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 ResearchConceptsEfficient 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 ResearchIdentification 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 ResearchMeSH 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 ResearchMeSH 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
2011
Power 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 ResearchConceptsMonte 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 studyAlleleSeq: 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 ResearchMeSH 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 alleleAllelesReadsSequenceExpressionMaternally
2010
Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families
Patel PV, Gianoulis TA, Bjornson RD, Yip KY, Engelman DM, Gerstein MB. Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families. Genome Research 2010, 20: 960-971. PMID: 20430783, PMCID: PMC2892097, DOI: 10.1101/gr.102814.109.Peer-Reviewed Original ResearchConceptsProtein familyMembrane proteinsMembrane protein familyRecent metagenomic studiesMembrane protein contentAdditional environmental featuresProtein diversityDisparate habitatsGenomic diversityPhosphate transporterProtein variationMetagenomic sequencesMetagenomic studiesEnvironmental featuresIron transporterHuman impactProteinOceanographic variablesMarine sitesEcological analysisTransportersDiversityProtein contentFamilyClimate change
2009
RigidFinder: A fast and sensitive method to detect rigid blocks in large macromolecular complexes
Abyzov A, Bjornson R, Felipe M, Gerstein M. RigidFinder: A fast and sensitive method to detect rigid blocks in large macromolecular complexes. Proteins Structure Function And Bioinformatics 2009, 78: 309-324. PMID: 19705487, DOI: 10.1002/prot.22544.Peer-Reviewed Original ResearchConceptsLarge macromolecular complexesMacromolecular complexesLarge-scale conformational changesRNA polymerase IIT7 RNA polymeraseMultiple polypeptide chainsPolymerase IIRNA polymeraseDistance conservationPhosphate dikinaseDifferent conformationsInter-residue distancesLarge complexesConformational changesPolypeptide chainDomain motionPartial refoldingFurther distinguishing featureConformationStructure determinationComplexesDikinaseSensitive identificationGroELIdentificationPeakSeq enables systematic scoring of ChIP-seq experiments relative to controls
Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R, Carriero N, Snyder M, Gerstein MB. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nature Biotechnology 2009, 27: 66-75. PMID: 19122651, PMCID: PMC2924752, DOI: 10.1038/nbt.1518.Peer-Reviewed Original Research