Robert Bjornson
Director of Computational Research SupportDownloadHi-Res Photo
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Director of Computational Research Support
Biography
Dr. Robert Bjornson, a Senior Research Scientist in Computer Science, has a background in parallel computing and bioinformatics. He has extensive experience with HPC algorithms and software in both academic and commercial environments. Dr. Bjornson manages the high performance computing clusters for the Biological & Biomedical Sciences, and the Keck Biomedical HPC facility. In addition to providing training, consultation, programming support, and debugging assistance for user applications, he manages the installation and availability of a variety of HPC software applications, libraries, and tools. Dr. Bjornson holds a Ph.D. in Computer Science from Yale University.
Departments & Organizations
- Center for Biomedical Data Science
- High Performance Computation
- Keck
Research
Research at a Glance
Yale Co-Authors
Frequent collaborators of Robert Bjornson's published research.
Publications Timeline
A big-picture view of Robert Bjornson's research output by year.
Alexej Abyzov, PhD
Yong Kong, PhD
Adalgisa Caccone
Mark Gerstein, PhD
Ruth Halaban, PhD
18Publications
1,374Citations
Publications
2020
Using blockchain to log genome dataset access: efficient storage and query
Gürsoy G, Bjornson R, Green ME, Gerstein M. Using blockchain to log genome dataset access: efficient storage and query. BMC Medical Genomics 2020, 13: 78. PMID: 32693796, PMCID: PMC7372787, DOI: 10.1186/s12920-020-0716-z.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsEfficient queryData streamsMemory requirementsSecure genome analysis competitionAccess log filesSecure data storageProperties of securityMB of memoryAccess logsFast queriesSensitive informationBlockchain platformSecure storageBlockchain technologyUser activityElectronic health recordsLog filesLocal storageRapid queryEfficient storageQueriesData storageDataset accessMore memoryData frames
2014
A spatial simulation approach to account for protein structure when identifying non-random somatic mutations
Ryslik GA, Cheng Y, Cheung KH, Bjornson RD, Zelterman D, Modis Y, Zhao H. A spatial simulation approach to account for protein structure when identifying non-random somatic mutations. BMC Bioinformatics 2014, 15: 231. PMID: 24990767, PMCID: PMC4227039, DOI: 10.1186/1471-2105-15-231.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsIdentification of PLX4032‐resistance mechanisms and implications for novel RAF inhibitors
Choi J, Landrette SF, Wang T, Evans P, Bacchiocchi A, Bjornson R, Cheng E, Stiegler AL, Gathiaka S, Acevedo O, Boggon TJ, Krauthammer M, Halaban R, Xu T. Identification of PLX4032‐resistance mechanisms and implications for novel RAF inhibitors. Pigment Cell & Melanoma Research 2014, 27: 253-262. PMID: 24283590, PMCID: PMC4065135, DOI: 10.1111/pcmr.12197.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAmino Acid SequenceCell Line, TumorCell ProliferationDNA Transposable ElementsDrug Resistance, NeoplasmHumansIndolesMAP Kinase Signaling SystemMelanomaModels, MolecularMolecular Sequence DataMutagenesis, InsertionalMutant ProteinsMutationProtein Kinase InhibitorsProto-Oncogene Proteins B-rafSulfonamidesVemurafenibConceptsBRAF mutationsNovel BRAF mutationBRAF inhibitorsNext-generation BRAF inhibitorsPLX4032-resistant melanoma cellsMelanoma cellsMelanoma patient survivalHuman prostate cancerBRAF mutant cellsWhole-exome sequencingMelanoma patientsPatient survivalClinical trialsProstate cancerRAF inhibitorsOncogenic mutationsNew screening approachRelevant aberrationsInhibitorsCellsMutationsScreening approachNovel RAF inhibitorsPatientsPLX8394
2013
Low-copy piggyBac transposon mutagenesis in mice identifies genes driving melanoma
Ni TK, Landrette SF, Bjornson RD, Bosenberg MW, Xu T. Low-copy piggyBac transposon mutagenesis in mice identifies genes driving melanoma. Proceedings Of The National Academy Of Sciences Of The United States Of America 2013, 110: e3640-e3649. PMID: 24003131, PMCID: PMC3780872, DOI: 10.1073/pnas.1314435110.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAnimalsBlotting, WesternDNA PrimersDNA Transposable ElementsGene Expression Regulation, NeoplasticGenetic TestingHEK293 CellsHumansImmunohistochemistryMAP Kinase Kinase Kinase 1MelanomaMiceMice, TransgenicMutagenesis, InsertionalReverse Transcriptase Polymerase Chain ReactionSignal TransductionSpecies SpecificityConceptsCancer-driving genesMitogen-activated protein kinase kinase kinase 1Membrane associated guanylate kinaseProtein kinase kinase kinase 1Kinase kinase kinase 1Protein tyrosine phosphataseTransposon mutagenesis approachKinase kinase 1Transposon mutagenesis screenHuman melanomaBackground mutation rateMelanoma driver genesUndescribed genesIdentifies genesMutagenesis screenPDZ domainGuanylate kinaseTyrosine phosphataseTransposon mutagenesisCellular transformationMutagenesis approachKinase 1Mutation rateERK signalingDriver genes
2012
Insight into the Transmission Biology and Species-Specific Functional Capabilities of Tsetse (Diptera: Glossinidae) Obligate Symbiont Wigglesworthia
Rio RV, Symula RE, Wang J, Lohs C, Wu YN, Snyder AK, Bjornson RD, Oshima K, Biehl BS, Perna NT, Hattori M, Aksoy S. Insight into the Transmission Biology and Species-Specific Functional Capabilities of Tsetse (Diptera: Glossinidae) Obligate Symbiont Wigglesworthia. MBio 2012, 3: 10.1128/mbio.00240-11. PMID: 22334516, PMCID: PMC3280448, DOI: 10.1128/mbio.00240-11.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAmino Acid SequenceAnimalsChorismic AcidDNA, BacterialEvolution, MolecularFlagellaFolic AcidGene Expression Regulation, BacterialGenome, BacterialGenome, InsectImmunohistochemistryInheritance PatternsMolecular Sequence DataPhenylalaninePlasmidsSpecies SpecificitySymbiosisSyntenyTranscription, GeneticTsetse FliesWigglesworthiaConceptsSister speciesObligate endosymbiontsTranscriptional regulationHost speciesDevelopment-specific gene expressionTissue-specific functional rolesRobust transcriptional regulationTsetse fliesHigher parasite susceptibilityRole of flagellaAncient endosymbiontGenomic stasisSymbiont genomesSymbiont transmissionGene inventoryHigh syntenySister genomesSmall genomesExtensive conservationUnique genesReproductive biologyFunctional biologyTranscriptional analysisHost developmentLarge inversions
2011
Influence of Host Phylogeographic Patterns and Incomplete Lineage Sorting on Within-Species Genetic Variability in Wigglesworthia Species, Obligate Symbionts of Tsetse Flies
Symula RE, Marpuri I, Bjornson RD, Okedi L, Beadell J, Alam U, Aksoy S, Caccone A. Influence of Host Phylogeographic Patterns and Incomplete Lineage Sorting on Within-Species Genetic Variability in Wigglesworthia Species, Obligate Symbionts of Tsetse Flies. Applied And Environmental Microbiology 2011, 77: 8400-8408. PMID: 21948847, PMCID: PMC3233068, DOI: 10.1128/aem.05688-11.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsObligate symbiontsHost speciesGenetic variationMultiple host populationsIncongruence length difference testIncomplete lineage sortingHost-symbiont combinationsSingle host speciesPopulation-level processesIncomplete lineageLineage sortingPhylogeographic patternsSouthern lineagesSymbiont lineagesHost mtDNAWigglesworthia glossinidiaMolecular varianceBacterial symbiontsEvolutionary associationEvolutionary historySouthern populationsMitochondrial DNA haplogroupsVariable lociSymbiontsGenetic variabilityPower of Data Mining Methods to Detect Genetic Associations and Interactions
Molinaro AM, Carriero N, Bjornson R, Hartge P, Rothman N, Chatterjee N. Power of Data Mining Methods to Detect Genetic Associations and Interactions. Human Heredity 2011, 72: 85-97. PMID: 21934324, PMCID: PMC3222116, DOI: 10.1159/000330579.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMonte Carlo logic regressionRandom forestVariable importance measuresRF variable importance measuresData mining methodsComplex variable interactionsMining methodsTree-based methodsDimensionality reductionPrediction modelSuch methodsImportance measuresLogic regressionSimulation modelMultifactor dimensionality reductionData analysisVariable interactionsAlgorithmSimulation studyACT: aggregation and correlation toolbox for analyses of genome tracks
Jee J, Rozowsky J, Yip KY, Lochovsky L, Bjornson R, Zhong G, Zhang Z, Fu Y, Wang J, Weng Z, Gerstein M. ACT: aggregation and correlation toolbox for analyses of genome tracks. Bioinformatics 2011, 27: 1152-1154. PMID: 21349863, PMCID: PMC3072554, DOI: 10.1093/bioinformatics/btr092.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsTranscription start siteInteractive web serverHigh-throughput genomic experimentsModENCODE projectGenome tracksSingle nucleotide polymorphismsRNA-seqStart siteGenomics experimentsGenome ProjectNucleotide polymorphismsEncodesWeb serverAggregationSitesExample datasetPolymorphismACT sitesAggregate profilesToolboxAlleleSeq: analysis of allele‐specific expression and binding in a network framework
Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M. AlleleSeq: analysis of allele‐specific expression and binding in a network framework. Molecular Systems Biology 2011, 7: msb201154. PMID: 21811232, PMCID: PMC3208341, DOI: 10.1038/msb.2011.54.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAllelesCell LineChromosome MappingChromosomes, Human, XChromosomes, Human, YDatabases, GeneticDNA-Binding ProteinsGene Expression RegulationGene Regulatory NetworksGenome, HumanHumansMolecular Sequence AnnotationOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideSequence Analysis, RNATranscription FactorsConceptsAllele-specific expressionGenome sequenceFunctional genomics data setsAllele-specific behaviorAllele-specific eventsDiploid genome sequenceChIP-seq data setsGenomic data setsGenomic sequence variantsPersonal genome sequencesAlignment of readsRNA-seqGenome ProjectPaternal alleleComputational pipelineReads mappingSequence variantsNetwork motifsVariation dataReference alleleAllelesReadsSequenceExpressionMaternallyLinda
Bjornson R. Linda. 2011, 1014-1021. DOI: 10.1007/978-0-387-09766-4_233.Peer-Reviewed Original Research
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