2025
Viral genomic features predict Orthopoxvirus reservoir hosts
Tseng K, Koehler H, Becker D, Gibb R, Carlson C, Pilar Fernandez M, Seifert S. Viral genomic features predict Orthopoxvirus reservoir hosts. Communications Biology 2025, 8: 309. PMID: 40000824, PMCID: PMC11862092, DOI: 10.1038/s42003-025-07746-0.Peer-Reviewed Original ResearchConceptsHost speciesViral genomic featuresHost ecological traitsPotential host speciesGenomic featuresHistorical rangeEcological traitsParts of Southeast AsiaWildlife surveillanceHuman populationCausative agent of smallpoxHost predictionAgent of smallpoxSpeciesSoutheast AsiaCausative agentGeographic regionsOrthopoxviruses
2024
Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features
Lanino L, D'Amico S, Maggioni G, Al Ali N, Wang Y, Gurnari C, Gagelmann N, Bewersdorf J, Ball S, Guglielmelli P, Meggendorfer M, Hunter A, Kubasch A, Travaglino E, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Buizza A, Sauta E, Zampini M, Riva E, Asti G, Delleani M, Ficara F, Santoro A, Sala C, Dall'Olio D, Dall'Olio L, Kewan T, Casetti I, Awada H, Xicoy B, Vucinic V, Hou H, Chou W, Yao C, Lin C, Tien H, Consagra A, Sallman D, Kern W, Bernardi M, Chiusolo P, Borin L, Voso M, Pleyer L, Palomo L, Quintela D, Jerez A, Cornejo E, Martin P, Díaz-Beyá M, Pita A, Roldan V, Suarez D, Velasco E, Calabuig M, Garcia-Manero G, Loghavi S, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Kröger N, Fenaux P, Fontenay M, Santini V, Haferlach T, Germing U, Padron E, Robin M, Passamonti F, Solary E, Vannucchi A, Castellani G, Zeidan A, Komrokji R, Della Porta M. Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features. Blood 2024, 144: 1005. DOI: 10.1182/blood-2024-204826.Peer-Reviewed Original ResearchGenomic featuresSplicing mutationBiallelic inactivationAnalysis of genomic profilesBiallelic inactivation of TP53Clinical phenotypeGene expression profilesCNV analysisMorphological featuresInactivation of TP53Myeloid neoplasmsGenomic characterizationRNAseq dataMorphological dataMutation screeningExpression profilesMutationsJAK/STATGenomic profilingGenomeHierarchical importanceHeterogeneous phenotypesIntegrated analysisPhenotypeHematological phenotypeA Molecular-Based Ecosystem to Improve Personalized Medicine in Patients with Chronic Myelomonocytic Leukemia (CMML)
Lanino L, Hunter A, Gagelmann N, Robin M, Sala C, Dall'Olio D, Gurnari C, Dall'Olio L, Wang Y, Pleyer L, Xicoy B, Montalban-Bravo G, Shih L, Haque T, Abdel-Wahab O, Geissler K, Bataller A, Bazinet A, Meggendorfer M, Casetti I, Sauta E, Travaglino E, Palomo L, Zamora L, Quintela D, Jerez A, Cornejo E, Garcia Martin P, Díaz-Beyá M, Avendaño Pita A, Roldan V, Fiallo Suarez D, Cerezo Velasco E, Calabuig M, Such E, Sanz G, Kubasch A, Castilla-Llorente C, Bulabois C, Souchet L, Awada H, Bernardi M, Chiusolo P, Curti A, Giaccone L, Onida F, Borin L, Passamonti F, Diral E, Vucinic V, Bergonzi G, Voso M, Hou H, Chou W, Yao C, Lin C, Tien H, Campagna A, Ubezio M, Russo A, Todisco G, Maggioni G, Tentori C, Buizza A, Asti G, Zampini M, Riva E, Delleani M, Consagra A, Ficara F, Santoro A, Carota L, Sanavia T, Rollo C, Kiwan A, VanOudenhove J, Fariselli P, Al Ali N, Sallman D, Kern W, Garcia-Manero G, Thota S, Griffiths E, Follo M, Finelli C, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Bejar R, Thol F, Kröger N, Fenaux P, Itzykson R, Graubert T, Fontenay M, Zeidan A, Komrokji R, Santini V, Haferlach T, Germing U, D'Amico S, Castellani G, Patnaik M, Solary E, Padron E, Della Porta M. A Molecular-Based Ecosystem to Improve Personalized Medicine in Patients with Chronic Myelomonocytic Leukemia (CMML). Blood 2024, 144: 1003-1003. DOI: 10.1182/blood-2024-200104.Peer-Reviewed Original ResearchChronic myelomonocytic leukemiaLeukemia-free survivalMyeloid neoplasmsProportion of patientsOverall survivalMolecular-based toolsMolecular informationEvaluation of mutation statusInfluence disease phenotypeGenomic overlapScoring systemGenomic associationsGenomic featuresSplicing machineryConcordance indexGenomic characterizationChronic myelomonocytic leukemia patientsMedian leukemia-free survivalProbability of disease relapseAllogeneic stem cell transplantationSignal transductionGenomic heterogeneityRisk of disease progressionMulti-color flow cytometryMutation screeningEnhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA)
Sauta E, Sartori F, Lanino L, Asti G, D'Amico S, Delleani M, Riva E, Zampini M, Zazzetti E, Bicchieri M, Maggioni G, Campagna A, Todisco G, Tentori C, Ubezio M, Russo A, Buizza A, Ficara F, Crisafulli L, Brindisi M, Ventura D, Pinocchio N, Rahal D, Lancellotti C, Bonometti A, Di Tommaso L, Savevski V, Santoro A, Derus N, Dall'Olio D, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Garcia-Manero G, Haferlach T, Kordasti S, Zeidan A, Castellani G, Sanavia T, Fariselli P, Della Porta M. Enhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA). Blood 2024, 144: 105-105. DOI: 10.1182/blood-2024-205413.Peer-Reviewed Original ResearchPersonalized medicine programsMyelodysplastic syndrome patientsMyelodysplastic syndromeOverall survivalConcordance indexClinical outcomesMay-Grunwald-GiemsaHypomethylating agentsBone marrowAnalysis of hematological malignanciesSomatic mutation screeningEvaluation of T lymphocytesResponse to hypomethylating agentsCD34+ bone marrowStudies of myelodysplastic syndromesGenomic featuresMDS populationRNA-seqPrediction of patient outcomeGenomic characterizationHarrell's concordance indexPredicting Clinical OutcomesHematoxylin and eosin (H&EMorphological dataMulti-OmicsData-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes
Komrokji R, Lanino L, Ball S, Bewersdorf J, Marchetti M, Maggioni G, Travaglino E, Al Ali N, Fenaux P, Platzbecker U, Santini V, Diez-Campelo M, Singh A, Jain A, Aguirre L, Tinsley-Vance S, Schwabkey Z, Chan O, Xie Z, Brunner A, Kuykendall A, Bennett J, Buckstein R, Bejar R, Carraway H, DeZern A, Griffiths E, Halene S, Hasserjian R, Lancet J, List A, Loghavi S, Odenike O, Padron E, Patnaik M, Roboz G, Stahl M, Sekeres M, Steensma D, Savona M, Taylor J, Xu M, Sweet K, Sallman D, Nimer S, Hourigan C, Wei A, Sauta E, D’Amico S, Asti G, Castellani G, Delleani M, Campagna A, Borate U, Sanz G, Efficace F, Gore S, Kim T, Daver N, Garcia-Manero G, Rozman M, Orfao A, Wang A, Foucar M, Germing U, Haferlach T, Scheinberg P, Miyazaki Y, Iastrebner M, Kulasekararaj A, Cluzeau T, Kordasti S, van de Loosdrecht A, Ades L, Zeidan A, Della Porta M, Syndromes I. Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes. The Lancet Haematology 2024, 11: e862-e872. PMID: 39393368, DOI: 10.1016/s2352-3026(24)00251-5.Peer-Reviewed Original ResearchGenomic featuresData-driven approachTP53 inactivationGenomic heterogeneityEntity labelsGenetic featuresDel(7q)/-7Myelodysplastic syndromeGenomic profilingData scientistsMutated SF3B1Cluster assignmentComplex karyotypeRUNX1 mutationsModified Delphi consensus processDel(5qIsolated del(5qAcute myeloid leukemiaData-DrivenDelphi consensus processMarrow blastsPrecision treatment paradigm: Genomic features and therapeutic implications in mesenchymal‐epithelial transition‐amplified gastric cancer
Yu Y, Zhang Z, Zhu M, Shan Y, Wang Y, Wei L, Huang X, Sun D, Peng Z, Liu T. Precision treatment paradigm: Genomic features and therapeutic implications in mesenchymal‐epithelial transition‐amplified gastric cancer. Clinical And Translational Discovery 2024, 4 DOI: 10.1002/ctd2.350.Peer-Reviewed Original ResearchSingle nucleotide variantsCopy number variationsGenomic featuresMET amplificationOverall survivalCohort 1TCGA cohortMesenchymal-epithelial transitionGastric cancerSignificant copy number variationsCohort 2Nucleotide variantsPI3K pathwayCancer Genome AtlasNumber variationsRNA dataExpression analysisMutational landscapeProgression-FreeClinical responseK pathwayMET therapyChinese patientsKaplan-MeierTreated patientsMutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting
Prosz A, Sahgal P, Huffman B, Sztupinszki Z, Morris C, Chen D, Börcsök J, Diossy M, Tisza V, Spisak S, Likasitwatanakul P, Rusz O, Csabai I, Cecchini M, Baca Y, Elliott A, Enzinger P, Singh H, Ubellaker J, Lazaro J, Cleary J, Szallasi Z, Sethi N. Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting. Npj Precision Oncology 2024, 8: 87. PMID: 38589664, PMCID: PMC11001913, DOI: 10.1038/s41698-024-00561-6.Peer-Reviewed Original ResearchNucleotide excision repairGastric cancer cell linesNucleotide excision repair-deficientPlatinum chemotherapyHR deficiencyCancer cell linesPARP inhibitorsHomologous recombinationGenome sequence dataSensitivity to platinum chemotherapySingle-cell RNA sequencingCell linesHR-deficient cancersDNA repair pathwaysSensitivity to cisplatinRad51 foci assaysMutational signature analysisSequence dataGenomic featuresWhole exomeInduce apoptosisRNA sequencingGastroesophageal adenocarcinomaRepair pathwaysHRD scoreClinical and molecular characterization of urothelial (UC) vs. small cell carcinoma (SCC) of the urinary tract.
Chawla N, Mercier B, Govindarajan A, Li X, Castro D, Ebrahimi H, Barragan-Carrillo R, Zang P, LeVee A, Dizman N, Hsu J, Meza L, Zengin Z, Salgia N, Chehrazi-Raffle A, Dorff T, Pal S, Tripathi A. Clinical and molecular characterization of urothelial (UC) vs. small cell carcinoma (SCC) of the urinary tract. Journal Of Clinical Oncology 2024, 42: 682-682. DOI: 10.1200/jco.2024.42.4_suppl.682.Peer-Reviewed Original ResearchSmall cell carcinomaFeatures of small cell carcinomaGenome sequencePrevalence of genomic alterationsSmall cell carcinoma patientsSmall cell carcinoma groupFrequency of CDKN2APrevalence of TP53Fisher's exact testTumor gradeCell carcinomaPathological variablesUse of bloodUrinary tractBladder cancerExact testGenomic alterationsProportion Z testGenomic profilingGenomic featuresPathological featuresTherapeutic strategiesGenomic datasetsGenomic characteristicsBonferroni correctionMemory B cell subsets have divergent developmental origins that are coupled to distinct imprinted epigenetic states
Callahan D, Smita S, Joachim S, Hoehn K, Kleinstein S, Weisel F, Chikina M, Shlomchik M. Memory B cell subsets have divergent developmental origins that are coupled to distinct imprinted epigenetic states. Nature Immunology 2024, 25: 562-575. PMID: 38200277, PMCID: PMC12036331, DOI: 10.1038/s41590-023-01721-9.Peer-Reviewed Original ResearchGerminal center B cellsDistinct genomic featuresDP cellsDevelopmental originsEpigenetic stateFunctional diversityEpigenetic patternsTranscriptional profilingGenomic featuresDN cellsDistinct developmental historiesB cellsReporter miceFunctional responseCellsMemory B cellsChromatinB cell subsetsCell-dependent responsesMultiple approachesProgenyDiversityT cell-dependent responsesGerminal centersDevelopmental history
2023
Patient-derived glioblastoma cell lines with conserved genome profiles of the original tissue
Kim S, Cho Y, Shin Y, Yu H, Chowdhury T, Kim S, Yi K, Choi C, Cha S, Park C, Ku J. Patient-derived glioblastoma cell lines with conserved genome profiles of the original tissue. Scientific Data 2023, 10: 448. PMID: 37438387, PMCID: PMC10338444, DOI: 10.1038/s41597-023-02365-y.Peer-Reviewed Original ResearchConceptsCell linesPatient-derived glioblastoma cell linesPatient-derived cell linesWhole exome sequencing datasetsExome sequencing datasetsGBM cell linesGlioblastoma cell linesSequence dataGenomic featuresLethal intracranial tumorSequencing technologiesSequencing datasetsMolecular markersWES datasetsGenome profilesMutational signaturesDruggable targetsNumber alterationsBiological credibilityGenomic profilesBiological platformMolecular characteristicsOriginal tissueTumor tissueGlioblastomaNetwork embedding unveils the hidden interactions in the mammalian virome
Poisot T, Ouellet M, Mollentze N, Farrell M, Becker D, Brierley L, Albery G, Gibb R, Seifert S, Carlson C. Network embedding unveils the hidden interactions in the mammalian virome. Patterns 2023, 4: 100738. PMID: 37409053, PMCID: PMC10318366, DOI: 10.1016/j.patter.2023.100738.Peer-Reviewed Original ResearchMammalian viromeViral genomic featuresHost-virus interactionsGraph embeddingGenomic featuresNetwork science problemUnder-characterizedFundamental biologyHuman infectionsViromeRecommender systemsDiscovery effortsAmazon basinDisease emergenceHidden interactionsImputation algorithmData biasNetworkLinear filterScience problems
2022
A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes
Toh H, Yang C, Formenti G, Raja K, Yan L, Tracey A, Chow W, Howe K, Bergeron L, Zhang G, Haase B, Mountcastle J, Fedrigo O, Fogg J, Kirilenko B, Munegowda C, Hiller M, Jain A, Kihara D, Rhie A, Phillippy A, Swanson S, Jiang P, Clegg D, Jarvis E, Thomson J, Stewart R, Chaisson M, Bukhman Y. A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes. BMC Biology 2022, 20: 245. PMID: 36344967, PMCID: PMC9641963, DOI: 10.1186/s12915-022-01427-8.Peer-Reviewed Original ResearchConceptsVertebrate Genomes ProjectGenome assemblyChromosome-level reference genome assemblyLevels of genomic resolutionGenes associated with type 2 diabetesReference genome assemblyNile ratContig N50Scaffold N50Diet-induced diabetesGenomic resolutionDuplicated GenesSegmental duplicationsGenomic featuresGenome ProjectModel organismsRobust diurnal rhythmParental haplotypesGenetic basisHouse miceMus musculusCone-rich retinaGenesN50Genetic modification
2021
Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning
Park JJ, Chen S. Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning. Patterns 2021, 3: 100407. PMID: 34812427, PMCID: PMC8598947, DOI: 10.1016/j.patter.2021.100407.Peer-Reviewed Original ResearchAmino acid resolutionSARS-CoV-2 genomeGenomic featuresGenetic elementsCoronavirus genomeGenomeFunctional studiesSequence targetsGenomic profilesUnbiased collectionMajor threatPathogenic virusesUnappreciated featureAnimal originInterpretable signaturesRapid characterizationPathogenic regionsSARS-CoVB cellsSystematic mapRdRpNucleotidesEpitope predictionProteinPathogenicityWhole-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
NIMBus: 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 modelPrioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features
Ray JP, de Boer CG, Fulco CP, Lareau CA, Kanai M, Ulirsch JC, Tewhey R, Ludwig LS, Reilly SK, Bergman DT, Engreitz JM, Issner R, Finucane HK, Lander ES, Regev A, Hacohen N. Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features. Nature Communications 2020, 11: 1237. PMID: 32144282, PMCID: PMC7060350, DOI: 10.1038/s41467-020-15022-4.Peer-Reviewed Original ResearchConceptsChromatin accessible regionsGenome-wide association studiesDisease-associated lociGenetic variantsCausal genetic variantsDisease-Associated VariantsComplex traitsGenetic variationRegulatory regionsGenomic featuresCausal variantsRegulatory potentialAssociation studiesReporter activityDisease-associated haplotypeLinkage disequilibriumCommon variantsTight linkage disequilibriumExperimental assaysCell linesImmune cell linesLociAccessible regionsTNFAIP3TNFAIP3 locusFine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, Pooley KA, Dennis J, Michailidou K, Turman C, Soucy P, Lemaçon A, Lush M, Tyrer JP, Ghoussaini M, Moradi Marjaneh M, Jiang X, Agata S, Aittomäki K, Alonso MR, Andrulis IL, Anton-Culver H, Antonenkova NN, Arason A, Arndt V, Aronson KJ, Arun BK, Auber B, Auer PL, Azzollini J, Balmaña J, Barkardottir RB, Barrowdale D, Beeghly-Fadiel A, Benitez J, Bermisheva M, Białkowska K, Blanco AM, Blomqvist C, Blot W, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Borg A, Bosse K, Brauch H, Brenner H, Briceno I, Brock IW, Brooks-Wilson A, Brüning T, Burwinkel B, Buys SS, Cai Q, Caldés T, Caligo MA, Camp NJ, Campbell I, Canzian F, Carroll JS, Carter BD, Castelao JE, Chiquette J, Christiansen H, Chung WK, Claes KBM, Clarke CL, Collée J, Cornelissen S, Couch F, Cox A, Cross S, Cybulski C, Czene K, Daly M, de la Hoya M, Devilee P, Diez O, Ding Y, Dite G, Domchek S, Dörk T, dos-Santos-Silva I, Droit A, Dubois S, Dumont M, Duran M, Durcan L, Dwek M, Eccles D, Engel C, Eriksson M, Evans D, Fasching P, Fletcher O, Floris G, Flyger H, Foretova L, Foulkes W, Friedman E, Fritschi L, Frost D, Gabrielson M, Gago-Dominguez M, Gambino G, Ganz P, Gapstur S, Garber J, García-Sáenz J, Gaudet M, Georgoulias V, Giles G, Glendon G, Godwin A, Goldberg M, Goldgar D, González-Neira A, Tibiletti M, Greene M, Grip M, Gronwald J, Grundy A, Guénel P, Hahnen E, Haiman C, Håkansson N, Hall P, Hamann U, Harrington P, Hartikainen J, Hartman M, He W, Healey C, Heemskerk-Gerritsen B, Heyworth J, Hillemanns P, Hogervorst F, Hollestelle A, Hooning M, Hopper J, Howell A, Huang G, Hulick P, Imyanitov E, Isaacs C, Iwasaki M, Jager A, Jakimovska M, Jakubowska A, James P, Janavicius R, Jankowitz R, John E, Johnson N, Jones M, Jukkola-Vuorinen A, Jung A, Kaaks R, Kang D, Kapoor P, Karlan B, Keeman R, Kerin M, Khusnutdinova E, Kiiski J, Kirk J, Kitahara C, Ko Y, Konstantopoulou I, Kosma V, Koutros S, Kubelka-Sabit K, Kwong A, Kyriacou K, Laitman Y, Lambrechts D, Lee E, Leslie G, Lester J, Lesueur F, Lindblom A, Lo W, Long J, Lophatananon A, Loud J, Lubiński J, MacInnis R, Maishman T, Makalic E, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez M, Matsuo K, Maurer T, Mavroudis D, Mayes R, McGuffog L, McLean C, Mebirouk N, Meindl A, Miller A, Miller N, Montagna M, Moreno F, Muir K, Mulligan A, Muñoz-Garzon V, Muranen T, Narod S, Nassir R, Nathanson K, Neuhausen S, Nevanlinna H, Neven P, Nielsen F, Nikitina-Zake L, Norman A, Offit K, Olah E, Olopade O, Olsson H, Orr N, Osorio A, Pankratz V, Papp J, Park S, Park-Simon T, Parsons M, Paul J, Pedersen I, Peissel B, Peshkin B, Peterlongo P, Peto J, Plaseska-Karanfilska D, Prajzendanc K, Prentice R, Presneau N, Prokofyeva D, Pujana M, Pylkäs K, Radice P, Ramus S, Rantala J, Rau-Murthy R, Rennert G, Risch H, Robson M, Romero A, Rossing M, Saloustros E, Sánchez-Herrero E, Sandler D, Santamariña M, Saunders C, Sawyer E, Scheuner M, Schmidt D, Schmutzler R, Schneeweiss A, Schoemaker M, Schöttker B, Schürmann P, Scott C, Scott R, Senter L, Seynaeve C, Shah M, Sharma P, Shen C, Shu X, Singer C, Slavin T, Smichkoska S, Southey M, Spinelli J, Spurdle A, Stone J, Stoppa-Lyonnet D, Sutter C, Swerdlow A, Tamimi R, Tan Y, Tapper W, Taylor J, Teixeira M, Tengström M, Teo S, Terry M, Teulé A, Thomassen M, Thull D, Tischkowitz M, Toland A, Tollenaar R, Tomlinson I, Torres D, Torres-Mejía G, Troester M, Truong T, Tung N, Tzardi M, Ulmer H, Vachon C, van Asperen C, van der Kolk L, van Rensburg E, Vega A, Viel A, Vijai J, Vogel M, Wang Q, Wappenschmidt B, Weinberg C, Weitzel J, Wendt C, Wildiers H, Winqvist R, Wolk A, Wu A, Yannoukakos D, Zhang Y, Zheng W, Hunter D, Pharoah P, Chang-Claude J, García-Closas M, Schmidt M, Milne R, Kristensen V, French J, Edwards S, Antoniou A, Chenevix-Trench G, Simard J, Easton D, Kraft P, Dunning A. Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nature Genetics 2020, 52: 56-73. PMID: 31911677, PMCID: PMC6974400, DOI: 10.1038/s41588-019-0537-1.Peer-Reviewed Original ResearchConceptsCausal variantsTranscription factorsTarget genesActive gene regulatory regionsHigh-confidence target genesGenomic feature annotationsGenome-wide association studiesBreast cancer risk variantsGene regulatory regionsCredible causal variantsGene ontology pathwaysChromatin interactionsFunctional annotationGenomic regionsOntology pathwaysRegulatory regionsGenomic featuresCancer driversGene expressionAssociation studiesAssociation analysisGenesLinkage disequilibriumRisk variantsHigh posterior probability
2018
Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS
Dobbyn A, Huckins L, Boocock J, Sloofman L, Glicksberg B, Giambartolomei C, Hoffman G, Perumal T, Girdhar K, Jiang Y, Raj T, Ruderfer D, Kramer R, Pinto D, Akbarian S, Roussos P, Domenici E, Devlin B, Sklar P, Stahl E, Sieberts S, Sklar P, Buxbaum J, Devlin B, Lewis D, Gur R, Hahn C, Hirai K, Toyoshiba H, Domenici E, Essioux L, Mangravite L, Peters M, Lehner T, Lipska B, Cicek A, Lu C, Roeder K, Xie L, Talbot K, Hemby S, Essioux L, Browne A, Chess A, Topol A, Charney A, Dobbyn A, Readhead B, Zhang B, Pinto D, Bennett D, Kavanagh D, Ruderfer D, Stahl E, Schadt E, Hoffman G, Shah H, Zhu J, Johnson J, Fullard J, Dudley J, Girdhar K, Brennand K, Sloofman L, Huckins L, Fromer M, Mahajan M, Roussos P, Akbarian S, Purcell S, Hamamsy T, Raj T, Haroutunian V, Wang Y, Gümüş Z, Senthil G, Kramer R, Logsdon B, Derry J, Dang K, Sieberts S, Perumal T, Visintainer R, Shinobu L, Sullivan P, Klei L. Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. American Journal Of Human Genetics 2018, 102: 1169-1184. PMID: 29805045, PMCID: PMC5993513, DOI: 10.1016/j.ajhg.2018.04.011.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociConditional expression quantitative trait lociCommonMind ConsortiumEQTL signalsGenome-wide association study (GWAS) lociSchizophrenia GWASContext-specific regulationQuantitative trait lociCo-localization analysisGene expression levelsGWAS associationsNovel genesTrait lociStudy lociCausal genesEQTL dataFine mappingGenomic featuresGWAS statisticsGene expressionGenesGWASLociExpression levelsHuman brain samples
2017
Comparative analysis reveals genomic features of stress-induced transcriptional readthrough
Vilborg A, Sabath N, Wiesel Y, Nathans J, Levy-Adam F, Yario TA, Steitz JA, Shalgi R. Comparative analysis reveals genomic features of stress-induced transcriptional readthrough. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: e8362-e8371. PMID: 28928151, PMCID: PMC5635911, DOI: 10.1073/pnas.1711120114.Peer-Reviewed Original ResearchConceptsTranscriptional readthroughReadthrough transcriptionGenomic featuresOsmotic stressProtein-coding gene lociHeat shockUnique chromatin signatureGenome-wide mappingOpen chromatin statePolymerase II occupancyNuclear RNA-seqGenome-wide studiesChromatin signaturesChromatin stateNIH 3T3 mouse fibroblast cellsNeighboring genesRNA classesReadthrough transcriptsReadthrough phenomenonRegulated processRNA-seqGene transcriptionGene locusStress responsePotential regulatorLandscape and variation of novel retroduplications in 26 human populations
Zhang Y, Li S, Abyzov A, Gerstein MB. Landscape and variation of novel retroduplications in 26 human populations. PLOS Computational Biology 2017, 13: e1005567. PMID: 28662076, PMCID: PMC5510864, DOI: 10.1371/journal.pcbi.1005567.Peer-Reviewed Original ResearchConceptsParent genesSequencing dataHigh-coverage exomesLow-coverage whole-genome sequencing dataHuman populationWhole-genome sequencing dataExon-exon junctionsGenomes Phase 3Young L1 elementsPaired-end readsPotential disease associationsRetrotranspositional activityGenomic elementsNucleosome positioningPhylogenetic treeRetroduplicationExome sequencing dataReference genomeGenomic featuresInsertion eventsL1 elementsComprehensive discoveryPopulation markersSNP callingFunctional regions
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