Search results for "High-throughput"

showing 10 items of 292 documents

New Insights into the Genome Organization of Yeast Killer Viruses Based on “Atypical” Killer Strains Characterized by High-Throughput Sequencing

2017

Viral M-dsRNAs encoding yeast killer toxins share similar genomic organization, but no overall sequence identity. The dsRNA full-length sequences of several known M-viruses either have yet to be completed, or they were shorter than estimated by agarose gel electrophoresis. High-throughput sequencing was used to analyze some M-dsRNAs previously sequenced by traditional techniques, and new dsRNAs from atypical killer strains of Saccharomyces cerevisiae and Torulaspora delbrueckii. All dsRNAs expected to be present in a given yeast strain were reliably detected and sequenced, and the previously-known sequences were confirmed. The few discrepancies between viral variants were mostly located aro…

0301 basic medicineRNA recombinationGenotypeHealth Toxicology and Mutagenesis030106 microbiologySaccharomyces cerevisiaelcsh:MedicineTorulaspora delbrueckiidsRNAGenome ViralSaccharomyces cerevisiaeToxicologyGenomeDNA sequencingArticle<i>Saccharomyces cerevisiae</i>; <i>Torulaspora delbrueckii</i>; killer; virus genome; dsRNA; sequencing; HTS; RNA recombination; phylogenetic originphylogenetic origin03 medical and health sciencesTorulaspora delbrueckiiGenomic organizationGeneticsbiologyPhylogenetic treelcsh:RHigh-Throughput Nucleotide SequencingTorulasporasequencingbiology.organism_classificationYeastTorulasporaKiller Factors Yeast030104 developmental biologyPhenotypevirus genomeVirusesRNA ViralHTSkillerToxins
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Impact of poplar-based phytomanagement on soil properties and microbial communities in a metal-contaminated site

2016

Despite a long history of use in phytomanagement strategies, the impacts of poplar trees on the structure and function of microbial communities that live in the soil remain largely unknown. The current study combined fungal and bacterial community analyses from different management regimes using Illumina-based sequencing with soil analysis. The poplar phytomanagement regimes led to a significant increase in soil fertility and a decreased bioavailability of Zn and Cd, in concert with changes in the microbial communities. The most notable changes in the relative abundance of taxa and operational taxonomic units unsurprisingly indicated that root and soil constitute distinct ecological microbi…

0301 basic medicineSoil testMicrobial ConsortiaEnvironmentPlant RootsApplied Microbiology and BiotechnologyMicrobiology[ SDV.EE ] Life Sciences [q-bio]/Ecology environmentSoil03 medical and health sciencesMicrobial ecologyMycorrhizaeSoil PollutantsDominance (ecology)Relative species abundanceComputingMilieux_MISCELLANEOUSEcosystemSoil Microbiology[SDV.EE]Life Sciences [q-bio]/Ecology environment2. Zero hungerLaccariaEcologybiologyEcologyfungiHigh-Throughput Nucleotide Sequencingfood and beverages15. Life on landbiology.organism_classificationBiodegradation EnvironmentalPopulus030104 developmental biologyAgronomyHabitatPenicillium canescensMetalsSoil fertilityFEMS Microbiology Ecology
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SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations

2016

Various approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated. We present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a mult…

0301 basic medicineSomatic cellBayesian probabilityBiologyPolymorphism Single NucleotideGermline03 medical and health sciencesGene FrequencyINDEL MutationStructural BiologyModelling and SimulationIndel callingGenetic variationHumansAlleleIndelMolecular BiologyOvarian NeoplasmsGeneticsResearchApplied MathematicsComputational BiologyHigh-Throughput Nucleotide SequencingSNP callingSomatic SNV callingCystadenocarcinoma SerousComputer Science ApplicationsGerm Cells030104 developmental biologyBayesian modelModeling and SimulationMutation (genetic algorithm)FemaleMultinomial distributionAlgorithmsBMC Systems Biology
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MassARRAY determination of somatic oncogenic mutations in solid tumors: Moving forward to personalized medicine.

2016

This article will review the impact of the recently developed MassARRAY technology on our understanding of cancer biology and treatment. Analysis of somatic mutations is a useful tool in selecting personalized therapy, and for predicting the outcome of many solid tumors. Here, we review the literature on the application of MassARRAY technology (Sequenom Hamburg, Germany) to determine the mutation profile of solid tumors from patients. We summarize the use of commercially available panels of mutations - such as OncoCarta™ or other combinations - and their concordance with results obtained by using other technologies, such as next generation sequencing.

0301 basic medicineSomatic cellConcordanceComputational biologymedicine.disease_causeBioinformatics03 medical and health sciences0302 clinical medicineNeoplasmsMedicineHumansRadiology Nuclear Medicine and imagingCancer biologyPersonalized therapyPrecision MedicineOligonucleotide Array Sequence AnalysisMutationbusiness.industryHigh-Throughput Nucleotide SequencingGeneral MedicineOncogenesPrecision medicine030104 developmental biologyOncology030220 oncology & carcinogenesisMutationPersonalized medicinebusinessCancer treatment reviews
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MetaCache: context-aware classification of metagenomic reads using minhashing.

2017

Abstract Motivation Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy. Results We introduce MetaCache—a novel software for read classification using the big data technique minhashing. Our…

0301 basic medicineStatistics and ProbabilityComputer scienceSequence analysisContext (language use)BiochemistryGenome03 medical and health scienceschemistry.chemical_compound0302 clinical medicineRefSeqHumansMolecular BiologyInformation retrievalShotgun sequencingHigh-Throughput Nucleotide SequencingSequence Analysis DNAComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicschemistryMetagenomicsMetagenomics030217 neurology & neurosurgeryDNAAlgorithmsSoftwareReference genomeBioinformatics (Oxford, England)
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ParDRe: faster parallel duplicated reads removal tool for sequencing studies

2016

This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record [insert complete citation information here] is available online at: https://doi.org/10.1093/bioinformatics/btw038 [Abstract] Summary: Current next generation sequencing technologies often generate duplicated or near-duplicated reads that (depending on the application scenario) do not provide any interesting biological information but can increase memory requirements and computational time of downstream analysis. In this work we present ParDRe , a de novo parallel tool to remove duplicated and near-duplicated reads through the clustering of S…

0301 basic medicineStatistics and ProbabilityFASTQ formatDNA stringsSource codeDownstream (software development)Computer sciencemedia_common.quotation_subjectParallel computingcomputer.software_genreBiochemistryDNA sequencing03 medical and health scienceschemistry.chemical_compound0302 clinical medicineHybrid MPI/multithreadingCluster AnalysisParDReMolecular BiologyGenemedia_commonHigh-Throughput Nucleotide SequencingSequence Analysis DNAParallel toolComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicschemistryData miningcomputerAlgorithms030217 neurology & neurosurgeryDNABioinformatics
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panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.

2018

Abstract Motivation The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic spec…

0301 basic medicineStatistics and ProbabilityLineage (genetic)Computer scienceAb initioComputational biologyBacterial genome size[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]BiochemistryGenome[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing03 medical and health sciences[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]Insertion sequenceMolecular BiologyGenomic organizationHigh-Throughput Nucleotide SequencingSequence Analysis DNA[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/BacteriologyPipeline (software)[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and Mathematics[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]DNA Transposable Elements[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Genome BacterialSoftwareBioinformatics (Oxford, England)
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AFS: identification and quantification of species composition by metagenomic sequencing

2017

Abstract Summary DNA-based methods to detect and quantify taxon composition in biological materials are often based on species-specific polymerase chain reaction, limited to detecting species targeted by the assay. Next-generation sequencing overcomes this drawback by untargeted shotgun sequencing of whole metagenomes at affordable cost. Here we present AFS, a software pipeline for quantification of species composition in food. AFS uses metagenomic shotgun sequencing and sequence read counting to infer species proportions. Using Illumina data from a reference sausage comprising four species, we reveal that AFS is independent of the sequencing assay and library preparation protocol. Cost-sav…

0301 basic medicineStatistics and ProbabilitySequence analysisLibrary preparationComputational biologyBiologyBioinformaticsBiochemistrylaw.invention03 medical and health sciences0404 agricultural biotechnologylawMolecular BiologyPolymerase chain reactionShotgun sequencingHigh-Throughput Nucleotide SequencingSequence Analysis DNA04 agricultural and veterinary sciencesAccession number (bioinformatics)040401 food scienceBiological materialsComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicsMetagenomicsFood MicrobiologyIdentification (biology)MetagenomicsSoftwareBioinformatics
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Do next-generation sequencing results drive diagnostic and therapeutic decisions in MDS?

2019

Este artículo se encuentra disponible en la siguiente URL: https://ashpublications.org/bloodadvances/article/3/21/3454/422749/Do-next-generation-sequencing-results-drive

0301 basic medicineSíndromes mielodisplásicos - Aspectos moleculares.Clinical Decision-MakingMEDLINEComputational biologyDNA sequencing03 medical and health sciences0302 clinical medicineText miningHumansMedicineGenetic Predisposition to DiseaseSangre - Células - Aspectos moleculares.Molecular Targeted TherapyGenes.Genetic Association StudiesBlood cells - Molecular aspects.business.industryDecision TreesDisease ManagementHigh-Throughput Nucleotide SequencingGenomicsHematologyPrognosisCombined Modality TherapyMyelodysplastic syndrome - Molecular aspects.030104 developmental biologyMyelodysplastic Syndromes030220 oncology & carcinogenesisMutationPoint-CounterpointMolecular biology.Biología molecular.businessBiomarkersBlood Advances
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Parallel and Space-Efficient Construction of Burrows-Wheeler Transform and Suffix Array for Big Genome Data

2016

Next-generation sequencing technologies have led to the sequencing of more and more genomes, propelling related research into the era of big data. In this paper, we present ParaBWT, a parallelized Burrows-Wheeler transform (BWT) and suffix array construction algorithm for big genome data. In ParaBWT, we have investigated a progressive construction approach to constructing the BWT of single genome sequences in linear space complexity, but with a small constant factor. This approach has been further parallelized using multi-threading based on a master-slave coprocessing model. After gaining the BWT, the suffix array is constructed in a memory-efficient manner. The performance of ParaBWT has b…

0301 basic medicineTheoretical computer scienceBurrows–Wheeler transformComputer scienceGenomicsData_CODINGANDINFORMATIONTHEORYParallel computingGenomelaw.invention03 medical and health scienceslawGeneticsHumansEnsemblMulti-core processorApplied MathematicsLinear spaceSuffix arrayChromosome MappingHigh-Throughput Nucleotide SequencingGenomicsSequence Analysis DNA030104 developmental biologyAlgorithmsBiotechnologyReference genomeIEEE/ACM Transactions on Computational Biology and Bioinformatics
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