Search results for "eukaryote"

showing 10 items of 16 documents

Linking extreme seasonality and gene expression in arctic marine protists

2021

ABSTRACTAt high latitudes, strong seasonal differences in light availability affect marine organisms and restrict the timing of ecosystem processes. Marine protists are key players in Arctic aquatic ecosystems, yet little is known about their ecological roles over yearly cycles. This is especially true for the dark polar night period, which up until recently was assumed to be devoid of biological activity. A 12 million transcripts catalogue was built from 0.45-10 μm protist assemblages sampled over 13 months in a time series station in an arctic fjord in Svalbard. Community gene expression was correlated with seasonality, with light as the main driving factor. Transcript diversity and evenn…

0106 biological sciencesClimate changemicrobial eukaryotesBiologyunicellular eukaryotesmedicine.disease_cause01 natural sciences03 medical and health sciencespolar daymedicineEcosystem14. Life underwater030304 developmental biology[SDV.EE]Life Sciences [q-bio]/Ecology environment0303 health sciencesmetatranscriptomicsPolar nightpolar nightEcology010604 marine biology & hydrobiologyAquatic ecosystemProtistSeasonalitymedicine.disease[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]Arctic13. Climate actionSpecies evennesstime seriesgeographic locations
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The gypsy database (GyDB) of mobile genetic elements: release 2.0

2011

This article introduces the second release of the Gypsy Database of Mobile Genetic Elements (GyDB 2.0): a research project devoted to the evolutionary dynamics of viruses and transposable elements based on their phylogenetic classification (per lineage and protein domain). The Gypsy Database (GyDB) is a long-term project that is continuously progressing, and that owing to the high molecular diversity of mobile elements requires to be completed in several stages. GyDB 2.0 has been powered with a wiki to allow other researchers participate in the project. The current database stage and scope are long terminal repeats (LTR) retroelements and relatives. GyDB 2.0 is an update based on the analys…

0106 biological sciencesProtein domainretroelementsLineage (evolution)[SDV]Life Sciences [q-bio]Retroviridae ProteinsCaulimoviridaeEukaryote evolutioncomputer.software_genrephylogeny01 natural sciencesDatabases GeneticRefSeqPhylogenyPriority journalbase de données0303 health sciencesRetrovirusPhylogenetic treeDatabaseSequence analysisdatabases geneticArticlesClassificationChemistryGenetic lineRetroelementsGenetic databaseComputer programBiologyArticleMobile genetic element03 medical and health sciencesLong terminal repeatWeb pagephylogénieVirus proteinGeneticsLife Science[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyAccess to informationTransposon030304 developmental biologyretroelements;phylogeny;software;terminal repeat sequences;databases geneticHidden Markov modelCauliflower mosaic virusCaulimovirussoftwareRetroposonTerminal Repeat SequencesDNA structureInterspersed Repetitive Sequencesterminal repeat sequencesNonhumanRetroviridaeData analysis softwareGenetic variabilityMobile genetic elementscomputerLENGUAJES Y SISTEMAS INFORMATICOSSoftware010606 plant biology & botanyPhylogenetic nomenclaturePhylogenetic tree
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Deep learning architectures for prediction of nucleosome positioning from sequences data

2018

Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…

0301 basic medicineComputer scienceCellBiochemistrychemistry.chemical_compound0302 clinical medicineStructural Biologylcsh:QH301-705.5Nucleosome classificationSequenceSettore INF/01 - InformaticabiologyApplied MathematicsEpigeneticComputer Science ApplicationsChromatinNucleosomesmedicine.anatomical_structurelcsh:R858-859.7EukaryoteDNA microarrayDatabases Nucleic AcidComputational biologySaccharomyces cerevisiaelcsh:Computer applications to medicine. Medical informatics03 medical and health sciencesDeep LearningmedicineNucleosomeAnimalsHumansEpigeneticsMolecular BiologyGeneBase Sequencebusiness.industryDeep learningResearchReproducibility of Resultsbiology.organism_classificationYeastNucleosome classification Epigenetic Deep learning networks Recurrent neural networks030104 developmental biologylcsh:Biology (General)chemistryRecurrent neural networksROC CurveDeep learning networksArtificial intelligenceNeural Networks Computerbusiness030217 neurology & neurosurgeryDNABMC Bioinformatics
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Deep learning network for exploiting positional information in nucleosome related sequences

2017

A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…

0301 basic medicineComputer scienceSpeech recognitionCell02 engineering and technologyComputational biologyGenomeDNA sequencing03 medical and health scienceschemistry.chemical_compoundDeep Learning0202 electrical engineering electronic engineering information engineeringmedicineNucleosomeNucleotideGeneSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionichemistry.chemical_classificationSequenceSettore INF/01 - Informaticabiologybusiness.industryDeep learningnucleosomebiology.organism_classificationSubstringChromatinIdentification (information)030104 developmental biologymedicine.anatomical_structurechemistry020201 artificial intelligence & image processingEukaryoteNucleosome classification Epigenetic Deep learning networks Recurrent Neural NetworksArtificial intelligencebusinessDNA
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Genomic and functional gene studies suggest a key role of beta-carotene oxygenase 1 like (bco1l) gene in salmon flesh color

2019

AbstractRed coloration of muscle tissue (flesh) is a unique trait in several salmonid genera, including Atlantic salmon. The color results from dietary carotenoids deposited in the flesh, whereas the color intensity is affected both by diet and genetic components. Herein we report on a genome-wide association study (GWAS) to identify genetic variation underlying this trait. Two SNPs on ssa26 showed strong associations to the flesh color in salmon. Two genes known to be involved in carotenoid metabolism were located in this QTL- region: beta-carotene oxygenase 1 (bco1) and beta-carotene oxygenase 1 like (bco1l). To determine whether flesh color variation is caused by one, or both, of these g…

0301 basic medicineOxygenasegenetic structuresQuantitative Trait LociSalmo salarPopulationlcsh:MedicineGenome-wide association studySingle-nucleotide polymorphismQuantitative trait locusBiologyArticle03 medical and health sciencesstomatognathic systemGenetic variationAnimalsVDP::Landbruks- og Fiskerifag: 900::Fiskerifag: 920::Fiskehelse: 923lcsh:ScienceeducationCarotenoidGenebeta-Carotene 1515'-Monooxygenasechemistry.chemical_classificationGeneticseducation.field_of_studyMultidisciplinary030102 biochemistry & molecular biologyPigmentationEukaryoteFleshlcsh:Rfood and beveragesGenomicsbeta CaroteneEnzymes030104 developmental biologychemistrylcsh:QGenome-Wide Association Study
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Informational, ecological and system approaches for complete genome analysis

2012

In the study of the genetic modifications leading populations to adapt to their environment, it is important to distinguish changes resulting in an increase in biological fiteness from those slightly deleterious. Besides that the concept of neutral changes is defined since the ¿Origin of species¿ by Charles Darwin, its relevance to the overall changes defining evolutionary process was considered to be very low, if existent. But, in the late sixties, with the advances of molecular experiments and the first comparative studies in this field, neutral changes were proven to be almost sufficient to explain the amount of changes per generation observed at molecular level. Based on this observatio…

:CIENCIAS DE LA VIDA::Genética ::Otras [UNESCO]eukaryotesUNESCO::CIENCIAS DE LA VIDA::Biología molecularevolutionadaptationecologygenome:CIENCIAS DE LA VIDA::Biología molecular [UNESCO]UNESCO::CIENCIAS DE LA VIDA::Genética ::Otras
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Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms.

2015

International audience; Diatoms are one of the most ecologically successful classes of photosynthetic marine eukaryotes in the contemporary oceans. Over the past 30 million years, they have helped to moderate Earth's climate by absorbing carbon dioxide from the atmosphere, sequestering it via the biological carbon pump and ultimately burying organic carbon in the lithosphere. The proportion of planetary primary production by diatoms in the modern oceans is roughly equivalent to that of terrestrial rainforests. In photosynthesis, the efficient conversion of carbon dioxide into organic matter requires a tight control of the ATP/NADPH ratio which, in other photosynthetic organisms, relies prin…

Aquatic Organismschemistry.chemical_compoundAdenosine TriphosphateSettore BIO/04 - Fisiologia VegetaleCYCLIC ELECTRON FLOWPlastidsPhotosynthesisPHAEODACTYLUM-TRICORNUTUMPlant Proteinschemistry.chemical_classificationMultidisciplinarymicroalgaeRespirationCarbon fixationEnergetic interactionsProton-Motive ForceMitochondriametabolic mutantPhenotypeATP/NADPH ratioOXYGEN PHOTOREDUCTIONCarbon dioxideOxidoreductasesOxidation-ReductionOceanOceans and SeasElectron flowMarine eukaryotesBiologyPhotosynthesisCHLAMYDOMONAS-REINHARDTIICarbon cycleCarbon CycleMitochondrial ProteinsEnergetic exchangesBotanyOrganic matterEcosystem[SDV.BBM]Life Sciences [q-bio]/Biochemistry Molecular Biology14. Life underwaterPlastidEcosystemDiatomsChemiosmosisfungiECSCarbon Dioxidechemistry13. Climate actionNADP
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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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Microbial and meiofaunal response to intensive mussel-farm biodeposition in coastal sediments of the Western Mediterranean

2000

We studied the impact of organic loads due to the biodeposition of a mussel farm in a coastal area of the Tyrrhenian Sea (Western Mediterranean). Sediment chemistry, microbial and meiofaunal assemblages were investigated from March 1997 to February 1998 on monthly basis at two stations: the first was located under the mussel farm, while the second was at about 1-km distance and served as control. Benthic response to changes in the biodepositional regime was investigated in terms of biochemical composition of the sedimentary organic matter, phytopigment content, bacterial abundance and composition and meiofaunal community structure. A large accumulation of chloroplastic pigments, proteins an…

Mediterranean climateSettore BIO/07 - EcologiaBiomass (ecology)animal structuresBacteriaEcologyMeiobenthosMeiofaunafungiCommunity structureMusselAquatic ScienceCyanobacteriaOceanographyPollutionmussel farm; bacteria; cyanobacteria; picoeukaryotes; meiofauna; Mediterranean SeaOceanographyMediterranean seaBenthic zoneMediterranean SeaMussel farmSedimentary organic matterEnvironmental sciencePicoeukaryote
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Biological properties of extracellular vesicles and their physiological functions

2015

The authors wish to thank Dr R Simpson and Dr D Taylor for critical reading of the manuscript and acknowledge the Horizon 2020 European Cooperation in Science and Technology programme and its support of our European Network on Microvesicles and Exosomes in Health & Disease (ME-HaD; BM1202 www.cost.eu/COST_Actions/bmbs/Actions/BM1202). In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive invest…

ProteomicsCellular distributionMATURE DENDRITIC CELLSReviewReview ArticleUrineEmbryo developmentMonocyteProtein processingVascular biologyFecesVesícules seminalsSYNCYTIOTROPHOBLAST MICROVILLOUS MEMBRANESCell selectionPregnancyT lymphocyteBileCELL-DERIVED EXOSOMESBiogenesisLung lavageUterus fluidInnate immunityMale genital systemlcsh:CytologyMicrovesicleOUTER-MEMBRANE VESICLESBlood clottingprokaryoteEukaryotaExtracellular vesicleRNA analysisCell biologyBloodCerebrospinal fluidLiver metabolismmicrovesicleMorphogenHumanNervous systemCell signalingBreast milkNatural killer cellFisiologiaExtracellular vesiclesExosomelcsh:QH573-671SalivaBiologyBiology and Life SciencesDNAPlantRNA transportCell functionMacrophageMolecular biologyPhysiologyMedizinProteomicsFACTOR PATHWAY INHIBITOReukaryoteProtein glycosylationExtracellular spaceTissue repairEspai extracel·lularReticulocyteSeminal plasmaMesenchymal stem cellAntigen presenting cellSeminal vesiclesNose mucusBiofilmNeutrophilMicroRNAPLANT-MICROBE INTERACTIONSLipidAmnion fluidProkaryotamicroparticleCell interactionCell transporteukaryote exosome extracellular vesicle microparticle microvesicle physiology prokaryoteBone mineralizationMicroorganismHistologyAdaptive immunityMembrane vesicleComputational biologyMembrane receptorBiologyStressCell communicationMast cellMESENCHYMAL STEM-CELLSHUMAN ENDOTHELIAL-CELLSexosomeCytokineSynovial fluidCell BiologyNonhumanIMMUNE-MODULATORY FEATURESReview articleDNA contentphysiologyRNAINTESTINAL EPITHELIAL-CELLSextracellular vesicleBody fluidLectinBiogenesis
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