2024
A genotyping array for the globally invasive vector mosquito, Aedes albopictus
Cosme L, Corley M, Johnson T, Severson D, Yan G, Wang X, Beebe N, Maynard A, Bonizzoni M, Khorramnejad A, Martins A, Lima J, Munstermann L, Surendran S, Chen C, Maringer K, Wahid I, Mukherjee S, Xu J, Fontaine M, Estallo E, Stein M, Livdahl T, Scaraffia P, Carter B, Mogi M, Tuno N, Mains J, Medley K, Bowles D, Gill R, Eritja R, González-Obando R, Trang H, Boyer S, Abunyewa A, Hackett K, Wu T, Nguyễn J, Shen J, Zhao H, Crawford J, Armbruster P, Caccone A. A genotyping array for the globally invasive vector mosquito, Aedes albopictus. Parasites & Vectors 2024, 17: 106. PMID: 38439081, PMCID: PMC10910840, DOI: 10.1186/s13071-024-06158-z.Peer-Reviewed Original ResearchConceptsWhole-genome sequencingLow-coverage whole-genome sequencingSNP chipRepetitive elementsGenomic analysisNative rangePatterns of genomic variationWhole-genome sequencing dataSNP chip genotypesPopulation genomic analysesProtein-coding genesLevels of admixtureOrigin of invasionNon-coding regionsPercentage of repetitive elementsGenotyping of samplesChip genotypesGenetic clustersAncestry analysisGenomic variationGenotyping arraysGenotyping platformsMendelian genesGenetic variationGenotyping methods
2021
Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies
Li D, McIntosh C, Mastaglia F, Wilton S, Aung-Htut M. Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies. Translational Neurodegeneration 2021, 10: 16. PMID: 34016162, PMCID: PMC8136212, DOI: 10.1186/s40035-021-00240-7.Peer-Reviewed Original ResearchConceptsAlternative splicingSplicing patternsSplicing defectsNeurodegenerative diseasesTrans-splicing factorsEukaryotic gene expressionDiversity of proteomesNon-coding regionsPrecursor messenger RNAAntisense oligonucleotide therapeuticsSplice-switching antisense oligonucleotidesRNA splicingMature mRNAGenetic plasticityCellular homeostasisSplicingGene expressionNeuronal differentiationNeuronal migrationNeuronal cellsNeurodegenerative disordersAlzheimer's diseaseSynaptic functionMessenger RNAAntisense oligonucleotidesEnvironmental and sex-specific molecular signatures of glioma causation
Claus EB, Cannataro VL, Gaffney SG, Townsend JP. Environmental and sex-specific molecular signatures of glioma causation. Neuro-Oncology 2021, 24: 29-36. PMID: 33942853, PMCID: PMC8730771, DOI: 10.1093/neuonc/noab103.Peer-Reviewed Original ResearchConceptsIDH wild-type tumorsWild-type tumorsEnvironmental risk factorsIDH-mutant tumorsRisk factorsCases of gliomaMolecular signaturesPIK3CA mutationsPossible risk exposuresMutation subtypesCancer effectsExogenous exposureAdult gliomasTumorsWhole-exome sequencing dataGliomasKinase domainMutational signaturesCancer-causing mutationsMalesFemalesNon-coding regionsPIK3R1SexCancer mutational signaturesWhole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers
Li X, Kumar S, Harmanci A, Li S, Kitchen RR, Zhang Y, Wali VB, Reddy SM, Woodward WA, Reuben JM, Rozowsky J, Hatzis C, Ueno NT, Krishnamurthy S, Pusztai L, Gerstein M. Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers. Genome Medicine 2021, 13: 70. PMID: 33902690, PMCID: PMC8077918, DOI: 10.1186/s13073-021-00879-x.Peer-Reviewed Original ResearchConceptsSingle nucleotide variantsWhole-genome sequencingGermline single nucleotide variantsInternational Cancer Genome ConsortiumGenomic featuresGenomic alterationsGenome ConsortiumClonal architectureWhole Genomes (PCAWG) ConsortiumNon-coding regionsCancer-related pathwaysNon-IBC samplesCancer Genome Atlas ProgramMAST2 geneCopy number profilesPan-cancer analysisTGF-β pathwayGenomic architectureGenomic regionsSimilar genomic alterationsSimilar genomic characteristicsComplex SVsIBC samplesGenomic differencesOverall mutational load
2020
Massively parallel techniques for cataloguing the regulome of the human brain
Townsley KG, Brennand KJ, Huckins LM. Massively parallel techniques for cataloguing the regulome of the human brain. Nature Neuroscience 2020, 23: 1509-1521. PMID: 33199899, PMCID: PMC8018778, DOI: 10.1038/s41593-020-00740-1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsRegulatory elementsTarget genesParallel reporter assaysPutative regulatory elementsNon-coding regionsDisease-associated lociSpecific expression patternsCandidate risk lociPluripotent stem cellsHigh-throughput assaysRelevant molecular pathwaysTranscriptional responseRegulatory architectureRisk lociExpression patternsReporter assaysComplex brain disordersMolecular pathwaysRegulomeStem cellsRisk architectureGenetic riskGenesLociGenetic diagnosisNIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis
Zhang J, Liu J, McGillivray P, Yi C, Lochovsky L, Lee D, Gerstein M. NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis. BMC Bioinformatics 2020, 21: 474. PMID: 33092526, PMCID: PMC7580035, DOI: 10.1186/s12859-020-03758-1.Peer-Reviewed Original ResearchConceptsDNase I hypersensitive sitesMutation rate heterogeneityDNA elementsCancer whole genome sequencesMutational hotspotsMutation burden analysisFunctional genomics dataNon-coding regionsGene regulatory networksWhole Genomes (PCAWG) projectWhole genome sequencesBackground mutation rateBurden analysisChromatin organizationReplication timingGenome sequenceRegulatory networksTranscription factorsHypersensitive sitesGenomic featuresRate heterogeneityGenome ProjectGenomic dataIntegrative methodGamma-Poisson mixture modelAnalyses of non-coding somatic drivers in 2,658 cancer whole genomes
Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshøj H, Hess JM, Juul RI, Lin Z, Feuerbach L, Sabarinathan R, Madsen T, Kim J, Mularoni L, Shuai S, Lanzós A, Herrmann C, Maruvka YE, Shen C, Amin SB, Bandopadhayay P, Bertl J, Boroevich KA, Busanovich J, Carlevaro-Fita J, Chakravarty D, Chan CWY, Craft D, Dhingra P, Diamanti K, Fonseca NA, Gonzalez-Perez A, Guo Q, Hamilton MP, Haradhvala NJ, Hong C, Isaev K, Johnson TA, Juul M, Kahles A, Kahraman A, Kim Y, Komorowski J, Kumar K, Kumar S, Lee D, Lehmann KV, Li Y, Liu EM, Lochovsky L, Park K, Pich O, Roberts ND, Saksena G, Schumacher SE, Sidiropoulos N, Sieverling L, Sinnott-Armstrong N, Stewart C, Tamborero D, Tubio JMC, Umer HM, Uusküla-Reimand L, Wadelius C, Wadi L, Yao X, Zhang CZ, Zhang J, Haber JE, Hobolth A, Imielinski M, Kellis M, Lawrence MS, von Mering C, Nakagawa H, Raphael BJ, Rubin MA, Sander C, Stein LD, Stuart JM, Tsunoda T, Wheeler DA, Johnson R, Reimand J, Gerstein M, Khurana E, Campbell PJ, López-Bigas N, Weischenfeldt J, Beroukhim R, Martincorena I, Pedersen J, Getz G. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 2020, 578: 102-111. PMID: 32025015, PMCID: PMC7054214, DOI: 10.1038/s41586-020-1965-x.Peer-Reviewed Original ResearchConceptsInternational Cancer Genome ConsortiumStructural variantsPoint mutationsDriver discoveryProtein-coding genesNon-coding genesNon-coding regionsPan-cancer analysisDriver point mutationsSomatic driversCancer Genome AtlasRegulatory sequencesCancer genomesUntranslated regionGenome ConsortiumFocal deletionsGenesGenome AtlasGenomeNovel candidatesMutationsRecurrent breakpointsRegion of TP53DiscoveryVariants
2017
Chromatin accessibility prediction via a hybrid deep convolutional neural network
Liu Q, Xia F, Yin Q, Jiang R. Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics 2017, 34: 732-738. PMID: 29069282, PMCID: PMC6192215, DOI: 10.1093/bioinformatics/btx679.Peer-Reviewed Original ResearchConceptsSequence signaturesHuman genomeIdentification of causative genetic variantsIdentification of non-coding variantsMatch of identified sequence signaturesLarge-scale sequencing dataAnnotate regulatory elementsDNA sequence signaturesHigh-throughput biological experimentsNon-coding variantsChromatin accessibility dataNon-coding regionsHuman inherited diseasesCausative genetic variantsDNA sequence codeDeep convolutional neural networkAssociated with diseaseConvolutional neural networkNoncoding variantsSequence dataChromatin accessibilitySupplementary dataGenomic levelRegulatory elementsDNA sequencesMapping the chromatin and RNA expression landscapes of human macrophages to interrogate the function of disease-associated genetic polymorphisms
Martins A, Narayanan M, Fixsen B, Tsang J. Mapping the chromatin and RNA expression landscapes of human macrophages to interrogate the function of disease-associated genetic polymorphisms. The Journal Of Immunology 2017, 198: 59.15-59.15. DOI: 10.4049/jimmunol.198.supp.59.15.Peer-Reviewed Original ResearchSingle-nucleotide polymorphismsRegulatory elementsDisease-associated genetic polymorphismsGenome-wide association studiesGene expressionRegulatory element activityIdentified many single-nucleotide polymorphismsNon-coding regionsCondition-dependent activityHistone 3 lysineModulate gene expressionResponse to diverse stimuliMeasure gene expressionChromatin stateAssociation studiesTranscriptional networksGene modulesExpression landscapeFunctional enrichmentElement activityDiverse stimuliPromoter/enhancer activityGenetic polymorphismsDevelopment of diseaseChromatin
2016
Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies
Lu Q, Powles RL, Wang Q, He BJ, Zhao H. Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies. PLOS Genetics 2016, 12: e1005947. PMID: 27058395, PMCID: PMC4825932, DOI: 10.1371/journal.pgen.1005947.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGWAS signalsComplex traitsFunctional annotationAssociation studiesHuman complex traitsFunctional regionsNon-coding regionsGWAS p-valuesWide association studyNovel biological insightsRelevant tissue typesEpigenetic annotationsGenomic functionsRegulatory machineryTransposable elementsHuman genomeGenoSkylineRisk lociBiological insightsIntegrative analysisGenetic studiesRegulatory miRNAPrioritization performanceSpecific annotations
2015
Widespread Inducible Transcription Downstream of Human Genes
Vilborg A, Passarelli MC, Yario TA, Tycowski KT, Steitz JA. Widespread Inducible Transcription Downstream of Human Genes. Molecular Cell 2015, 59: 449-461. PMID: 26190259, PMCID: PMC4530028, DOI: 10.1016/j.molcel.2015.06.016.Peer-Reviewed Original ResearchConceptsOsmotic stressLong non-coding regionsDownstream of genesProtein-coding genesNon-coding regionsPervasive transcriptionHuman cell linesTranscription downstreamHuman genomeHuman genesTranscript inductionRNA-seqPolyA signalUpstream transcriptsUndescribed mechanismGenesCell linesTranscriptionTranscript typeActive regulationTranscriptsDetailed mechanistic studiesRNADownstreamMechanistic studiesA Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data. Scientific Reports 2015, 5: 10576. PMID: 26015273, PMCID: PMC4444969, DOI: 10.1038/srep10576.Peer-Reviewed Original ResearchConceptsHuman genomeFunctional regionsStatistical frameworkAnnotation dataFunctional annotation dataWhole-genome annotationNon-coding regionsGenomic conservationHigh-throughput experimentsENCODE projectExperimental annotationsGenomeUnsupervised statistical learningFunctional potentialHuman geneticsStatistical learningComputational predictionsIntegrated analysisAnnotationAnnotation methodDiverse typesPowerful toolGeneticsMajor goalWeb server
2014
Most recent common ancestor of TTR Val30Met mutation in Italian population and its potential role in genotype-phenotype correlation
Iorio A, De Angelis F, Di Girolamo M, Luigetti M, Pradotto L, Mauro A, Manfellotto D, Fuciarelli M, Polimanti R. Most recent common ancestor of TTR Val30Met mutation in Italian population and its potential role in genotype-phenotype correlation. Amyloid 2014, 22: 73-78. PMID: 25510352, DOI: 10.3109/13506129.2014.994597.Peer-Reviewed Original ResearchConceptsMost recent common ancestorNon-coding regionsRecent common ancestorAge of originPhenotypic heterogeneityGenetic diversityCommon ancestorPhenotypic variationIndependent originsGenetic evidenceMultiple founder mutationsMicrosatellite markersGenetic analysisGenetic relationshipsGenotype-phenotype correlationAmyloidogenic TTR mutationsPhenotypic variabilityMutationsHuman populationWorldwide distributionPotential roleTTR geneAutosomal transmissionFounder mutationDifferent originsSP0104 The Molecular Basis of Autoimmune Disease
Hafler D. SP0104 The Molecular Basis of Autoimmune Disease. Annals Of The Rheumatic Diseases 2014, 73: 27-28. DOI: 10.1136/annrheumdis-2014-eular.6254.Peer-Reviewed Original ResearchGenome-wide association studiesNon-coding regionsConsensus transcription factorNumerous genetic associationsDistinct cell typesDifferent autoimmune diseasesAutoimmune diseasesChromatin mapsTh17 cellsGWAS hitsHigh NaCl levelsTranscription factorsDNA sequencesMolecular basisGenetic dataCausal mutationsDisease riskAssociation studiesMechanistic basisCommon SNPsNucleotide variantsAP-1Risk SNPsCell typesSpecific disruption
2013
Intronic rs2147363 Variant in ATP7B Transcription Factor-Binding Site Associated with Alzheimer's Disease
Bucossi S, Polimanti R, Ventriglia M, Mariani S, Siotto M, Ursini F, Trotta L, Scrascia F, Callea A, Vernieri F, Squitti R. Intronic rs2147363 Variant in ATP7B Transcription Factor-Binding Site Associated with Alzheimer's Disease. Journal Of Alzheimer’s Disease 2013, 37: 453-459. PMID: 23948886, DOI: 10.3233/jad-130431.Peer-Reviewed Original ResearchConceptsLinkage disequilibriumDisease-causing variantsCis-regulatory elementsNon-coding regionsObserved genetic associationIntronic single nucleotide polymorphismSingle nucleotide polymorphismsTranscription factorsGenetic variationATP7B variantsSilico analysisRegulatory functionsLD analysisNucleotide polymorphismsGenetic associationSites AssociatedAlzheimer's diseaseAD riskKey roleVariantsATP7B geneDifferential Expression of HPV16 L2 Gene in Cervical Cancers Harboring Episomal HPV16 Genomes: Influence of Synonymous and Non-Coding Region Variations
Mandal P, Bhattacharjee B, Ghosh D, Mondal N, Chowdhury R, Roy S, Sengupta S. Differential Expression of HPV16 L2 Gene in Cervical Cancers Harboring Episomal HPV16 Genomes: Influence of Synonymous and Non-Coding Region Variations. PLOS ONE 2013, 8: e65647. PMID: 23762404, PMCID: PMC3675152, DOI: 10.1371/journal.pone.0065647.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedCapsid ProteinsCarcinoma, Squamous CellFemaleGene DosageGene Expression Regulation, ViralGenome, ViralHost-Pathogen InteractionsHuman papillomavirus 16HumansMicroRNAsMiddle AgedOncogene Proteins, ViralOpen Reading FramesPapillomavirus InfectionsPlasmidsPolymorphism, GeneticPromoter Regions, GeneticRNA, MessengerSequence Analysis, DNAUterine Cervical NeoplasmsConceptsNon-coding regionsHPV16 genomeSynonymous variationNon-malignant samplesHuman codonsL2 genesWhole-genome sequence analysisShort non-coding regionHPV16 isolatesGenome sequence analysisMiRNA binding sitesWeak promoter activityHsa-mir-548Gene copy numberCaCx casesEpisomal HPV16 genomesSequence variationCoding regionSequence analysisGenomeNon-codingViral genomeEpisomal viral genomesCopy numberL2 ORFModels of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data
Yaari G, Vander Heiden J, Uduman M, Gadala-Maria D, Gupta N, Stern JN, O’Connor K, Hafler DA, Laserson U, Vigneault F, Kleinstein SH. Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data. Frontiers In Immunology 2013, 4: 358. PMID: 24298272, PMCID: PMC3828525, DOI: 10.3389/fimmu.2013.00358.Peer-Reviewed Original ResearchAccurate background modelSynonymous mutationsNon-coding regionsParticular codon usageNon-functional sequencesComputational analysis methodsObserved mutation patternExisting modelsBackground modelInfluence of selectionCodon usageSHM targetingBase compositionImproved modelSequencing dataNucleotide substitutionsAnalysis methodStatistical analysisFunctional sequencesMutation targetingB-cell cancersModelSomatic hypermutation patternsMutationsHypermutation patterns
2012
Deep sequencing reveals unique small RNA repertoire that is regulated during head regeneration in Hydra magnipapillata
Krishna S, Nair A, Cheedipudi S, Poduval D, Dhawan J, Palakodeti D, Ghanekar Y. Deep sequencing reveals unique small RNA repertoire that is regulated during head regeneration in Hydra magnipapillata. Nucleic Acids Research 2012, 41: 599-616. PMID: 23166307, PMCID: PMC3592418, DOI: 10.1093/nar/gks1020.Peer-Reviewed Original ResearchConceptsSmall RNAsAnnotated transcriptomeTransposable elementsNon-coding RNAsDeep sequencingRepertoire of small RNAsClasses of small RNAsFine-tunes gene expressionHydra magnipapillataGene expressionHead regenerationSmall RNA repertoireCnidarian model systemsNon-coding regionsAbundant small RNAsStem-loop structureSmall non-coding RNAsModulating important processesPost-transcriptional regulationHydra head regenerationRNA expression patternsBilaterian miRNAsEndo-siRNAsInverted repeatsMiRNA loci
2010
Annotating non-coding regions of the genome
Alexander RP, Fang G, Rozowsky J, Snyder M, Gerstein MB. Annotating non-coding regions of the genome. Nature Reviews Genetics 2010, 11: 559-571. PMID: 20628352, DOI: 10.1038/nrg2814.Peer-Reviewed Original ResearchConceptsFunctional genomics experimentsGenomics experimentsBiological knowledgeChromosome conformation captureNon-coding genomeDNA sequence dataFunctional genomics dataHigh sequence similarityMore biological knowledgeNon-coding regionsPaired-end sequencingLow-throughput methodsConformation captureSequence similarityHuman genomeHuman haplotypesSequence comparisonSequence dataDNA sequencesGenomic dataGenomeFunctional analysisSequence analysisSequence familiesCell types
2009
Exonic remnants of whole-genome duplication reveal cis-regulatory function of coding exons
Dong X, Navratilova P, Fredman D, Drivenes Ø, Becker T, Lenhard B. Exonic remnants of whole-genome duplication reveal cis-regulatory function of coding exons. Nucleic Acids Research 2009, 38: 1071-1085. PMID: 19969543, PMCID: PMC2831330, DOI: 10.1093/nar/gkp1124.Peer-Reviewed Original ResearchConceptsWhole-genome duplicationGenomic regulatory blocksNon-coding regionsCoding exonsHost genesCis-regulatory functionNon-coding elementsExonic regulatory elementsCis-regulatory inputsProtein coding exonsProtein-codingMammalian exonsEvolutionary separationGenomic approachesRegulatory blocksRegulatory elementsCoding sequenceDevelopmental genesSelection pressureRegulatory informationTarget genesExonGenesSequence spaceTeleost
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