2021
Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track
Kuo T, Bath T, Ma S, Pattengale N, Yang M, Cao Y, Hudson C, Kim J, Post K, Xiong L, Ohno-Machado L. Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track. International Journal Of Medical Informatics 2021, 154: 104559. PMID: 34474309, PMCID: PMC9933142, DOI: 10.1016/j.ijmedinf.2021.104559.Peer-Reviewed Original ResearchConceptsSecure genome analysis competitionData retrieval queriesBlockchain-based methodData sharing methodReal-world problemsQuery indexType of sharingRetrieval queriesBlockchain technologyLedger technologySource codeBlockchain utilizationBlockchain strategyHealthcare applicationsInteraction recordsSharing methodTest datasetSharing recordsBiomedical research applicationsConsortium resourcesNew platformPatient recordsBlockchainQueriesTechnology
2017
PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens
Spahn P, Bath T, Weiss R, Kim J, Esko J, Lewis N, Harismendy O. PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens. Scientific Reports 2017, 7: 15854. PMID: 29158538, PMCID: PMC5696473, DOI: 10.1038/s41598-017-16193-9.Peer-Reviewed Original ResearchConceptsCRISPR/Large-scale genetic screensCRISPR/Cas9Publication-ready plotsSequence quality controlPlatform-independent analysisUser-friendly implementationLarge sequencing datasetsGenetic screenAnalysis optionsFunctional genomicsPooled screensSgRNA libraryBioinformatics toolsSequencing datasetsComprehensive functionalityExperimental biologistsBioinformatics expertiseArt toolsCustom libraryLimited functionalityTest datasetSequence extractionGene rankingIncreased popularity
2014
GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda
Wang S, Kim J, Jiang X, Brunner S, Ohno-Machado L. GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda. BMC Medical Genomics 2014, 7: s9. PMID: 25077821, PMCID: PMC4101446, DOI: 10.1186/1755-8794-7-s1-s9.Peer-Reviewed Original ResearchConceptsCompute Unified Device ArchitectureGraphics processing unitsHigh performance computeParallel computingNVIDIA Compute Unified Device ArchitectureUnified Device ArchitectureMultiple test datasetsGiga cell updatesTimes performance gainsSmith-Waterman algorithmGPU developersSW implementationSource codeExecution timeGHz CPUIntel XeonLong reference sequencesProcessing unitTarget identification algorithmCell updatesTest datasetProjects/Such large scalePerformance gainsBiomedical research community