2014
Computational Analysis in Cancer Exome Sequencing
Evans P, Kong Y, Krauthammer M. Computational Analysis in Cancer Exome Sequencing. Methods In Molecular Biology 2014, 1176: 219-227. PMID: 25030931, DOI: 10.1007/978-1-4939-0992-6_18.Peer-Reviewed Original ResearchConceptsSomatic single nucleotide variantsMutational eventsSingle nucleotide variantsHuman genesSequencing readsShort insertionsDriver genesNucleotide variantsNumber alterationsExome sequencingGenesCancer samplesComputational analysisMore mutational eventsPowerful toolComputational methodsExomeSequencingDeletionReads
2013
Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics
Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, Beal K, Chakravarty D, Challis D, Chen Y, Clarke D, Clarke L, Cunningham F, Evani US, Flicek P, Fragoza R, Garrison E, Gibbs R, Gümüş ZH, Herrero J, Kitabayashi N, Kong Y, Lage K, Liluashvili V, Lipkin SM, MacArthur DG, Marth G, Muzny D, Pers TH, Ritchie GRS, Rosenfeld JA, Sisu C, Wei X, Wilson M, Xue Y, Yu F, Consortium 1, Dermitzakis ET, Yu H, Rubin MA, Tyler-Smith C, Gerstein M. Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics. Science 2013, 342: 1235587. PMID: 24092746, PMCID: PMC3947637, DOI: 10.1126/science.1235587.Peer-Reviewed Original ResearchNegligible impact of rare autoimmune-locus coding-region variants on missing heritability
Hunt KA, Mistry V, Bockett NA, Ahmad T, Ban M, Barker JN, Barrett JC, Blackburn H, Brand O, Burren O, Capon F, Compston A, Gough SC, Jostins L, Kong Y, Lee JC, Lek M, MacArthur DG, Mansfield JC, Mathew CG, Mein CA, Mirza M, Nutland S, Onengut-Gumuscu S, Papouli E, Parkes M, Rich SS, Sawcer S, Satsangi J, Simmonds MJ, Trembath RC, Walker NM, Wozniak E, Todd JA, Simpson MA, Plagnol V, van Heel DA. Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 2013, 498: 232-235. PMID: 23698362, PMCID: PMC3736321, DOI: 10.1038/nature12170.Peer-Reviewed Original ResearchAdjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes
Evans P, Avey S, Kong Y, Krauthammer M. Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes. IEEE Transactions On NanoBioscience 2013, 12: 150-157. PMID: 23694700, PMCID: PMC3989533, DOI: 10.1109/tnb.2013.2263391.Peer-Reviewed Original ResearchConceptsMore non-synonymous mutationsMutation frequencyTumor sequencing projectsGene-specific mannerCancer driver genesNon-synonymous mutationsSynonymous mutation ratioMutation biasSequencing projectsBackground mutation frequencyGene expressionDriver genesGenesTumor developmentMutation burdenMutation ratioHigher non-synonymous mutation burdenMutationsMutation countsExpressionBackground frequencyFrequency biasesIdentification
2012
Exon capture and bulk segregant analysis: rapid discovery of causative mutations using high-throughput sequencing
del Viso F, Bhattacharya D, Kong Y, Gilchrist MJ, Khokha MK. Exon capture and bulk segregant analysis: rapid discovery of causative mutations using high-throughput sequencing. BMC Genomics 2012, 13: 649. PMID: 23171430, PMCID: PMC3526394, DOI: 10.1186/1471-2164-13-649.Peer-Reviewed Original ResearchConceptsBulk segregant analysisForward genetic screenSegregant analysisGenetic screenGenome assemblyExon captureCausative mutationsVertebrate model systemHigh-throughput sequencingHuman genetic analysisThousands of SNPsAssembly of scaffoldsModel systemGenomic resourcesVertebrate modelXenopus tropicalisFine mappingGenetic analysisCapture sequencingSequence variantsSequencingMutationsRapid discoveryMutantsExome sequencingExome sequencing identifies recurrent somatic RAC1 mutations in melanoma
Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker J, Cheng E, Davis MJ, Goh G, Choi M, Ariyan S, Narayan D, Dutton-Regester K, Capatana A, Holman EC, Bosenberg M, Sznol M, Kluger HM, Brash DE, Stern DF, Materin MA, Lo RS, Mane S, Ma S, Kidd KK, Hayward NK, Lifton RP, Schlessinger J, Boggon TJ, Halaban R. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nature Genetics 2012, 44: 1006-1014. PMID: 22842228, PMCID: PMC3432702, DOI: 10.1038/ng.2359.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCase-Control StudiesDNA Mutational AnalysisExomeFemaleGene FrequencyGenetic Predisposition to DiseaseHumansMaleMelanomaMiddle AgedModels, MolecularMutationProto-Oncogene Proteins B-rafProto-Oncogene Proteins p21(ras)Rac1 GTP-Binding ProteinSequence Analysis, DNASkin NeoplasmsUveal NeoplasmsConceptsSun-exposed melanomasForward genetics uncovers Transmembrane protein 107 as a novel factor required for ciliogenesis and Sonic hedgehog signaling
Christopher KJ, Wang B, Kong Y, Weatherbee SD. Forward genetics uncovers Transmembrane protein 107 as a novel factor required for ciliogenesis and Sonic hedgehog signaling. Developmental Biology 2012, 368: 382-392. PMID: 22698544, PMCID: PMC3402655, DOI: 10.1016/j.ydbio.2012.06.008.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsBody PatterningCells, CulturedCiliaEmbryo, MammalianExtremitiesFemaleGene Expression Regulation, DevelopmentalHedgehog ProteinsIn Situ HybridizationKruppel-Like Transcription FactorsMaleMembrane ProteinsMiceMice, Inbred C3HMice, Inbred C57BLMice, Mutant StrainsMicroscopy, Electron, ScanningMolecular Sequence DataMutationNerve Tissue ProteinsNeural TubeSequence Homology, Amino AcidSignal TransductionZinc Finger Protein Gli2Zinc Finger Protein Gli3ConceptsCilia formationDistinct tissuesLeft-right patterning defectsForward genetic approachDevelopmental patterning eventsLeft-right specificationShh target genesNovel mutant allelesSonic hedgehog (Shh) signalingNeuronal cell typesEmbryonic patterningMutant phenotypeCilia mutantsDynamic organellesGli activatorPatterning defectsPatterning eventsRepressor formCilia biogenesisUnknown regulatorCilia formGenetic approachesSkeletal formationTarget genesTmem107
2010
MU2A—reconciling the genome and transcriptome to determine the effects of base substitutions
Garla V, Kong Y, Szpakowski S, Krauthammer M. MU2A—reconciling the genome and transcriptome to determine the effects of base substitutions. Bioinformatics 2010, 27: 416-418. PMID: 21149339, PMCID: PMC3031033, DOI: 10.1093/bioinformatics/btq658.Peer-Reviewed Original ResearchConceptsProtein productsSequence variantsNext-generation sequencing technologiesGenomic sequence variantsTranscript librariesGenomic coordinatesGenomic positionsReference genomeSequencing technologiesSequence variationGenomeGenomic variantsBase substitutionsVariant annotationTranscriptomeTranscriptsVariantsAnnotationMappingConstruction and maintenance of randomized retroviral expression libraries for transmembrane protein engineering
Marlatt SA, Kong Y, Cammett TJ, Korbel G, Noonan JP, DiMaio D. Construction and maintenance of randomized retroviral expression libraries for transmembrane protein engineering. Protein Engineering Design And Selection 2010, 24: 311-320. PMID: 21149273, PMCID: PMC3038463, DOI: 10.1093/protein/gzq112.Peer-Reviewed Original ResearchConceptsSignificant genetic bottleneckRetroviral expression libraryDeep DNA sequencingAmino acid segmentNovel biological activitiesGenetic bottleneckTransmembrane domainMammalian cellsLibrary sequencesRandom mutagenesisProtein engineeringExpression libraryDeep sequencingDifferent proteinsSmall proteinsGenetic selectionAcid segmentRetroviral libraryDNA sequencingSequencing resultsRandomized libraryProteinPowerful approachSequencingDiversity