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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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