single cell sequencinglcsh:R858-859.7DNA microarraySoftwareBMC Bioinformatics
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CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

2020

Abstract Background Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel …

Computer scienceCelllcsh:Computer applications to medicine. Medical informaticsBiochemistryConvolutional neural networkDNA sequencingchemistry.chemical_compoundStructural BiologyTranscription (biology)medicineHumansNucleosomeA-DNAEpigeneticsMolecular Biologylcsh:QH301-705.5Nucleosome classificationSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabiologybusiness.industryApplied MathematicsDeep learningResearchEpigeneticPattern recognitionGenomicsbiology.organism_classificationNucleosomesComputer Science ApplicationsRecurrent neural networkmedicine.anatomical_structurechemistrylcsh:Biology (General)Recurrent neural networkslcsh:R858-859.7Deep learning networksEukaryoteNeural Networks ComputerArtificial intelligenceDNA microarraybusinessDNABMC Bioinformatics
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PVAmpliconFinder: a workflow for the identification of human papillomaviruses from high-throughput amplicon sequencing

2019

Abstract Background The detection of known human papillomaviruses (PVs) from targeted wet-lab approaches has traditionally used PCR-based methods coupled with Sanger sequencing. With the introduction of next-generation sequencing (NGS), these approaches can be revisited to integrate the sequencing power of NGS. Although computational tools have been developed for metagenomic approaches to search for known or novel viruses in NGS data, no appropriate tool is available for the classification and identification of novel viral sequences from data produced by amplicon-based methods. Results We have developed PVAmpliconFinder, a data analysis workflow designed to rapidly identify and classify kno…

Computer scienceComputational biologylcsh:Computer applications to medicine. Medical informaticsBiochemistryWorkflowUser-Computer Interface03 medical and health sciencessymbols.namesakeStructural BiologyHumansVirus discoverylcsh:QH301-705.5PapillomaviridaeMolecular BiologyThroughput (business)PhylogenyAmplicon sequencing030304 developmental biologySanger sequencing0303 health sciencesBiological data030306 microbiologyMethodology ArticleApplied MathematicsHigh-Throughput Nucleotide SequencingPapillomavirusAmpliconComputer Science ApplicationsIdentification (information)Workflowlcsh:Biology (General)MetagenomicsDNA ViralAmplicon sequencingsymbolslcsh:R858-859.7Primer (molecular biology)DNA microarrayBMC Bioinformatics
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2004

The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was bench…

ContigArtificial neural networkApplied MathematicsBiologycomputer.software_genreBiochemistryGenomeComputer Science ApplicationsTerm (time)Support vector machineAnnotationStructural BiologyControlled vocabularyData miningDNA microarrayMolecular BiologycomputerBMC Bioinformatics
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Aneuploid IMR90 cells induced by depletion of pRB, DNMT1 and MAD2 show a common gene expression signature

2019

Chromosome segregation defects lead to aneuploidy which is a major feature of solid tumors. How diploid cells face chromosome mis-segregation and how aneuploidy is tolerated in tumor cells are not completely defined yet. Thus, an important goal of cancer genetics is to identify gene networks that underlie aneuploidy and are involved in its tolerance. To this aim, we induced aneuploidy in IMR90 human primary cells by depleting pRB, DNMT1 and MAD2 and analyzed their gene expression profiles by microarray analysis. Bioinformatic analysis revealed a common gene expression profile of IMR90 cells that became aneuploid. Gene Set Enrichment Analysis (GSEA) also revealed gene-sets/pathways that are …

DNA (Cytosine-5-)-Methyltransferase 1AneuploidyBiologyMicroarrayReal-Time Polymerase Chain ReactionRetinoblastoma ProteinCell LineRNA interferenceGene expressionProtein Interaction MappingGeneticsmedicineHumansGeneOligonucleotide Array Sequence AnalysisMicroarray analysis techniquesGene Expression ProfilingBioinformatics analysiChromosomeFibroblastsmedicine.diseaseAneuploidyGene Expression RegulationRNAiMad2 ProteinsDNMT1Cancer researchKIF4ARNA InterferenceTranscriptomeIMR90 human fibroblast
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Evaluation of the DNA microarray “AMR Direct Flow Chip Kit” for detection of antimicrobial resistance genes from Gram-positive and Gram-negative bact…

2019

Abstract Introduction The AMR Direct Flow Chip assay allows the simultaneous detection of a large variety of antibiotic resistance genetic markers. To assess this kit's performance, we use isolated colonies as starting material. The assay has been approved by the European Economic Area as a suitable device for in vitro diagnosis (CE IVD) using clinical specimens. Methods A total of 210 bacterial isolates harbouring either one or more antimicrobial resistance genes including plasmid-encoded extended-spectrum β-lactamases (SHV, CTX-M) and carbapenemases (GES, SME, KPC, NMC/IMI, SIM, GIM, SPM, NDM, VIM, IMP, and OXA), mecA, vanA and vanB, and 30 controls were included. Results The assay displa…

DNA Bacterial0301 basic medicineMicrobiology (medical)030106 microbiologyGram-Positive BacteriaSensitivity and Specificitybeta-Lactam Resistancebeta-LactamasesMicrobiology03 medical and health sciences0302 clinical medicineAntibiotic resistanceBacterial ProteinsVancomycinDrug Resistance Multiple BacterialGram-Negative Bacteriapolycyclic compoundsmedicineHumans030212 general & internal medicineGeneGram-Positive Bacterial InfectionsOligonucleotide Array Sequence AnalysisGrambiologyDrug Resistance Microbialbiochemical phenomena metabolism and nutritionbacterial infections and mycosesbiology.organism_classificationIn vitroGenes BacterialGenetic markerVancomycinReagent Kits DiagnosticDNA microarrayGram-Negative Bacterial InfectionsBacteriamedicine.drugEnfermedades infecciosas y microbiologia clinica (English ed.)
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Impact of analytic provenance in genome analysis

2014

Background Many computational methods are available for assembly and annotation of newly sequenced microbial genomes. However, when new genomes are reported in the literature, there is frequently very little critical analysis of choices made during the sequence assembly and gene annotation stages. These choices have a direct impact on the biologically relevant products of a genomic analysis - for instance identification of common and differentiating regions among genomes in a comparison, or identification of enriched gene functional categories in a specific strain. Here, we examine the outcomes of different assembly and analysis steps in typical workflows in a comparison among strains of Vi…

DNA BacterialComparative genomicsGeneticsComputational BiologySequence assemblyMicrobiologiaMolecular Sequence AnnotationSequence Analysis DNAComputational biologyGene AnnotationBiologyGenomeAnnotationProceedingsWorkflowGenes BacterialBacteris patògensGeneticsIdentification (biology)DNA microarrayVibrio vulnificusGenome BacterialBiotechnologyBMC Genomics
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