Search results for "k-mer counting"

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Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms

2018

Abstract Motivation Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in…

0301 basic medicineEpigenomicsgenomic analysis; hadoop; distributed computingStatistics and ProbabilityComputer scienceBig dataSequence assemblyGenomeBiochemistryDomain (software engineering)Set (abstract data type)03 medical and health sciencesdistributed computingSoftwareComputational Theory and MathematicAnimalsCluster AnalysisHumansA-DNAk-mer counting distributed computing hadoop map reduceMolecular BiologyEpigenomicsBacteriabusiness.industryk-mer countingEukaryotaLinguisticsComputer Science Applications1707 Computer Vision and Pattern RecognitionGenomicsSequence Analysis DNAComputer Science ApplicationsComputational Mathematics030104 developmental biologymap reduceComputational Theory and MathematicsDistributed algorithmgenomic analysisKernel (statistics)MetagenomehadoopbusinessAlgorithmAlgorithmsSoftware
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Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics

2019

Abstract Background Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the …

Data AnalysisFOS: Computer and information sciencesTime FactorsTime FactorComputer scienceStatistics as TopicBig dataApache Spark; distributed computing; performance evaluation; k-mer countinglcsh:Computer applications to medicine. Medical informaticsBiochemistryDomain (software engineering)Databases03 medical and health sciences0302 clinical medicineStructural BiologyComputer clusterStatisticsSpark (mathematics)Molecular Biologylcsh:QH301-705.5030304 developmental biology0303 health sciencesGenomeSettore INF/01 - InformaticaBase SequenceNucleic AcidApache Sparkbusiness.industryResearchApache Spark; Distributed computing; k-mer counting; Performance evaluation; Algorithms; Base Sequence; Software; Time Factors; Data Analysis; Databases Nucleic Acid; Genome; Statistics as TopicApplied Mathematicsk-mer countingDistributed computingComputer Science ApplicationsAlgorithmData AnalysiComputer Science - Distributed Parallel and Cluster Computinglcsh:Biology (General)030220 oncology & carcinogenesisScalabilityPerformance evaluationlcsh:R858-859.7Algorithm designDistributed Parallel and Cluster Computing (cs.DC)Databases Nucleic AcidbusinessAlgorithmsSoftware
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