Search results for "algorithms"

showing 10 items of 1716 documents

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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Reactome diagram viewer: data structures and strategies to boost performance

2017

Abstract Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partit…

0301 basic medicineStatistics and ProbabilityDatabases FactualComputer scienceKnowledge BasesDatabases and OntologiesBiochemistryWorld Wide Web03 medical and health sciences0302 clinical medicineHumansMolecular BiologyInternetComputational BiologyData structureOriginal PapersComputer Science ApplicationsVisualizationComputational Mathematics030104 developmental biologyComputational Theory and Mathematics030220 oncology & carcinogenesisScalabilityAlgorithmsMetabolic Networks and PathwaysSoftwareBioinformatics
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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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L1-Penalized Censored Gaussian Graphical Model

2018

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…

0301 basic medicineStatistics and ProbabilityFOS: Computer and information sciencesgraphical lassoComputer scienceGaussianNormal DistributionInferenceMultivariate normal distribution01 natural sciencesMethodology (stat.ME)010104 statistics & probability03 medical and health sciencessymbols.namesakeGraphical LassoExpectation–maximization algorithmHumansComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsStatistics - MethodologyEstimation theoryReverse Transcriptase Polymerase Chain ReactionEstimatorexpectation-maximization algorithmGeneral MedicineCensoring (statistics)High-dimensional datahigh-dimensional dataGaussian graphical model030104 developmental biologysymbolscensored dataCensored dataExpectation-Maximization algorithmStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmAlgorithms
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Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

2016

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…

0301 basic medicineStatistics and ProbabilityFactorialDependency (UML)Computer scienceGaussianNormal Distributionpenalized inferencesparse networkscomputer.software_genreMachine learning01 natural sciencesNormal distribution010104 statistics & probability03 medical and health sciencessymbols.namesakeSparse networksGeneticsComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsgene-regulatory systemMolecular BiologyProbabilityMarkov chainModels GeneticPenalized inferencebusiness.industryModel selectiongraphical modelGene-regulatory systemsComputational Mathematics030104 developmental biologysymbolsA priori and a posterioriData miningArtificial intelligenceGraphical modelsSettore SECS-S/01 - StatisticabusinesscomputerNeisseriaAlgorithmsStatistical applications in genetics and molecular biology
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The latent geometry of the human protein interaction network

2017

Abstract Motivation A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins. Results We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperboli…

0301 basic medicineStatistics and ProbabilityGeometric analysisComputer scienceHyperbolic geometrySystems biologyComplex systemContext (language use)GeometryBiochemistryProtein–protein interaction03 medical and health sciencesInteraction networkHumansProtein Interaction MapsRepresentation (mathematics)Cluster analysisMolecular BiologySystems BiologyHyperbolic spaceProteinsFunction (mathematics)Original PapersComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicsEmbeddingSignal transductionAlgorithmsSignal Transduction
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Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.

2017

International audience; Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algori…

0301 basic medicineStatistics and ProbabilityMALDI-TOFexperimental designBiometryprotein quantificationQuantitative proteomicsVariance component analysis[ CHIM ] Chemical Sciences01 natural sciencesSignaltechnological variability010104 statistics & probability03 medical and health sciencesstatistical analysis[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[CHIM.ANAL]Chemical Sciences/Analytical chemistryComponent (UML)[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]biomarker discoverysum of squares type0101 mathematicsBiomarker discoverysignal processingMathematicsSignal processingAnalysis of Variance[ PHYS ] Physics [physics]Noise (signal processing)ProteinsGeneral MedicineVariance (accounting)[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]030104 developmental biologySpectrometry Mass Matrix-Assisted Laser Desorption-IonizationLinear Modelsvariance components[ CHIM.ANAL ] Chemical Sciences/Analytical chemistryStatistics Probability and UncertaintyBiological systemAlgorithmsBiomarkersBiometrical journal. Biometrische Zeitschrift
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MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems

2016

This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of recordJorge González-Domínguez, Yongchao Liu, Juan Touriño, Bertil Schmidt; MSAProbs-MPI: parallel multiple sequence aligner for distributed-memory systems, Bioinformatics, Volume 32, Issue 24, 15 December 2016, Pages 3826–3828, https://doi.org/10.1093/bioinformatics/btw558is available online at: https://doi.org/10.1093/bioinformatics/btw558 [Abstracts] MSAProbs is a state-of-the-art protein multiple sequence alignment tool based on hidden Markov models. It can achieve high alignment accuracy at the expense of relatively long runtimes for large-sca…

0301 basic medicineStatistics and ProbabilitySource codeComputer sciencemedia_common.quotation_subject02 engineering and technologyParallel computingcomputer.software_genreBiochemistryExecution time03 medical and health sciences0202 electrical engineering electronic engineering information engineeringCluster (physics)Point (geometry)Amino Acid SequenceMolecular Biologymedia_commonSequenceMultiple sequence alignmentProtein multiple sequenceComputational BiologyProteinsMarkov ChainsComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicsDistributed memory systemsMSAProbs020201 artificial intelligence & image processingMPIData miningSequence AlignmentcomputerAlgorithmsSoftware
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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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Deep learning models for bacteria taxonomic classification of metagenomic data.

2018

Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…

0301 basic medicineTime FactorsDBNComputer scienceBiochemistryStructural BiologyRNA Ribosomal 16SDatabases Geneticlcsh:QH301-705.5Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazionibiologySettore INF/01 - InformaticaShotgun sequencingApplied MathematicsAmpliconClassificationComputer Science Applicationslcsh:R858-859.7DNA microarrayShotgunAlgorithmsCNN030106 microbiologyk-mer representationlcsh:Computer applications to medicine. Medical informaticsDNA sequencing03 medical and health sciencesMetagenomicDeep LearningMolecular BiologyBacteriaModels GeneticPhylumbusiness.industryDeep learningResearchReproducibility of ResultsPattern recognitionBiological classification16S ribosomal RNAbiology.organism_classificationAmpliconHypervariable region030104 developmental biologyTaxonlcsh:Biology (General)MetagenomicsMetagenomeArtificial intelligenceMetagenomicsNeural Networks ComputerbusinessClassifier (UML)BacteriaBMC bioinformatics
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