2024
Resolving the 22q11.2 deletion using CTLR-Seq reveals chromosomal rearrangement mechanisms and individual variance in breakpoints
Zhou B, Purmann C, Guo H, Shin G, Huang Y, Pattni R, Meng Q, Greer S, Roychowdhury T, Wood R, Ho M, Dohna H, Abyzov A, Hallmayer J, Wong W, Ji H, Urban A. Resolving the 22q11.2 deletion using CTLR-Seq reveals chromosomal rearrangement mechanisms and individual variance in breakpoints. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2322834121. PMID: 39042694, PMCID: PMC11295037, DOI: 10.1073/pnas.2322834121.Peer-Reviewed Original ResearchConceptsLong-read sequencingPulse-field gel electrophoresisBase-pair resolutionDNA methylation patternsCell-type specific analysisCell type-specificChromosomal interactionsSequence assemblySegmental duplicationsGenome sequenceGenomic rearrangementsGenomic regionsChromosomal breakpointsHuman genomeGenomic recombinationMethylation patternsSequence analysisHaplotype-specificDeletion haplotypesGel electrophoresisGenomeAmplification-freeBreakpoint locationsMicrodeletion disorderType-specific
2023
Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target
Roychowdhury T, Klarin D, Levin M, Spin J, Rhee Y, Deng A, Headley C, Tsao N, Gellatly C, Zuber V, Shen F, Hornsby W, Laursen I, Verma S, Locke A, Einarsson G, Thorleifsson G, Graham S, Dikilitas O, Pattee J, Judy R, Pauls-Verges F, Nielsen J, Wolford B, Brumpton B, Dilmé J, Peypoch O, Juscafresa L, Edwards T, Li D, Banasik K, Brunak S, Jacobsen R, Garcia-Barrio M, Zhang J, Rasmussen L, Lee R, Handa A, Wanhainen A, Mani K, Lindholt J, Obel L, Strauss E, Oszkinis G, Nelson C, Saxby K, van Herwaarden J, van der Laan S, van Setten J, Camacho M, Davis F, Wasikowski R, Tsoi L, Gudjonsson J, Eliason J, Coleman D, Henke P, Ganesh S, Chen Y, Guan W, Pankow J, Pankratz N, Pedersen O, Erikstrup C, Tang W, Hveem K, Gudbjartsson D, Gretarsdottir S, Thorsteinsdottir U, Holm H, Stefansson K, Ferreira M, Baras A, Kullo I, Ritchie M, Christensen A, Iversen K, Eldrup N, Sillesen H, Ostrowski S, Bundgaard H, Ullum H, Burgess S, Gill D, Gallagher K, Sabater-Lleal M, Surakka I, Jones G, Bown M, Tsao P, Willer C, Damrauer S. Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target. Nature Genetics 2023, 55: 1831-1842. PMID: 37845353, PMCID: PMC10632148, DOI: 10.1038/s41588-023-01510-y.Peer-Reviewed Original ResearchConceptsAbdominal aortic aneurysmDevelopment of AAAAortic aneurysmNonhigh-density lipoprotein cholesterolLipid metabolismExtracellular matrix dysregulationClinical risk factorsPreclinical mouse modelsGrowth factor β signalingLipoprotein cholesterolMatrix dysregulationIndependent associationRisk factorsPCSK9 lossDiscovery cohortPolygenic risk scoresRisk lociAAA pathogenesisMouse modelRisk scoreTherapeutic targetAAA riskΒ signalingCommon diseaseMendelian randomizationGenome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program
Klarin D, Devineni P, Sendamarai A, Angueira A, Graham S, Shen Y, Levin M, Pirruccello J, Surakka I, Karnam P, Roychowdhury T, Li Y, Wang M, Aragam K, Paruchuri K, Zuber V, Shakt G, Tsao N, Judy R, Vy H, Verma S, Rader D, Do R, Bavaria J, Nadkarni G, Ritchie M, Burgess S, Guo D, Ellinor P, LeMaire S, Milewicz D, Willer C, Natarajan P, Tsao P, Pyarajan S, Damrauer S. Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program. Nature Genetics 2023, 55: 1106-1115. PMID: 37308786, PMCID: PMC10335930, DOI: 10.1038/s41588-023-01420-z.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesMillion Veteran ProgramProtein-altering variantsHuman genetic evidenceGenetic architectureComplex traitsGenetic evidenceDNA sequencesRisk lociMendelian formsRisk genesCell typesGenetic determinantsVeteran ProgramCurrent understandingGenesLociTraitsTAADReplicationSequenceVariants
2021
Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm
Roychowdhury T, Lu H, Hornsby WE, Crone B, Wang GT, Guo DC, Sendamarai AK, Devineni P, Lin M, Zhou W, Graham SE, Wolford BN, Surakka I, Wang Z, Chang L, Zhang J, Mathis M, Brummett CM, Melendez TL, Shea MJ, Kim KM, Deeb GM, Patel HJ, Eliason J, Eagle KA, Yang B, Ganesh SK, Brumpton B, Åsvold BO, Skogholt AH, Hveem K, Program V, Pyarajan S, Klarin D, Tsao PS, Damrauer SM, Leal SM, Milewicz DM, Chen YE, Garcia-Barrio MT, Willer CJ. Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm. American Journal Of Human Genetics 2021, 108: 1578-1589. PMID: 34265237, PMCID: PMC8456156, DOI: 10.1016/j.ajhg.2021.06.016.Peer-Reviewed Original ResearchMeSH KeywordsAortaAortic Aneurysm, ThoracicBcl-2-Associated X ProteinCase-Control StudiesCaspase 3Diabetes Mellitus, Type 2Endothelial CellsGene Expression RegulationGenome, HumanGenome-Wide Association StudyHumansIntronsMichiganMuscle, Smooth, VascularMutationProto-Oncogene Proteins c-bcl-2Quantitative Trait LociTranscription Factor 7-Like 2 ProteinConceptsGenome-wide significant associationDifferent causal variantsGenome-wide scanCommon variantsVascular smooth muscle cell apoptosisSmooth muscle cell apoptosisMuscle cell apoptosisRegulatory variantsGenomics initiativesSingle geneThird intronCausal variantsMichigan Genomics InitiativeHeritable diseaseType 2 diabetesCell apoptosisGenetic associationAdditional independent cohortsTCF7L2Aortic aneurysmLociHigh expressionTAAGenetic risk factorsFunctional relationshipPattern of genomic variation in SARS-CoV-2 (COVID-19) suggests restricted nonrandom changes: Analysis using Shewhart control charts
MANDAL S, ROYCHOWDHURY T, BHATTACHARYA A. Pattern of genomic variation in SARS-CoV-2 (COVID-19) suggests restricted nonrandom changes: Analysis using Shewhart control charts. Journal Of Biosciences 2021, 46: 11. PMID: 33709963, PMCID: PMC7856336, DOI: 10.1007/s12038-020-00131-5.Peer-Reviewed Original Research
2020
Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease
Nielsen JB, Rom O, Surakka I, Graham SE, Zhou W, Roychowdhury T, Fritsche LG, Gagliano Taliun SA, Sidore C, Liu Y, Gabrielsen ME, Skogholt AH, Wolford B, Overton W, Zhao Y, Chen J, Zhang H, Hornsby WE, Acheampong A, Grooms A, Schaefer A, Zajac GJM, Villacorta L, Zhang J, Brumpton B, Løset M, Rai V, Lundegaard PR, Olesen MS, Taylor KD, Palmer ND, Chen YD, Choi SH, Lubitz SA, Ellinor PT, Barnes KC, Daya M, Rafaels N, Weiss ST, Lasky-Su J, Tracy RP, Vasan RS, Cupples LA, Mathias RA, Yanek LR, Becker LC, Peyser PA, Bielak LF, Smith JA, Aslibekyan S, Hidalgo BA, Arnett DK, Irvin MR, Wilson JG, Musani SK, Correa A, Rich SS, Guo X, Rotter JI, Konkle BA, Johnsen JM, Ashley-Koch AE, Telen MJ, Sheehan VA, Blangero J, Curran JE, Peralta JM, Montgomery C, Sheu WH, Chung RH, Schwander K, Nouraie SM, Gordeuk VR, Zhang Y, Kooperberg C, Reiner AP, Jackson RD, Bleecker ER, Meyers DA, Li X, Das S, Yu K, LeFaive J, Smith A, Blackwell T, Taliun D, Zollner S, Forer L, Schoenherr S, Fuchsberger C, Pandit A, Zawistowski M, Kheterpal S, Brummett CM, Natarajan P, Schlessinger D, Lee S, Kang HM, Cucca F, Holmen OL, Åsvold BO, Boehnke M, Kathiresan S, Abecasis GR, Chen YE, Willer CJ, Hveem K. Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease. Nature Communications 2020, 11: 6417. PMID: 33339817, PMCID: PMC7749177, DOI: 10.1038/s41467-020-20086-3.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseProtein-altering variantsNon-fasting blood glucoseFatty liver diseaseMore cardiovascular diseasesPotential therapeutic targetNovel candidate drug targetsDrug targetsLipoprotein cholesterolLiver diseaseLiver functionHUNT StudyBlood glucoseLiver enzymesMetabolic disordersCandidate drug targetsTherapeutic targetLDL uptakeLDL receptorDiseaseHepatoma cells resultsAdverse effectsBlood traitsDyslipidemiaBeneficial impactGWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer
Zhou W, Brumpton B, Kabil O, Gudmundsson J, Thorleifsson G, Weinstock J, Zawistowski M, Nielsen JB, Chaker L, Medici M, Teumer A, Naitza S, Sanna S, Schultheiss UT, Cappola A, Karjalainen J, Kurki M, Oneka M, Taylor P, Fritsche LG, Graham SE, Wolford BN, Overton W, Rasheed H, Haug EB, Gabrielsen ME, Skogholt AH, Surakka I, Davey Smith G, Pandit A, Roychowdhury T, Hornsby WE, Jonasson JG, Senter L, Liyanarachchi S, Ringel MD, Xu L, Kiemeney LA, He H, Netea-Maier RT, Mayordomo JI, Plantinga TS, Hrafnkelsson J, Hjartarson H, Sturgis EM, Palotie A, Daly M, Citterio CE, Arvan P, Brummett CM, Boehnke M, de la Chapelle A, Stefansson K, Hveem K, Willer CJ, Åsvold BO. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer. Nature Communications 2020, 11: 3981. PMID: 32769997, PMCID: PMC7414135, DOI: 10.1038/s41467-020-17718-z.Peer-Reviewed Original ResearchConceptsPleiotropic effectsGenome-wide significant lociPhenome-wide association analysisSignificant lociGenetic markersAssociation analysisUK BiobankNormal developmentFunctional experimentsIndex variantsGenetic contributionGWASTwo-sample Mendelian randomizationPolygenic scoresVariantsMendelian randomizationLociG67Independent studiesThyroid cancerMetabolismBiobankHormoneThyroglobulin secretion
2019
Chromatin organization modulates the origin of heritable structural variations in human genome
Roychowdhury T, Abyzov A. Chromatin organization modulates the origin of heritable structural variations in human genome. Nucleic Acids Research 2019, 47: 2766-2777. PMID: 30773596, PMCID: PMC6451188, DOI: 10.1093/nar/gkz103.Peer-Reviewed Original Research
2018
Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, Won H, van Bakel H, Varghese M, Wang Y, Shieh AW, Haney J, Parhami S, Belmont J, Kim M, Moran Losada P, Khan Z, Mleczko J, Xia Y, Dai R, Wang D, Yang YT, Xu M, Fish K, Hof PR, Warrell J, Fitzgerald D, White K, Jaffe AE, Peters M, Gerstein M, Liu C, Iakoucheva L, Pinto D, Geschwind D, Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 2018, 362 PMID: 30545856, PMCID: PMC6443102, DOI: 10.1126/science.aat8127.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyTranscriptome-wide characterizationPathogenic dysregulationTranscriptomic organizationDifferential splicingCoexpression networkGenetic enrichmentRegulatory regionsGenomic dataGene expressionAssociation studiesDisease locusComprehensive resourceMechanistic insightsCis effectSplicingBipolar disorderNeural-immune mechanismsMolecular pathologyTherapeutic developmentAutism spectrum disorderExpressionMajor psychiatric disordersBrain expressionDiseased brainComprehensive functional genomic resource and integrative model for the human brain
Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, Xu M, Gandal MJ, Lou S, Zhang J, Park JJ, Yan C, Rhie SK, Manakongtreecheep K, Zhou H, Nathan A, Peters M, Mattei E, Fitzgerald D, Brunetti T, Moore J, Jiang Y, Girdhar K, Hoffman GE, Kalayci S, Gümüş ZH, Crawford GE, Roussos P, Akbarian S, Jaffe A, White K, Weng Z, Sestan N, Geschwind D, Knowles J, Gerstein M, Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018, 362 PMID: 30545857, PMCID: PMC6413328, DOI: 10.1126/science.aat8464.Peer-Reviewed Original ResearchConceptsQuantitative trait lociCell type proportionsComprehensive functional genomics resourceExpression quantitative trait lociFunctional genomics resourcesSingle-cell expression profilesGene regulatory networksFurther quantitative trait lociPsychENCODE ConsortiumGenomic resourcesComprehensive online resourceRegulatory networksKey genesCross-population variationExpression profilesMolecular mechanismsCell typesGenesAdult brainPolygenic risk scoresStudy variantsChromatinSplicingGenetic riskInterpretable deep learning modelTranscriptome and epigenome landscape of human cortical development modeled in organoids
Amiri A, Coppola G, Scuderi S, Wu F, Roychowdhury T, Liu F, Pochareddy S, Shin Y, Safi A, Song L, Zhu Y, Sousa AMM, Gerstein M, Crawford G, Sestan N, Abyzov A, Vaccarino F, Akbarian S, An J, Armoskus C, Ashley-Koch A, Beach T, Belmont J, Bendl J, Borrman T, Brown L, Brown M, Brown M, Brunetti T, Bryois J, Burke E, Camarena A, Carlyle B, Chae Y, Charney A, Chen C, Cheng L, Cherskov A, Choi J, Clarke D, Collado-Torres L, Dai R, De La Torre Ubieta L, DelValle D, Devillers O, Dracheva S, Emani P, Evgrafov O, Farnham P, Fitzgerald D, Flatow E, Francoeur N, Fullard J, Gandal M, Gao T, Garrett M, Geschwind D, Giase G, Girdhar K, Giusti-Rodriguez P, Goes F, Goodman T, Grennan K, Gu M, Gürsoy G, Hadjimichael E, Hahn C, Haroutunian V, Hauberg M, Hoffman G, Huey J, Hyde T, Ivanov N, Jacobov R, Jaffe A, Jiang Y, Jiang Y, Johnson G, Kassim B, Kefi A, Kim Y, Kitchen R, Kleiman J, Knowles J, Kozlenkov A, Li M, Li Z, Lipska B, Liu C, Liu S, Mangravite L, Mariani J, Mattei E, Miller D, Moore J, Nairn A, Navarro F, Park R, Peters M, Pinto D, Pochareddy S, Polioudakis D, Pratt H, Price A, Purcaro M, Ray M, Reddy T, Rhie S, Roussos P, Sanders S, Santpere G, Schreiner S, Sheppard B, Shi X, Shieh A, Shin J, Skarica M, Song L, Sousa A, Spitsyna V, State M, Sullivan P, Swarup V, Szatkiewicz J, Szekely A, Tao R, van Bakel H, Wang Y, Wang D, Warrell J, Webster M, Weissman S, Weng Z, Werling D, White K, Willsey J, Wiseman J, Witt H, Won H, Wray G, Xia Y, Xu M, Yang Y, Yang M, Zandi P, Zhang J, Zharovsky E. Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science 2018, 362 PMID: 30545853, PMCID: PMC6426303, DOI: 10.1126/science.aat6720.Peer-Reviewed Original ResearchIntegrative functional genomic analysis of human brain development and neuropsychiatric risks
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw C, Pardiñas AF, Hu M, Jin F, Li Y, Owen M, O’Donovan M, Walters J, Posthuma D, Reimers M, Levitt P, Weinberger D, Hyde T, Kleinman J, Geschwind D, Hawrylycz M, State M, Sanders S, Sullivan P, Gerstein M, Lein E, Knowles J, Sestan N, Willsey A, Oldre A, Szafer A, Camarena A, Cherskov A, Charney A, Abyzov A, Kozlenkov A, Safi A, Jones A, Ashley-Koch A, Ebbert A, Price A, Sekijima A, Kefi A, Bernard A, Amiri A, Sboner A, Clark A, Jaffe A, Tebbenkamp A, Sodt A, Guillozet-Bongaarts A, Nairn A, Carey A, Huttner A, Chervenak A, Szekely A, Shieh A, Harmanci A, Lipska B, Carlyle B, Gregor B, Kassim B, Sheppard B, Bichsel C, Hahn C, Lee C, Chen C, Kuan C, Dang C, Lau C, Cuhaciyan C, Armoskus C, Mason C, Liu C, Slaughterbeck C, Bennet C, Pinto D, Polioudakis D, Franjic D, Miller D, Bertagnolli D, Lewis D, Feng D, Sandman D, Clarke D, Williams D, DelValle D, Fitzgerald D, Shen E, Flatow E, Zharovsky E, Burke E, Olson E, Fulfs E, Mattei E, Hadjimichael E, Deelman E, Navarro F, Wu F, Lee F, Cheng F, Goes F, Vaccarino F, Liu F, Hoffman G, Gürsoy G, Gee G, Mehta G, Coppola G, Giase G, Sedmak G, Johnson G, Wray G, Crawford G, Gu G, van Bakel H, Witt H, Yoon H, Pratt H, Zhao H, Glass I, Huey J, Arnold J, Noonan J, Bendl J, Jochim J, Goldy J, Herstein J, Wiseman J, Miller J, Mariani J, Stoll J, Moore J, Szatkiewicz J, Leng J, Zhang J, Parente J, Rozowsky J, Fullard J, Hohmann J, Morris J, Phillips J, Warrell J, Shin J, An J, Belmont J, Nyhus J, Pendergraft J, Bryois J, Roll K, Grennan K, Aiona K, White K, Aldinger K, Smith K, Girdhar K, Brouner K, Mangravite L, Brown L, Collado-Torres L, Cheng L, Gourley L, Song L, Ubieta L, Habegger L, Ng L, Hauberg M, Onorati M, Webster M, Kundakovic M, Skarica M, Reimers M, Johnson M, Chen M, Garrett M, Sarreal M, Reding M, Gu M, Peters M, Fisher M, Gandal M, Purcaro M, Smith M, Brown M, Shibata M, Brown M, Xu M, Yang M, Ray M, Shapovalova N, Francoeur N, Sjoquist N, Mastan N, Kaur N, Parikshak N, Mosqueda N, Ngo N, Dee N, Ivanov N, Devillers O, Roussos P, Parker P, Manser P, Wohnoutka P, Farnham P, Zandi P, Emani P, Dalley R, Mayani R, Tao R, Gittin R, Straub R, Lifton R, Jacobov R, Howard R, Park R, Dai R, Abramowicz S, Akbarian S, Schreiner S, Ma S, Parry S, Shapouri S, Weissman S, Caldejon S, Mane S, Ding S, Scuderi S, Dracheva S, Butler S, Lisgo S, Rhie S, Lindsay S, Datta S, Souaiaia T, Roychowdhury T, Gomez T, Naluai-Cecchini T, Beach T, Goodman T, Gao T, Dolbeare T, Fliss T, Reddy T, Chen T, Hyde T, Brunetti T, Lemon T, Desta T, Borrman T, Haroutunian V, Spitsyna V, Swarup V, Shi X, Jiang Y, Xia Y, Chen Y, Jiang Y, Wang Y, Chae Y, Yang Y, Kim Y, Riley Z, Krsnik Z, Deng Z, Weng Z, Lin Z, Li Z. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362 PMID: 30545854, PMCID: PMC6413317, DOI: 10.1126/science.aat7615.Peer-Reviewed Original ResearchConceptsIntegrative functional genomic analysisFunctional genomic analysisCell typesGene coexpression modulesDistinct cell typesCell type-specific dynamicsGenomic basisEpigenomic reorganizationEpigenomic landscapeEpigenomic regulationGenomic analysisCoexpression modulesIntegrative analysisHuman brain developmentFetal transitionHuman neurodevelopmentGenetic associationCellular compositionNeuropsychiatric riskBrain developmentNeurodevelopmental processesGenesTraitsPostnatal developmentNeuropsychiatric disordersRevealing the brain's molecular architecture
Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Revealing the brain's molecular architecture. Science 2018, 362: 1262-1263. PMID: 30545881, DOI: 10.1126/science.362.6420.1262.Peer-Reviewed Original ResearchDe novo genome and transcriptome analyses provide insights into the biology of the trematode human parasite Fasciolopsis buski
Biswal DK, Roychowdhury T, Pandey P, Tandon V. De novo genome and transcriptome analyses provide insights into the biology of the trematode human parasite Fasciolopsis buski. PLOS ONE 2018, 13: e0205570. PMID: 30325945, PMCID: PMC6191129, DOI: 10.1371/journal.pone.0205570.Peer-Reviewed Original ResearchConceptsShort interspersed elementsTranscriptome dataTranscriptome analysisDe novo genomeDraft genome assemblyGiant intestinal flukeNext-generation sequencing technologiesGeneration sequencing technologyNovo genomeRNAi pathwayGenome assemblyTransposable elementsEcological diversityImportant biological characteristicsInterspersed elementsGenome analysisProtein kinaseOverall transcriptomeSequencing technologiesTrematode parasitesFasciolopsis buskiEnergy metabolismIntestinal flukesSuitable diagnostic systemOrganisms
2017
Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis
Bae T, Tomasini L, Mariani J, Zhou B, Roychowdhury T, Franjic D, Pletikos M, Pattni R, Chen BJ, Venturini E, Riley-Gillis B, Sestan N, Urban AE, Abyzov A, Vaccarino FM. Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Science 2017, 359: 550-555. PMID: 29217587, PMCID: PMC6311130, DOI: 10.1126/science.aan8690.Peer-Reviewed Original ResearchConceptsSingle nucleotide variationsMutation rateCancer cell genomeClonal cell populationsCell genomeCell lineagesBackground mutagenesisHuman cellsMutational rateSomatic mosaicismSingle cellsOxidative damageGenomeMutagenesisCell populationsMutation spectrumNeurogenesisCellsHuman fetusesIndividual neuronsLineagesPregastrulationHuman brainBrainMutations
2013
Next-Generation Anchor Based Phylogeny (NexABP): Constructing phylogeny from Next-generation sequencing data
Roychowdhury T, Vishnoi A, Bhattacharya A. Next-Generation Anchor Based Phylogeny (NexABP): Constructing phylogeny from Next-generation sequencing data. Scientific Reports 2013, 3: 2634. PMID: 24022334, PMCID: PMC3769656, DOI: 10.1038/srep02634.Peer-Reviewed Original ResearchConceptsPhylogenetic analysisNext-generation sequencing datasetsWhole genome sequencesNGS dataNext-generation sequencing dataEvolutionary relationshipsStrains/isolatesPhylogenetic constructionDistinct lineagesReference genomeGenome sequenceSequencing datasetsSequencing dataEscherichia coliPhylogenyDifferent strainsVibrio choleraBootstrap analysisGenomeLineagesOrganismsColiInner branchesUnprecedented scalePathogensGenetic heterogeneity revealed by sequence analysis of Mycobacterium tuberculosis isolates from extra-pulmonary tuberculosis patients
Das S, Roychowdhury T, Kumar P, Kumar A, Kalra P, Singh J, Singh S, Prasad H, Bhattacharya A. Genetic heterogeneity revealed by sequence analysis of Mycobacterium tuberculosis isolates from extra-pulmonary tuberculosis patients. BMC Genomics 2013, 14: 404. PMID: 23773324, PMCID: PMC3699378, DOI: 10.1186/1471-2164-14-404.Peer-Reviewed Original ResearchConceptsM. tuberculosisClinical isolatesDrug resistant tubercle bacilliExtra-pulmonary tuberculosis patientsMajor public health problemExtra-pulmonary patientsNon-pulmonary sitesResistant tubercle bacilliExtra-pulmonary TBExtra-pulmonary tuberculosisPublic health problemSingle nucleotide variationsSpread of tuberculosisTotal clinical casesNext-generation sequencing approachHIV patientsImmunocompetent patientsTuberculosis patientsClinical manifestationsPulmonary patientsM. tuberculosis H37RvPatient isolatesCerebrospinal fluidHealth problemsPatients